import numpy as np
import pandas as pd
from matplotlib.pyplot import subplots
from statsmodels.api import OLS
import sklearn.model_selection as skm
import sklearn.linear_model as skl
from sklearn.preprocessing import StandardScaler
from ISLP import load_data
from ISLP.models import ModelSpec as MS
from functools import partial1. imports
from sklearn.pipeline import Pipeline
from sklearn.decomposition import PCA
from sklearn.cross_decomposition import PLSRegression
from ISLP.models import (Stepwise, sklearn_selected, sklearn_selection_path)
from l0bnb import fit_pathHitters = load_data('Hitters')
np.isnan(Hitters['Salary']).sum()
Hitters = Hitters.dropna(); Hitters.shape(263, 20)2. 단계적 선택법
- Cp계산법 (변형이 있음) 및 함수 설정
def nCp(sigma2, estimator, X, Y):
n, p = X.shape
Yhat = estimator.predict(X)
RSS = np.sum((Y - Yhat)**2)
return -(RSS + 2 * p * sigma2) / n
design = MS(Hitters.columns.drop('Salary')).fit(Hitters)
Y = np.array(Hitters['Salary'])
X = design.transform(Hitters)
sigma2 = OLS(Y,X).fit().scale
neg_Cp = partial(nCp, sigma2)
print(neg_Cp)functools.partial(<function nCp at 0x7f48c1aacb80>, 99591.35617968219)- 단계적 선택 방법을 적용(사용하는 측도는 일반적으로 P-value)
strategy = Stepwise.first_peak(design, direction='forward', max_terms=len(design.terms))
hitters_MSE = sklearn_selected(OLS, strategy)
hitters_MSE.fit(Hitters, Y)
hitters_MSE.selected_state_('Assists',
'AtBat',
'CAtBat',
'CHits',
'CHmRun',
'CRBI',
'CRuns',
'CWalks',
'Division',
'Errors',
'Hits',
'HmRun',
'League',
'NewLeague',
'PutOuts',
'RBI',
'Runs',
'Walks',
'Years')- Cp를 이용한 단계적 선택
hitters_Cp = sklearn_selected(OLS, strategy, scoring=neg_Cp)
hitters_Cp.fit(Hitters, Y)
hitters_Cp.selected_state_('Assists',
'AtBat',
'CAtBat',
'CRBI',
'CRuns',
'CWalks',
'Division',
'Hits',
'PutOuts',
'Walks')- 단계적 선택 과정에서 모든 예측치를 행렬로 모으기
strategy = Stepwise.fixed_steps(design, len(design.terms), direction='forward')
full_path = sklearn_selection_path(OLS, strategy)
full_path.fit(Hitters, Y)
Yhat_in = full_path.predict(Hitters)
Yhat_in.shape(263, 20)- 위에서 저장한 예측치(룬련 데이터)로 예측오차를 계산해서 그리기
mse_fig, ax = subplots(figsize=(8,8))
insample_mse = ((Yhat_in - Y[:,None])**2).mean(0)
n_steps = insample_mse.shape[0]
ax.plot(np.arange(n_steps), insample_mse,
'k', # color black
label='In-sample')
ax.set_ylabel('MSE', fontsize=20)
ax.set_xlabel('# steps of forward stepwise', fontsize=20)
ax.set_xticks(np.arange(n_steps)[::2])
ax.legend()
ax.set_ylim([50000,250000]);
3. 교차검증의 활용
- 10 folder CV를 통해서 예측된 값들을 모아놓음(훈련과 검증을 구분했음!!)
K=10
kfold = skm.KFold(K, random_state=0, shuffle=True)
Yhat_cv = skm.cross_val_predict(full_path, Hitters, Y, cv=kfold)
print(Yhat_cv.shape)(263, 20)- 위의 결과에 대한 훈련오차와 교차검증 오차
cv_mse = []
for train_idx, test_idx in kfold.split(Y):
errors = (Yhat_cv[test_idx] - Y[test_idx ,None])**2
cv_mse.append(errors.mean(0)) # column means
cv_mse = np.array(cv_mse).T
cv_mse.shape
ax.errorbar(np.arange(n_steps),
cv_mse.mean(1),
cv_mse.std(1) / np.sqrt(K), # 교차검증오차의 표준오차
label='Cross -validated', c='r')
ax.set_ylim ([50000 ,250000])
ax.legend()
mse_fig
- 훈련 예측 오차와 Validation set 을 이용한 평가 예측 오차
validation = skm.ShuffleSplit(n_splits=1, test_size=0.2,
random_state=0)
for train_idx, test_idx in validation.split(Y):
full_path.fit(Hitters.iloc[train_idx], Y[train_idx])
Yhat_val = full_path.predict(Hitters.iloc[test_idx])
errors = (Yhat_val - Y[test_idx ,None])**2
validation_mse = errors.mean(0)
ax.plot(np.arange(n_steps), validation_mse ,
'b--', # color blue, broken line
label='Validation')
ax.set_xticks(np.arange(n_steps)[::2])
ax.set_ylim ([50000 ,250000])
ax.legend()
mse_fig
4. 축소 알고리즘
- Ridge 회귀분석
# 2. 표준편차가 0인 열 탐지
X_std = X_np.std(axis=0)
valid_cols = X_std != 0 # 분산 0인 열 제외
# 3. 유효한 열만 사용
X_np = X_np[:, valid_cols]
X_mean = X_np.mean(axis=0, keepdims=True)
X_std = X_np.std(axis=0, keepdims=True)
# 4. 표준화
Xs = (X_np - X_mean) / X_std
# 5. Y도 numpy 배열로 표준화 없이 그대로 사용
lambdas = 10**np.linspace(8, -2, 100) / Y_np.std()
# 6. ElasticNet 경로 추정
soln_array = skl.ElasticNet.path(Xs, Y_np, l1_ratio=0.0, alphas=lambdas)[1]/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 64428165.36474803, tolerance: 12885.7065737425
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 64428069.135193564, tolerance: 12885.7065737425
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 64427947.709570706, tolerance: 12885.7065737425
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 64427794.49147929, tolerance: 12885.7065737425
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 64427601.15801401, tolerance: 12885.7065737425
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 64427357.208145335, tolerance: 12885.7065737425
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 64427049.39312406, tolerance: 12885.7065737425
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 64426660.99818401, tolerance: 12885.7065737425
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 64426170.936871, tolerance: 12885.7065737425
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 64425552.60935727, tolerance: 12885.7065737425
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 64424772.46361481, tolerance: 12885.7065737425
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 64423788.18271286, tolerance: 12885.7065737425
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 64422546.402046196, tolerance: 12885.7065737425
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 64420979.836119056, tolerance: 12885.7065737425
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 64419003.66458898, tolerance: 12885.7065737425
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 64416510.99045885, tolerance: 12885.7065737425
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 64413367.138336174, tolerance: 12885.7065737425
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 64409402.50628651, tolerance: 12885.7065737425
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 64404403.61988451, tolerance: 12885.7065737425
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 64398101.96098537, tolerance: 12885.7065737425
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 64390160.05690916, tolerance: 12885.7065737425
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 64380154.22050254, tolerance: 12885.7065737425
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 64367553.23368757, tolerance: 12885.7065737425
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 64351692.17811265, tolerance: 12885.7065737425
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 64331740.55708714, tolerance: 12885.7065737425
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 64306663.85815487, tolerance: 12885.7065737425
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 64275177.83204634, tolerance: 12885.7065737425
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 64235695.09903011, tolerance: 12885.7065737425
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 64186264.367964305, tolerance: 12885.7065737425
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 64124503.75014188, tolerance: 12885.7065737425
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 64047531.61120445, tolerance: 12885.7065737425
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 63951901.41718618, tolerance: 12885.7065737425
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 63833551.374737374, tolerance: 12885.7065737425
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 63687785.48493876, tolerance: 12885.7065737425
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 63509309.6856596, tolerance: 12885.7065737425
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 63292354.021598354, tolerance: 12885.7065737425
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 63030916.89990266, tolerance: 12885.7065737425
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 62719166.29703928, tolerance: 12885.7065737425
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 62352019.354438685, tolerance: 12885.7065737425
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 61925889.875772476, tolerance: 12885.7065737425
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 61439539.89859062, tolerance: 12885.7065737425
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 60894903.039219804, tolerance: 12885.7065737425
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 60297684.607476555, tolerance: 12885.7065737425
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 59657521.16598571, tolerance: 12885.7065737425
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 58987535.05051082, tolerance: 12885.7065737425
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 58303257.308936626, tolerance: 12885.7065737425
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 57621079.35589412, tolerance: 12885.7065737425
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 56956552.362989165, tolerance: 12885.7065737425
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 56322906.14367991, tolerance: 12885.7065737425
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 55730077.752803415, tolerance: 12885.7065737425
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 55184365.56435658, tolerance: 12885.7065737425
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 54688640.343648925, tolerance: 12885.7065737425
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 54242923.97107167, tolerance: 12885.7065737425
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 53845116.922758974, tolerance: 12885.7065737425
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 53491699.68250863, tolerance: 12885.7065737425
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 53178310.76477921, tolerance: 12885.7065737425
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 52900177.0923312, tolerance: 12885.7065737425
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 52652419.27709019, tolerance: 12885.7065737425
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 52430270.988470204, tolerance: 12885.7065737425
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 52229246.49376926, tolerance: 12885.7065737425
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 52045276.2512958, tolerance: 12885.7065737425
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 51874817.10761591, tolerance: 12885.7065737425
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 51714935.480955824, tolerance: 12885.7065737425
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 51563358.53546281, tolerance: 12885.7065737425
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 51418487.867063135, tolerance: 12885.7065737425
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 51279371.62042453, tolerance: 12885.7065737425
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 51145634.326098055, tolerance: 12885.7065737425
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 51017369.00299057, tolerance: 12885.7065737425
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 50895002.06601904, tolerance: 12885.7065737425
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 50779146.50047492, tolerance: 12885.7065737425
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 50670461.07683636, tolerance: 12885.7065737425
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 50569532.27326829, tolerance: 12885.7065737425
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 50476790.98101043, tolerance: 12885.7065737425
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 50392468.80539256, tolerance: 12885.7065737425
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 50316590.69087277, tolerance: 12885.7065737425
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 50248994.152135305, tolerance: 12885.7065737425
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 50189362.60450366, tolerance: 12885.7065737425
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 50137261.69126279, tolerance: 12885.7065737425
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 50092171.83247444, tolerance: 12885.7065737425
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 50053515.08162342, tolerance: 12885.7065737425
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 50020677.61213049, tolerance: 12885.7065737425
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 49993029.95018303, tolerance: 12885.7065737425
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 49969946.081426926, tolerance: 12885.7065737425
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 49950821.12032695, tolerance: 12885.7065737425
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 49935086.37579553, tolerance: 12885.7065737425
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 49922220.655421846, tolerance: 12885.7065737425
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 49911757.23721755, tolerance: 12885.7065737425
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 49903286.659218386, tolerance: 12885.7065737425
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 49896456.01860968, tolerance: 12885.7065737425
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 49890965.72520983, tolerance: 12885.7065737425
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 49886564.660254605, tolerance: 12885.7065737425
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 49883044.548197165, tolerance: 12885.7065737425
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 49880234.147846185, tolerance: 12885.7065737425
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 49877993.670362845, tolerance: 12885.7065737425
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 49876209.66553561, tolerance: 12885.7065737425
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 49874790.493499346, tolerance: 12885.7065737425
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 49873662.41408362, tolerance: 12885.7065737425
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 49872766.27281989, tolerance: 12885.7065737425
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 49872054.73300089, tolerance: 12885.7065737425
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 49871489.989638224, tolerance: 12885.7065737425
model = cd_fast.enet_coordinate_descent_gram(print(soln_array.shape)
soln_array[:,20](19, 100)array([ 0.08356411, 0.09289174, 0.07258417, 0.08889321, 0.09514025,
0.09398712, 0.08476414, 0.11134727, 0.11618264, 0.11110206,
0.11909691, 0.12000505, 0.10365092, -0.00297587, -0.04083794,
0.06369407, 0.00539038, -0.0011531 , -0.00056188])- 벌점함수의 초매개변수에 의해 추정된 회귀계수의 변화 추적
soln_path = pd.DataFrame(soln_array.T, columns=X.columns[1:], index=-np.log(lambdas))
soln_path.index.name = 'negative log(lambda)'
soln_path| AtBat | Hits | HmRun | Runs | RBI | Walks | Years | CAtBat | CHits | CHmRun | CRuns | CRBI | CWalks | League[N] | Division[W] | PutOuts | Assists | Errors | NewLeague[N] | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| negative log(lambda) | |||||||||||||||||||
| -12.310855 | 0.000800 | 0.000889 | 0.000695 | 0.000851 | 0.000911 | 0.000900 | 0.000812 | 0.001067 | 0.001113 | 0.001064 | 0.001141 | 0.001149 | 0.000993 | -0.000029 | -0.000390 | 0.000609 | 0.000052 | -0.000011 | -0.000006 |
| -12.078271 | 0.001010 | 0.001122 | 0.000878 | 0.001074 | 0.001150 | 0.001135 | 0.001025 | 0.001346 | 0.001404 | 0.001343 | 0.001439 | 0.001450 | 0.001253 | -0.000037 | -0.000492 | 0.000769 | 0.000065 | -0.000014 | -0.000007 |
| -11.845686 | 0.001274 | 0.001416 | 0.001107 | 0.001355 | 0.001451 | 0.001433 | 0.001293 | 0.001698 | 0.001772 | 0.001694 | 0.001816 | 0.001830 | 0.001581 | -0.000046 | -0.000621 | 0.000970 | 0.000082 | -0.000017 | -0.000009 |
| -11.613102 | 0.001608 | 0.001787 | 0.001397 | 0.001710 | 0.001831 | 0.001808 | 0.001632 | 0.002143 | 0.002236 | 0.002138 | 0.002292 | 0.002309 | 0.001995 | -0.000058 | -0.000784 | 0.001224 | 0.000104 | -0.000022 | -0.000012 |
| -11.380518 | 0.002029 | 0.002255 | 0.001763 | 0.002158 | 0.002310 | 0.002281 | 0.002059 | 0.002704 | 0.002821 | 0.002698 | 0.002892 | 0.002914 | 0.002517 | -0.000073 | -0.000990 | 0.001544 | 0.000131 | -0.000028 | -0.000015 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 9.784658 | -290.823989 | 336.929968 | 37.322686 | -59.748520 | -26.507086 | 134.855915 | -17.216195 | -387.775826 | 89.573601 | -12.273926 | 476.079273 | 257.271255 | -213.124780 | 31.258215 | -58.457857 | 78.761266 | 53.622113 | -22.208456 | -12.402891 |
| 10.017243 | -290.879272 | 337.113713 | 37.431373 | -59.916820 | -26.606957 | 134.900549 | -17.108041 | -388.458404 | 89.000707 | -12.661459 | 477.031349 | 257.966790 | -213.280891 | 31.256434 | -58.448850 | 78.761240 | 53.645147 | -22.198802 | -12.391969 |
| 10.249827 | -290.923382 | 337.260446 | 37.518064 | -60.051166 | -26.686604 | 134.936136 | -17.022194 | -388.997470 | 88.537380 | -12.971603 | 477.791860 | 258.523025 | -213.405740 | 31.254958 | -58.441682 | 78.761230 | 53.663357 | -22.191071 | -12.383205 |
| 10.482412 | -290.958537 | 337.377455 | 37.587122 | -60.158256 | -26.750044 | 134.964477 | -16.954081 | -389.423414 | 88.164178 | -13.219329 | 478.398404 | 258.967059 | -213.505412 | 31.253747 | -58.435983 | 78.761230 | 53.677759 | -22.184893 | -12.376191 |
| 10.714996 | -290.986528 | 337.470648 | 37.642077 | -60.243522 | -26.800522 | 134.987027 | -16.900054 | -389.760135 | 87.864551 | -13.416889 | 478.881540 | 259.321007 | -213.584869 | 31.252760 | -58.431454 | 78.761235 | 53.689152 | -22.179964 | -12.370587 |
100 rows × 19 columns
path_fig , ax = subplots(figsize=(8,8))
soln_path.plot(ax=ax, legend=False)
ax.set_xlabel('$-\log(\lambda)$', fontsize=20)
ax.set_ylabel('Standardized coefficients', fontsize=20)
ax.legend(loc='upper left');
- 중간에 하나를 살펴봄
beta_hat = soln_path.loc[soln_path.index[50]]
lambdas[50], beta_hat, np.linalg.norm(beta_hat)(1.97711243041609,
AtBat 16.736510
Hits 27.154006
HmRun 11.071130
Runs 22.645937
RBI 21.214362
Walks 26.593380
Years 13.134470
CAtBat 23.909104
CHits 28.375692
CHmRun 26.148683
CRuns 29.012704
CRBI 29.499455
CWalks 19.180750
League[N] 5.677305
Division[W] -23.833230
PutOuts 29.312404
Assists 2.004304
Errors -3.653105
NewLeague[N] 3.865199
Name: -0.6816374117753777, dtype: float64,
92.81615742902518)- 설정된 초매개변수에 따라 추정된 회귀계수의 크기를 계산
ridge = skl.ElasticNet(alpha=lambdas[59], l1_ratio=0)
scaler = StandardScaler(with_mean=True , with_std=True)
pipe = Pipeline(steps=[('scaler', scaler), ('ridge', ridge)])
pipe.fit(X, Y)
np.linalg.norm(ridge.coef_)/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.446e+07, tolerance: 5.332e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(160.42371017725904- 훈련과 검증 데이터를 나누어 검증 오차 계산
validation = skm.ShuffleSplit(n_splits=1, test_size=0.5, random_state=0)
ridge.alpha = 1e1
results = skm.cross_validate(ridge, X, Y, scoring='neg_mean_squared_error', cv=validation)
print(-results['test_score'])[132790.68707108]/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.737e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(- 가장 좋은 초매개변수의 값을 validation set을 이용해서 찾음
param_grid = {'ridge__alpha': lambdas}
grid = skm.GridSearchCV(pipe ,
param_grid, cv=validation, scoring='neg_mean_squared_error')
grid.fit(X, Y)
grid.best_params_['ridge__alpha']
print(grid.best_estimator_)/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.136e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.136e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.136e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.136e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.136e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.136e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.136e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.136e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.136e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.136e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.136e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.136e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.136e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.136e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.136e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.136e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.135e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.135e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.135e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.135e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.135e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.134e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.134e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.133e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.132e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.131e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.130e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.128e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.127e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.124e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.121e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.117e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.113e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.107e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.100e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.091e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.081e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.069e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.055e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.038e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.019e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.977e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.744e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.494e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.234e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.968e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.704e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.448e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.204e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.977e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.769e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.581e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.412e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.261e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.127e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.008e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.900e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.803e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.714e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.632e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.554e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.480e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.409e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.342e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.276e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.214e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.154e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.097e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.043e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.991e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.943e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.898e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.856e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.817e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.780e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.746e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.715e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.687e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.661e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.637e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.616e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.596e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.579e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.563e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.550e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.538e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.528e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.519e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.512e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.506e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.500e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.496e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.493e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.490e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.488e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.486e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(Pipeline(steps=[('scaler', StandardScaler()),
('ridge', ElasticNet(alpha=0.005899006046740856, l1_ratio=0))])/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.485e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.483e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.483e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.482e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.248e+07, tolerance: 5.332e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(print(grid.best_estimator_)Pipeline(steps=[('scaler', StandardScaler()),
('ridge', ElasticNet(alpha=0.005899006046740856, l1_ratio=0))])- 교차 검증 오차를 이용한 선택 및 교차 검증 오차의 확인
grid = skm.GridSearchCV(pipe,
param_grid, cv=kfold, scoring='neg_mean_squared_error')
grid.fit(X, Y)
grid.best_params_['ridge__alpha']/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.336e+07, tolerance: 4.672e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.202e+07, tolerance: 4.404e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.397e+07, tolerance: 4.794e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.372e+07, tolerance: 4.744e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.399e+07, tolerance: 4.799e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.506e+07, tolerance: 5.012e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.424e+07, tolerance: 4.848e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.468e+07, tolerance: 4.935e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.532e+07, tolerance: 5.064e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.350e+07, tolerance: 4.699e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.336e+07, tolerance: 4.672e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.202e+07, tolerance: 4.404e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.397e+07, tolerance: 4.794e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.372e+07, tolerance: 4.744e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.399e+07, tolerance: 4.799e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.506e+07, tolerance: 5.012e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.424e+07, tolerance: 4.848e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.468e+07, tolerance: 4.935e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.532e+07, tolerance: 5.064e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.350e+07, tolerance: 4.699e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.336e+07, tolerance: 4.672e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.202e+07, tolerance: 4.404e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.397e+07, tolerance: 4.794e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.372e+07, tolerance: 4.744e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.399e+07, tolerance: 4.799e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.506e+07, tolerance: 5.012e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.424e+07, tolerance: 4.848e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.468e+07, tolerance: 4.935e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.532e+07, tolerance: 5.064e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.350e+07, tolerance: 4.699e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.336e+07, tolerance: 4.672e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.202e+07, tolerance: 4.404e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.397e+07, tolerance: 4.794e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.372e+07, tolerance: 4.744e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.399e+07, tolerance: 4.799e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.506e+07, tolerance: 5.012e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.424e+07, tolerance: 4.848e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.468e+07, tolerance: 4.935e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.532e+07, tolerance: 5.064e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.350e+07, tolerance: 4.699e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.336e+07, tolerance: 4.672e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.202e+07, tolerance: 4.404e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.397e+07, tolerance: 4.794e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.372e+07, tolerance: 4.744e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.399e+07, tolerance: 4.799e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.506e+07, tolerance: 5.012e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.424e+07, tolerance: 4.848e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.468e+07, tolerance: 4.935e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.532e+07, tolerance: 5.064e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.350e+07, tolerance: 4.699e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.336e+07, tolerance: 4.672e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.202e+07, tolerance: 4.404e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.397e+07, tolerance: 4.794e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.372e+07, tolerance: 4.744e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.399e+07, tolerance: 4.799e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.506e+07, tolerance: 5.012e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.424e+07, tolerance: 4.848e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.468e+07, tolerance: 4.935e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.532e+07, tolerance: 5.064e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.350e+07, tolerance: 4.699e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.336e+07, tolerance: 4.672e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.202e+07, tolerance: 4.404e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.397e+07, tolerance: 4.794e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.372e+07, tolerance: 4.744e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.399e+07, tolerance: 4.799e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.506e+07, tolerance: 5.012e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.424e+07, tolerance: 4.848e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.468e+07, tolerance: 4.935e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.532e+07, tolerance: 5.064e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.350e+07, tolerance: 4.699e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.336e+07, tolerance: 4.672e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.202e+07, tolerance: 4.404e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.397e+07, tolerance: 4.794e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.372e+07, tolerance: 4.744e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.399e+07, tolerance: 4.799e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.506e+07, tolerance: 5.012e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.424e+07, tolerance: 4.848e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.468e+07, tolerance: 4.935e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.532e+07, tolerance: 5.064e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.350e+07, tolerance: 4.699e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.336e+07, tolerance: 4.672e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.202e+07, tolerance: 4.404e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.397e+07, tolerance: 4.794e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.372e+07, tolerance: 4.744e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.399e+07, tolerance: 4.799e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.506e+07, tolerance: 5.012e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.424e+07, tolerance: 4.848e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.467e+07, tolerance: 4.935e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.532e+07, tolerance: 5.064e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.349e+07, tolerance: 4.699e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.336e+07, tolerance: 4.672e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.202e+07, tolerance: 4.404e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.397e+07, tolerance: 4.794e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.372e+07, tolerance: 4.744e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.399e+07, tolerance: 4.799e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.506e+07, tolerance: 5.012e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.424e+07, tolerance: 4.848e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.467e+07, tolerance: 4.935e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.532e+07, tolerance: 5.064e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.349e+07, tolerance: 4.699e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.336e+07, tolerance: 4.672e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.202e+07, tolerance: 4.404e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.397e+07, tolerance: 4.794e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.372e+07, tolerance: 4.744e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.399e+07, tolerance: 4.799e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.506e+07, tolerance: 5.012e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.424e+07, tolerance: 4.848e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.467e+07, tolerance: 4.935e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.532e+07, tolerance: 5.064e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.349e+07, tolerance: 4.699e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.336e+07, tolerance: 4.672e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.202e+07, tolerance: 4.404e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.397e+07, tolerance: 4.794e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.371e+07, tolerance: 4.744e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.399e+07, tolerance: 4.799e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.506e+07, tolerance: 5.012e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.424e+07, tolerance: 4.848e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.467e+07, tolerance: 4.935e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.531e+07, tolerance: 5.064e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.349e+07, tolerance: 4.699e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.336e+07, tolerance: 4.672e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.202e+07, tolerance: 4.404e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.396e+07, tolerance: 4.794e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.371e+07, tolerance: 4.744e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.399e+07, tolerance: 4.799e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.506e+07, tolerance: 5.012e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.424e+07, tolerance: 4.848e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.467e+07, tolerance: 4.935e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.531e+07, tolerance: 5.064e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.349e+07, tolerance: 4.699e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.335e+07, tolerance: 4.672e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.202e+07, tolerance: 4.404e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.396e+07, tolerance: 4.794e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.371e+07, tolerance: 4.744e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.399e+07, tolerance: 4.799e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.505e+07, tolerance: 5.012e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.424e+07, tolerance: 4.848e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.467e+07, tolerance: 4.935e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.531e+07, tolerance: 5.064e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.349e+07, tolerance: 4.699e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.335e+07, tolerance: 4.672e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.201e+07, tolerance: 4.404e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.396e+07, tolerance: 4.794e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.371e+07, tolerance: 4.744e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.398e+07, tolerance: 4.799e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.505e+07, tolerance: 5.012e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.423e+07, tolerance: 4.848e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.467e+07, tolerance: 4.935e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.531e+07, tolerance: 5.064e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.349e+07, tolerance: 4.699e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.335e+07, tolerance: 4.672e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.201e+07, tolerance: 4.404e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.396e+07, tolerance: 4.794e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.371e+07, tolerance: 4.744e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.398e+07, tolerance: 4.799e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.505e+07, tolerance: 5.012e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.423e+07, tolerance: 4.848e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.467e+07, tolerance: 4.935e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.531e+07, tolerance: 5.064e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.349e+07, tolerance: 4.699e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.335e+07, tolerance: 4.672e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.201e+07, tolerance: 4.404e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.396e+07, tolerance: 4.794e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.370e+07, tolerance: 4.744e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.398e+07, tolerance: 4.799e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.505e+07, tolerance: 5.012e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.423e+07, tolerance: 4.848e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.466e+07, tolerance: 4.935e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.530e+07, tolerance: 5.064e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.348e+07, tolerance: 4.699e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.334e+07, tolerance: 4.672e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.201e+07, tolerance: 4.404e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.395e+07, tolerance: 4.794e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.370e+07, tolerance: 4.744e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.398e+07, tolerance: 4.799e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.504e+07, tolerance: 5.012e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.422e+07, tolerance: 4.848e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.466e+07, tolerance: 4.935e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.530e+07, tolerance: 5.064e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.348e+07, tolerance: 4.699e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.334e+07, tolerance: 4.672e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.200e+07, tolerance: 4.404e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.395e+07, tolerance: 4.794e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.370e+07, tolerance: 4.744e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.397e+07, tolerance: 4.799e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.504e+07, tolerance: 5.012e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.422e+07, tolerance: 4.848e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.465e+07, tolerance: 4.935e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.530e+07, tolerance: 5.064e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.347e+07, tolerance: 4.699e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.334e+07, tolerance: 4.672e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.200e+07, tolerance: 4.404e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.394e+07, tolerance: 4.794e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.369e+07, tolerance: 4.744e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.397e+07, tolerance: 4.799e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.503e+07, tolerance: 5.012e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.421e+07, tolerance: 4.848e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.465e+07, tolerance: 4.935e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.529e+07, tolerance: 5.064e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.347e+07, tolerance: 4.699e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.333e+07, tolerance: 4.672e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.199e+07, tolerance: 4.404e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.394e+07, tolerance: 4.794e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.368e+07, tolerance: 4.744e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.396e+07, tolerance: 4.799e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.503e+07, tolerance: 5.012e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.421e+07, tolerance: 4.848e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.464e+07, tolerance: 4.935e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.528e+07, tolerance: 5.064e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.346e+07, tolerance: 4.699e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.332e+07, tolerance: 4.672e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.198e+07, tolerance: 4.404e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.393e+07, tolerance: 4.794e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.367e+07, tolerance: 4.744e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.395e+07, tolerance: 4.799e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.502e+07, tolerance: 5.012e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.420e+07, tolerance: 4.848e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.463e+07, tolerance: 4.935e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.527e+07, tolerance: 5.064e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.345e+07, tolerance: 4.699e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.331e+07, tolerance: 4.672e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.197e+07, tolerance: 4.404e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.392e+07, tolerance: 4.794e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.366e+07, tolerance: 4.744e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.394e+07, tolerance: 4.799e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.500e+07, tolerance: 5.012e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.419e+07, tolerance: 4.848e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.462e+07, tolerance: 4.935e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.526e+07, tolerance: 5.064e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.344e+07, tolerance: 4.699e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.330e+07, tolerance: 4.672e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.196e+07, tolerance: 4.404e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.390e+07, tolerance: 4.794e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.365e+07, tolerance: 4.744e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.392e+07, tolerance: 4.799e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.499e+07, tolerance: 5.012e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.417e+07, tolerance: 4.848e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.461e+07, tolerance: 4.935e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.525e+07, tolerance: 5.064e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.342e+07, tolerance: 4.699e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.328e+07, tolerance: 4.672e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.194e+07, tolerance: 4.404e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.389e+07, tolerance: 4.794e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.363e+07, tolerance: 4.744e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.390e+07, tolerance: 4.799e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.497e+07, tolerance: 5.012e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.415e+07, tolerance: 4.848e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.459e+07, tolerance: 4.935e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.523e+07, tolerance: 5.064e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.340e+07, tolerance: 4.699e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.326e+07, tolerance: 4.672e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.192e+07, tolerance: 4.404e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.386e+07, tolerance: 4.794e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.361e+07, tolerance: 4.744e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.388e+07, tolerance: 4.799e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.495e+07, tolerance: 5.012e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.413e+07, tolerance: 4.848e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.457e+07, tolerance: 4.935e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.520e+07, tolerance: 5.064e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.338e+07, tolerance: 4.699e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.323e+07, tolerance: 4.672e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.190e+07, tolerance: 4.404e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.384e+07, tolerance: 4.794e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.358e+07, tolerance: 4.744e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.385e+07, tolerance: 4.799e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.492e+07, tolerance: 5.012e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.410e+07, tolerance: 4.848e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.454e+07, tolerance: 4.935e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.517e+07, tolerance: 5.064e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.335e+07, tolerance: 4.699e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.320e+07, tolerance: 4.672e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.187e+07, tolerance: 4.404e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.380e+07, tolerance: 4.794e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.354e+07, tolerance: 4.744e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.382e+07, tolerance: 4.799e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.488e+07, tolerance: 5.012e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.407e+07, tolerance: 4.848e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.450e+07, tolerance: 4.935e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.514e+07, tolerance: 5.064e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.331e+07, tolerance: 4.699e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.316e+07, tolerance: 4.672e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.183e+07, tolerance: 4.404e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.376e+07, tolerance: 4.794e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.350e+07, tolerance: 4.744e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.377e+07, tolerance: 4.799e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.484e+07, tolerance: 5.012e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.402e+07, tolerance: 4.848e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.445e+07, tolerance: 4.935e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.509e+07, tolerance: 5.064e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.326e+07, tolerance: 4.699e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.311e+07, tolerance: 4.672e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.178e+07, tolerance: 4.404e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.371e+07, tolerance: 4.794e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.344e+07, tolerance: 4.744e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.371e+07, tolerance: 4.799e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.478e+07, tolerance: 5.012e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.397e+07, tolerance: 4.848e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.440e+07, tolerance: 4.935e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.503e+07, tolerance: 5.064e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.320e+07, tolerance: 4.699e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.304e+07, tolerance: 4.672e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.172e+07, tolerance: 4.404e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.364e+07, tolerance: 4.794e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.337e+07, tolerance: 4.744e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.364e+07, tolerance: 4.799e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.471e+07, tolerance: 5.012e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.390e+07, tolerance: 4.848e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.433e+07, tolerance: 4.935e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.496e+07, tolerance: 5.064e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.312e+07, tolerance: 4.699e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.296e+07, tolerance: 4.672e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.164e+07, tolerance: 4.404e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.356e+07, tolerance: 4.794e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.328e+07, tolerance: 4.744e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.356e+07, tolerance: 4.799e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.462e+07, tolerance: 5.012e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.381e+07, tolerance: 4.848e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.424e+07, tolerance: 4.935e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.487e+07, tolerance: 5.064e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.303e+07, tolerance: 4.699e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.287e+07, tolerance: 4.672e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.154e+07, tolerance: 4.404e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.346e+07, tolerance: 4.794e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.317e+07, tolerance: 4.744e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.345e+07, tolerance: 4.799e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.451e+07, tolerance: 5.012e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.370e+07, tolerance: 4.848e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.413e+07, tolerance: 4.935e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.476e+07, tolerance: 5.064e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.291e+07, tolerance: 4.699e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.274e+07, tolerance: 4.672e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.143e+07, tolerance: 4.404e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.333e+07, tolerance: 4.794e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.304e+07, tolerance: 4.744e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.331e+07, tolerance: 4.799e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.438e+07, tolerance: 5.012e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.357e+07, tolerance: 4.848e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.400e+07, tolerance: 4.935e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.462e+07, tolerance: 5.064e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.276e+07, tolerance: 4.699e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.260e+07, tolerance: 4.672e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.128e+07, tolerance: 4.404e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.318e+07, tolerance: 4.794e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.287e+07, tolerance: 4.744e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.315e+07, tolerance: 4.799e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.422e+07, tolerance: 5.012e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.341e+07, tolerance: 4.848e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.383e+07, tolerance: 4.935e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.445e+07, tolerance: 5.064e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.259e+07, tolerance: 4.699e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.241e+07, tolerance: 4.672e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.111e+07, tolerance: 4.404e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.299e+07, tolerance: 4.794e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.268e+07, tolerance: 4.744e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.295e+07, tolerance: 4.799e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.402e+07, tolerance: 5.012e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.321e+07, tolerance: 4.848e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.363e+07, tolerance: 4.935e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.425e+07, tolerance: 5.064e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.237e+07, tolerance: 4.699e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.220e+07, tolerance: 4.672e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.090e+07, tolerance: 4.404e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.277e+07, tolerance: 4.794e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.244e+07, tolerance: 4.744e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.271e+07, tolerance: 4.799e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.378e+07, tolerance: 5.012e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.297e+07, tolerance: 4.848e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.339e+07, tolerance: 4.935e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.400e+07, tolerance: 5.064e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.211e+07, tolerance: 4.699e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.194e+07, tolerance: 4.672e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.065e+07, tolerance: 4.404e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.250e+07, tolerance: 4.794e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.215e+07, tolerance: 4.744e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.242e+07, tolerance: 4.799e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.349e+07, tolerance: 5.012e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.269e+07, tolerance: 4.848e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.311e+07, tolerance: 4.935e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.371e+07, tolerance: 5.064e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.181e+07, tolerance: 4.699e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.163e+07, tolerance: 4.672e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.035e+07, tolerance: 4.404e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.218e+07, tolerance: 4.794e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.182e+07, tolerance: 4.744e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.208e+07, tolerance: 4.799e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.315e+07, tolerance: 5.012e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.235e+07, tolerance: 4.848e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.277e+07, tolerance: 4.935e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.336e+07, tolerance: 5.064e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.144e+07, tolerance: 4.699e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.127e+07, tolerance: 4.672e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.000e+07, tolerance: 4.404e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.182e+07, tolerance: 4.794e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.143e+07, tolerance: 4.744e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.169e+07, tolerance: 4.799e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.276e+07, tolerance: 5.012e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.196e+07, tolerance: 4.848e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.238e+07, tolerance: 4.935e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.296e+07, tolerance: 5.064e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.102e+07, tolerance: 4.699e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.087e+07, tolerance: 4.672e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.961e+07, tolerance: 4.404e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.140e+07, tolerance: 4.794e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.099e+07, tolerance: 4.744e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.124e+07, tolerance: 4.799e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.231e+07, tolerance: 5.012e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.152e+07, tolerance: 4.848e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.193e+07, tolerance: 4.935e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.250e+07, tolerance: 5.064e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.054e+07, tolerance: 4.699e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.041e+07, tolerance: 4.672e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.917e+07, tolerance: 4.404e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.094e+07, tolerance: 4.794e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.049e+07, tolerance: 4.744e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.074e+07, tolerance: 4.799e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.181e+07, tolerance: 5.012e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.102e+07, tolerance: 4.848e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.143e+07, tolerance: 4.935e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.199e+07, tolerance: 5.064e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.000e+07, tolerance: 4.699e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.991e+07, tolerance: 4.672e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.868e+07, tolerance: 4.404e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.043e+07, tolerance: 4.794e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.995e+07, tolerance: 4.744e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.019e+07, tolerance: 4.799e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.127e+07, tolerance: 5.012e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.048e+07, tolerance: 4.848e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.088e+07, tolerance: 4.935e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.143e+07, tolerance: 5.064e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.941e+07, tolerance: 4.699e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.937e+07, tolerance: 4.672e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.816e+07, tolerance: 4.404e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.988e+07, tolerance: 4.794e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.937e+07, tolerance: 4.744e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.959e+07, tolerance: 4.799e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.068e+07, tolerance: 5.012e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.989e+07, tolerance: 4.848e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.029e+07, tolerance: 4.935e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.082e+07, tolerance: 5.064e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.878e+07, tolerance: 4.699e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.880e+07, tolerance: 4.672e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.761e+07, tolerance: 4.404e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.931e+07, tolerance: 4.794e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.877e+07, tolerance: 4.744e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.897e+07, tolerance: 4.799e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.006e+07, tolerance: 5.012e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.928e+07, tolerance: 4.848e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.967e+07, tolerance: 4.935e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.019e+07, tolerance: 5.064e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.812e+07, tolerance: 4.699e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.822e+07, tolerance: 4.672e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.705e+07, tolerance: 4.404e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.873e+07, tolerance: 4.794e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.816e+07, tolerance: 4.744e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.834e+07, tolerance: 4.799e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.944e+07, tolerance: 5.012e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.865e+07, tolerance: 4.848e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.904e+07, tolerance: 4.935e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.954e+07, tolerance: 5.064e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.744e+07, tolerance: 4.699e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.765e+07, tolerance: 4.672e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.649e+07, tolerance: 4.404e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.815e+07, tolerance: 4.794e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.755e+07, tolerance: 4.744e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.771e+07, tolerance: 4.799e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.881e+07, tolerance: 5.012e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.802e+07, tolerance: 4.848e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.841e+07, tolerance: 4.935e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.889e+07, tolerance: 5.064e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.677e+07, tolerance: 4.699e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.708e+07, tolerance: 4.672e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.594e+07, tolerance: 4.404e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.758e+07, tolerance: 4.794e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.696e+07, tolerance: 4.744e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.709e+07, tolerance: 4.799e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.820e+07, tolerance: 5.012e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.741e+07, tolerance: 4.848e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.780e+07, tolerance: 4.935e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.826e+07, tolerance: 5.064e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.612e+07, tolerance: 4.699e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.654e+07, tolerance: 4.672e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.542e+07, tolerance: 4.404e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.704e+07, tolerance: 4.794e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.640e+07, tolerance: 4.744e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.650e+07, tolerance: 4.799e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.762e+07, tolerance: 5.012e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.682e+07, tolerance: 4.848e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.721e+07, tolerance: 4.935e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.766e+07, tolerance: 5.064e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.549e+07, tolerance: 4.699e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.604e+07, tolerance: 4.672e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.494e+07, tolerance: 4.404e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.654e+07, tolerance: 4.794e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.588e+07, tolerance: 4.744e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.595e+07, tolerance: 4.799e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.708e+07, tolerance: 5.012e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.627e+07, tolerance: 4.848e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.666e+07, tolerance: 4.935e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.709e+07, tolerance: 5.064e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.491e+07, tolerance: 4.699e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.557e+07, tolerance: 4.672e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.449e+07, tolerance: 4.404e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.607e+07, tolerance: 4.794e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.541e+07, tolerance: 4.744e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.544e+07, tolerance: 4.799e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.658e+07, tolerance: 5.012e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.577e+07, tolerance: 4.848e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.615e+07, tolerance: 4.935e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.656e+07, tolerance: 5.064e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.437e+07, tolerance: 4.699e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.515e+07, tolerance: 4.672e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.408e+07, tolerance: 4.404e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.564e+07, tolerance: 4.794e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.498e+07, tolerance: 4.744e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.498e+07, tolerance: 4.799e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.613e+07, tolerance: 5.012e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.531e+07, tolerance: 4.848e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.569e+07, tolerance: 4.935e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.608e+07, tolerance: 5.064e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.389e+07, tolerance: 4.699e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.477e+07, tolerance: 4.672e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.372e+07, tolerance: 4.404e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.525e+07, tolerance: 4.794e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.459e+07, tolerance: 4.744e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.456e+07, tolerance: 4.799e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.572e+07, tolerance: 5.012e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.489e+07, tolerance: 4.848e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.528e+07, tolerance: 4.935e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.565e+07, tolerance: 5.064e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.345e+07, tolerance: 4.699e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.442e+07, tolerance: 4.672e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.340e+07, tolerance: 4.404e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.491e+07, tolerance: 4.794e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.425e+07, tolerance: 4.744e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.419e+07, tolerance: 4.799e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.535e+07, tolerance: 5.012e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.452e+07, tolerance: 4.848e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.490e+07, tolerance: 4.935e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.526e+07, tolerance: 5.064e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.306e+07, tolerance: 4.699e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.412e+07, tolerance: 4.672e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.311e+07, tolerance: 4.404e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.460e+07, tolerance: 4.794e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.395e+07, tolerance: 4.744e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.386e+07, tolerance: 4.799e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.502e+07, tolerance: 5.012e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.419e+07, tolerance: 4.848e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.457e+07, tolerance: 4.935e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.490e+07, tolerance: 5.064e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.272e+07, tolerance: 4.699e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.385e+07, tolerance: 4.672e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.286e+07, tolerance: 4.404e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.431e+07, tolerance: 4.794e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.368e+07, tolerance: 4.744e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.356e+07, tolerance: 4.799e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.473e+07, tolerance: 5.012e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.389e+07, tolerance: 4.848e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.428e+07, tolerance: 4.935e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.459e+07, tolerance: 5.064e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.242e+07, tolerance: 4.699e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.360e+07, tolerance: 4.672e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.264e+07, tolerance: 4.404e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.406e+07, tolerance: 4.794e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.345e+07, tolerance: 4.744e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.330e+07, tolerance: 4.799e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.447e+07, tolerance: 5.012e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.363e+07, tolerance: 4.848e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.401e+07, tolerance: 4.935e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.431e+07, tolerance: 5.064e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.215e+07, tolerance: 4.699e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.339e+07, tolerance: 4.672e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.244e+07, tolerance: 4.404e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.383e+07, tolerance: 4.794e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.324e+07, tolerance: 4.744e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.306e+07, tolerance: 4.799e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.423e+07, tolerance: 5.012e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.339e+07, tolerance: 4.848e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.378e+07, tolerance: 4.935e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.406e+07, tolerance: 5.064e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.192e+07, tolerance: 4.699e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.319e+07, tolerance: 4.672e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.227e+07, tolerance: 4.404e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.363e+07, tolerance: 4.794e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.305e+07, tolerance: 4.744e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.285e+07, tolerance: 4.799e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.402e+07, tolerance: 5.012e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.318e+07, tolerance: 4.848e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.356e+07, tolerance: 4.935e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.383e+07, tolerance: 5.064e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.171e+07, tolerance: 4.699e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.301e+07, tolerance: 4.672e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.211e+07, tolerance: 4.404e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.343e+07, tolerance: 4.794e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.288e+07, tolerance: 4.744e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.266e+07, tolerance: 4.799e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.383e+07, tolerance: 5.012e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.299e+07, tolerance: 4.848e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.337e+07, tolerance: 4.935e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.362e+07, tolerance: 5.064e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.153e+07, tolerance: 4.699e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.285e+07, tolerance: 4.672e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.197e+07, tolerance: 4.404e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.325e+07, tolerance: 4.794e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.273e+07, tolerance: 4.744e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.249e+07, tolerance: 4.799e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.365e+07, tolerance: 5.012e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.281e+07, tolerance: 4.848e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.319e+07, tolerance: 4.935e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.343e+07, tolerance: 5.064e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.137e+07, tolerance: 4.699e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.270e+07, tolerance: 4.672e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.184e+07, tolerance: 4.404e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.309e+07, tolerance: 4.794e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.259e+07, tolerance: 4.744e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.232e+07, tolerance: 4.799e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.348e+07, tolerance: 5.012e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.265e+07, tolerance: 4.848e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.303e+07, tolerance: 4.935e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.326e+07, tolerance: 5.064e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.122e+07, tolerance: 4.699e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.255e+07, tolerance: 4.672e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.172e+07, tolerance: 4.404e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.293e+07, tolerance: 4.794e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.245e+07, tolerance: 4.744e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.217e+07, tolerance: 4.799e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.332e+07, tolerance: 5.012e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.250e+07, tolerance: 4.848e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.287e+07, tolerance: 4.935e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.309e+07, tolerance: 5.064e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.109e+07, tolerance: 4.699e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.242e+07, tolerance: 4.672e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.160e+07, tolerance: 4.404e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.277e+07, tolerance: 4.794e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.233e+07, tolerance: 4.744e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.203e+07, tolerance: 4.799e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.317e+07, tolerance: 5.012e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.236e+07, tolerance: 4.848e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.272e+07, tolerance: 4.935e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.294e+07, tolerance: 5.064e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.096e+07, tolerance: 4.699e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.229e+07, tolerance: 4.672e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.149e+07, tolerance: 4.404e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.262e+07, tolerance: 4.794e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.221e+07, tolerance: 4.744e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.190e+07, tolerance: 4.799e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.303e+07, tolerance: 5.012e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.222e+07, tolerance: 4.848e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.257e+07, tolerance: 4.935e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.279e+07, tolerance: 5.064e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.085e+07, tolerance: 4.699e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.216e+07, tolerance: 4.672e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.138e+07, tolerance: 4.404e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.248e+07, tolerance: 4.794e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.209e+07, tolerance: 4.744e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.177e+07, tolerance: 4.799e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.289e+07, tolerance: 5.012e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.209e+07, tolerance: 4.848e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.243e+07, tolerance: 4.935e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.265e+07, tolerance: 5.064e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.074e+07, tolerance: 4.699e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.204e+07, tolerance: 4.672e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.128e+07, tolerance: 4.404e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.234e+07, tolerance: 4.794e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.198e+07, tolerance: 4.744e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.165e+07, tolerance: 4.799e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.275e+07, tolerance: 5.012e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.196e+07, tolerance: 4.848e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.229e+07, tolerance: 4.935e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.251e+07, tolerance: 5.064e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.064e+07, tolerance: 4.699e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.192e+07, tolerance: 4.672e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.118e+07, tolerance: 4.404e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.220e+07, tolerance: 4.794e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.187e+07, tolerance: 4.744e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.153e+07, tolerance: 4.799e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.262e+07, tolerance: 5.012e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.185e+07, tolerance: 4.848e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.216e+07, tolerance: 4.935e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.238e+07, tolerance: 5.064e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.055e+07, tolerance: 4.699e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.180e+07, tolerance: 4.672e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.109e+07, tolerance: 4.404e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.208e+07, tolerance: 4.794e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.176e+07, tolerance: 4.744e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.142e+07, tolerance: 4.799e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.250e+07, tolerance: 5.012e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.173e+07, tolerance: 4.848e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.203e+07, tolerance: 4.935e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.226e+07, tolerance: 5.064e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.046e+07, tolerance: 4.699e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.169e+07, tolerance: 4.672e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.100e+07, tolerance: 4.404e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.195e+07, tolerance: 4.794e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.166e+07, tolerance: 4.744e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.132e+07, tolerance: 4.799e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.239e+07, tolerance: 5.012e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.163e+07, tolerance: 4.848e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.191e+07, tolerance: 4.935e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.214e+07, tolerance: 5.064e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.038e+07, tolerance: 4.699e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.159e+07, tolerance: 4.672e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.092e+07, tolerance: 4.404e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.184e+07, tolerance: 4.794e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.157e+07, tolerance: 4.744e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.123e+07, tolerance: 4.799e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.228e+07, tolerance: 5.012e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.153e+07, tolerance: 4.848e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.179e+07, tolerance: 4.935e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.203e+07, tolerance: 5.064e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.030e+07, tolerance: 4.699e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.149e+07, tolerance: 4.672e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.084e+07, tolerance: 4.404e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.173e+07, tolerance: 4.794e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.148e+07, tolerance: 4.744e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.114e+07, tolerance: 4.799e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.218e+07, tolerance: 5.012e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.144e+07, tolerance: 4.848e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.168e+07, tolerance: 4.935e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.193e+07, tolerance: 5.064e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.023e+07, tolerance: 4.699e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.140e+07, tolerance: 4.672e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.077e+07, tolerance: 4.404e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.163e+07, tolerance: 4.794e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.139e+07, tolerance: 4.744e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.106e+07, tolerance: 4.799e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.209e+07, tolerance: 5.012e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.136e+07, tolerance: 4.848e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.158e+07, tolerance: 4.935e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.184e+07, tolerance: 5.064e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.017e+07, tolerance: 4.699e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.132e+07, tolerance: 4.672e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.070e+07, tolerance: 4.404e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.154e+07, tolerance: 4.794e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.132e+07, tolerance: 4.744e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.099e+07, tolerance: 4.799e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.201e+07, tolerance: 5.012e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.128e+07, tolerance: 4.848e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.149e+07, tolerance: 4.935e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.175e+07, tolerance: 5.064e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.011e+07, tolerance: 4.699e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.125e+07, tolerance: 4.672e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.064e+07, tolerance: 4.404e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.146e+07, tolerance: 4.794e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.124e+07, tolerance: 4.744e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.093e+07, tolerance: 4.799e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.194e+07, tolerance: 5.012e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.122e+07, tolerance: 4.848e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.140e+07, tolerance: 4.935e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.168e+07, tolerance: 5.064e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.006e+07, tolerance: 4.699e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.118e+07, tolerance: 4.672e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.059e+07, tolerance: 4.404e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.139e+07, tolerance: 4.794e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.118e+07, tolerance: 4.744e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.088e+07, tolerance: 4.799e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.187e+07, tolerance: 5.012e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.116e+07, tolerance: 4.848e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.133e+07, tolerance: 4.935e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.161e+07, tolerance: 5.064e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.001e+07, tolerance: 4.699e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.112e+07, tolerance: 4.672e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.054e+07, tolerance: 4.404e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.132e+07, tolerance: 4.794e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.111e+07, tolerance: 4.744e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.083e+07, tolerance: 4.799e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.181e+07, tolerance: 5.012e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.111e+07, tolerance: 4.848e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.126e+07, tolerance: 4.935e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.155e+07, tolerance: 5.064e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.968e+06, tolerance: 4.699e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.107e+07, tolerance: 4.672e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.050e+07, tolerance: 4.404e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.126e+07, tolerance: 4.794e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.106e+07, tolerance: 4.744e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.079e+07, tolerance: 4.799e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.176e+07, tolerance: 5.012e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.107e+07, tolerance: 4.848e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.120e+07, tolerance: 4.935e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.150e+07, tolerance: 5.064e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.931e+06, tolerance: 4.699e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.103e+07, tolerance: 4.672e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.047e+07, tolerance: 4.404e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.121e+07, tolerance: 4.794e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.101e+07, tolerance: 4.744e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.075e+07, tolerance: 4.799e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.172e+07, tolerance: 5.012e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.104e+07, tolerance: 4.848e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.115e+07, tolerance: 4.935e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.145e+07, tolerance: 5.064e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.899e+06, tolerance: 4.699e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.100e+07, tolerance: 4.672e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.044e+07, tolerance: 4.404e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.117e+07, tolerance: 4.794e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.097e+07, tolerance: 4.744e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.072e+07, tolerance: 4.799e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.168e+07, tolerance: 5.012e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.101e+07, tolerance: 4.848e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.111e+07, tolerance: 4.935e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.141e+07, tolerance: 5.064e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.871e+06, tolerance: 4.699e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.097e+07, tolerance: 4.672e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.041e+07, tolerance: 4.404e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.113e+07, tolerance: 4.794e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.093e+07, tolerance: 4.744e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.069e+07, tolerance: 4.799e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.165e+07, tolerance: 5.012e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.098e+07, tolerance: 4.848e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.107e+07, tolerance: 4.935e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.137e+07, tolerance: 5.064e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.848e+06, tolerance: 4.699e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.094e+07, tolerance: 4.672e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.039e+07, tolerance: 4.404e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.110e+07, tolerance: 4.794e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.089e+07, tolerance: 4.744e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.067e+07, tolerance: 4.799e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.162e+07, tolerance: 5.012e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.096e+07, tolerance: 4.848e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.104e+07, tolerance: 4.935e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.135e+07, tolerance: 5.064e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.828e+06, tolerance: 4.699e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.092e+07, tolerance: 4.672e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.037e+07, tolerance: 4.404e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.107e+07, tolerance: 4.794e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.086e+07, tolerance: 4.744e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.065e+07, tolerance: 4.799e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.160e+07, tolerance: 5.012e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.094e+07, tolerance: 4.848e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.101e+07, tolerance: 4.935e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.132e+07, tolerance: 5.064e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.811e+06, tolerance: 4.699e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.091e+07, tolerance: 4.672e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.035e+07, tolerance: 4.404e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.105e+07, tolerance: 4.794e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.084e+07, tolerance: 4.744e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.064e+07, tolerance: 4.799e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.158e+07, tolerance: 5.012e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.093e+07, tolerance: 4.848e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.099e+07, tolerance: 4.935e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.130e+07, tolerance: 5.064e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.797e+06, tolerance: 4.699e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.089e+07, tolerance: 4.672e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.034e+07, tolerance: 4.404e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.103e+07, tolerance: 4.794e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.082e+07, tolerance: 4.744e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.063e+07, tolerance: 4.799e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.156e+07, tolerance: 5.012e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.092e+07, tolerance: 4.848e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.097e+07, tolerance: 4.935e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.128e+07, tolerance: 5.064e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.786e+06, tolerance: 4.699e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.088e+07, tolerance: 4.672e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.033e+07, tolerance: 4.404e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.102e+07, tolerance: 4.794e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.080e+07, tolerance: 4.744e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.061e+07, tolerance: 4.799e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.155e+07, tolerance: 5.012e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.091e+07, tolerance: 4.848e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.096e+07, tolerance: 4.935e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.127e+07, tolerance: 5.064e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.776e+06, tolerance: 4.699e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.087e+07, tolerance: 4.672e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.032e+07, tolerance: 4.404e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.101e+07, tolerance: 4.794e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.079e+07, tolerance: 4.744e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.061e+07, tolerance: 4.799e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.154e+07, tolerance: 5.012e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.090e+07, tolerance: 4.848e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.095e+07, tolerance: 4.935e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.126e+07, tolerance: 5.064e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.768e+06, tolerance: 4.699e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.086e+07, tolerance: 4.672e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.031e+07, tolerance: 4.404e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.100e+07, tolerance: 4.794e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.078e+07, tolerance: 4.744e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.060e+07, tolerance: 4.799e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.153e+07, tolerance: 5.012e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.089e+07, tolerance: 4.848e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.094e+07, tolerance: 4.935e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.125e+07, tolerance: 5.064e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.762e+06, tolerance: 4.699e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.086e+07, tolerance: 4.672e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.031e+07, tolerance: 4.404e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.099e+07, tolerance: 4.794e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.077e+07, tolerance: 4.744e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.059e+07, tolerance: 4.799e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.152e+07, tolerance: 5.012e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.089e+07, tolerance: 4.848e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.093e+07, tolerance: 4.935e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.124e+07, tolerance: 5.064e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.757e+06, tolerance: 4.699e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.085e+07, tolerance: 4.672e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.030e+07, tolerance: 4.404e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.098e+07, tolerance: 4.794e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.076e+07, tolerance: 4.744e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.059e+07, tolerance: 4.799e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.152e+07, tolerance: 5.012e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.089e+07, tolerance: 4.848e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.093e+07, tolerance: 4.935e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.124e+07, tolerance: 5.064e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.753e+06, tolerance: 4.699e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.085e+07, tolerance: 4.672e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.030e+07, tolerance: 4.404e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.098e+07, tolerance: 4.794e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.075e+07, tolerance: 4.744e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.058e+07, tolerance: 4.799e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.151e+07, tolerance: 5.012e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.088e+07, tolerance: 4.848e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.092e+07, tolerance: 4.935e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.123e+07, tolerance: 5.064e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.750e+06, tolerance: 4.699e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.085e+07, tolerance: 4.672e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.030e+07, tolerance: 4.404e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.097e+07, tolerance: 4.794e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.075e+07, tolerance: 4.744e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.058e+07, tolerance: 4.799e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.151e+07, tolerance: 5.012e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.088e+07, tolerance: 4.848e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.092e+07, tolerance: 4.935e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.123e+07, tolerance: 5.064e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.747e+06, tolerance: 4.699e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.085e+07, tolerance: 4.672e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.029e+07, tolerance: 4.404e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.097e+07, tolerance: 4.794e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.074e+07, tolerance: 4.744e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.058e+07, tolerance: 4.799e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.151e+07, tolerance: 5.012e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.088e+07, tolerance: 4.848e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.091e+07, tolerance: 4.935e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.122e+07, tolerance: 5.064e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.745e+06, tolerance: 4.699e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.084e+07, tolerance: 4.672e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.029e+07, tolerance: 4.404e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.097e+07, tolerance: 4.794e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.074e+07, tolerance: 4.744e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.058e+07, tolerance: 4.799e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.150e+07, tolerance: 5.012e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.088e+07, tolerance: 4.848e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.091e+07, tolerance: 4.935e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.122e+07, tolerance: 5.064e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.743e+06, tolerance: 4.699e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.084e+07, tolerance: 4.672e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.029e+07, tolerance: 4.404e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.096e+07, tolerance: 4.794e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.074e+07, tolerance: 4.744e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.058e+07, tolerance: 4.799e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.150e+07, tolerance: 5.012e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.087e+07, tolerance: 4.848e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.091e+07, tolerance: 4.935e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.122e+07, tolerance: 5.064e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.742e+06, tolerance: 4.699e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.084e+07, tolerance: 4.672e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.029e+07, tolerance: 4.404e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.096e+07, tolerance: 4.794e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.073e+07, tolerance: 4.744e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.057e+07, tolerance: 4.799e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.150e+07, tolerance: 5.012e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.087e+07, tolerance: 4.848e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.091e+07, tolerance: 4.935e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.122e+07, tolerance: 5.064e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.741e+06, tolerance: 4.699e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.084e+07, tolerance: 4.672e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.029e+07, tolerance: 4.404e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.096e+07, tolerance: 4.794e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.073e+07, tolerance: 4.744e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.057e+07, tolerance: 4.799e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.150e+07, tolerance: 5.012e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.087e+07, tolerance: 4.848e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.091e+07, tolerance: 4.935e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.122e+07, tolerance: 5.064e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.740e+06, tolerance: 4.699e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.084e+07, tolerance: 4.672e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.029e+07, tolerance: 4.404e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.096e+07, tolerance: 4.794e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.073e+07, tolerance: 4.744e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.057e+07, tolerance: 4.799e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.150e+07, tolerance: 5.012e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.087e+07, tolerance: 4.848e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.091e+07, tolerance: 4.935e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.122e+07, tolerance: 5.064e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.739e+06, tolerance: 4.699e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.084e+07, tolerance: 4.672e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.029e+07, tolerance: 4.404e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.096e+07, tolerance: 4.794e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.073e+07, tolerance: 4.744e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.057e+07, tolerance: 4.799e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.150e+07, tolerance: 5.012e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.087e+07, tolerance: 4.848e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.090e+07, tolerance: 4.935e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.122e+07, tolerance: 5.064e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.739e+06, tolerance: 4.699e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.084e+07, tolerance: 4.672e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.029e+07, tolerance: 4.404e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.096e+07, tolerance: 4.794e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.073e+07, tolerance: 4.744e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.057e+07, tolerance: 4.799e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.150e+07, tolerance: 5.012e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.087e+07, tolerance: 4.848e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.090e+07, tolerance: 4.935e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.122e+07, tolerance: 5.064e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.738e+06, tolerance: 4.699e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.271e+07, tolerance: 5.332e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(0.01185247763144249print(grid.best_estimator_)Pipeline(steps=[('scaler', StandardScaler()),
('ridge', ElasticNet(alpha=0.01185247763144249, l1_ratio=0))])- 교차 검증 오차의 도표화
ridge_fig, ax = subplots(figsize=(8,8))
ax.errorbar(-np.log(lambdas),
-grid.cv_results_['mean_test_score'],
yerr=grid.cv_results_['std_test_score'] / np.sqrt(K))
ax.set_ylim ([50000 ,250000])
ax.set_xlabel('$-\log(\lambda)$', fontsize=20)
ax.set_ylabel('Cross -validated MSE', fontsize=20);
grid_r2 = skm.GridSearchCV(pipe, param_grid, cv=kfold)
grid_r2.fit(X, Y)/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.336e+07, tolerance: 4.672e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.202e+07, tolerance: 4.404e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.397e+07, tolerance: 4.794e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.372e+07, tolerance: 4.744e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.399e+07, tolerance: 4.799e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.506e+07, tolerance: 5.012e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.424e+07, tolerance: 4.848e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.468e+07, tolerance: 4.935e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.532e+07, tolerance: 5.064e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.350e+07, tolerance: 4.699e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.336e+07, tolerance: 4.672e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.202e+07, tolerance: 4.404e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.397e+07, tolerance: 4.794e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.372e+07, tolerance: 4.744e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.399e+07, tolerance: 4.799e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.506e+07, tolerance: 5.012e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.424e+07, tolerance: 4.848e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.468e+07, tolerance: 4.935e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.532e+07, tolerance: 5.064e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.350e+07, tolerance: 4.699e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.336e+07, tolerance: 4.672e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.202e+07, tolerance: 4.404e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.397e+07, tolerance: 4.794e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.372e+07, tolerance: 4.744e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.399e+07, tolerance: 4.799e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.506e+07, tolerance: 5.012e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.424e+07, tolerance: 4.848e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.468e+07, tolerance: 4.935e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.532e+07, tolerance: 5.064e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.350e+07, tolerance: 4.699e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.336e+07, tolerance: 4.672e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.202e+07, tolerance: 4.404e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.397e+07, tolerance: 4.794e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.372e+07, tolerance: 4.744e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.399e+07, tolerance: 4.799e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.506e+07, tolerance: 5.012e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.424e+07, tolerance: 4.848e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.468e+07, tolerance: 4.935e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.532e+07, tolerance: 5.064e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.350e+07, tolerance: 4.699e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.336e+07, tolerance: 4.672e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.202e+07, tolerance: 4.404e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.397e+07, tolerance: 4.794e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.372e+07, tolerance: 4.744e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.399e+07, tolerance: 4.799e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.506e+07, tolerance: 5.012e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.424e+07, tolerance: 4.848e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.468e+07, tolerance: 4.935e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.532e+07, tolerance: 5.064e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.350e+07, tolerance: 4.699e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.336e+07, tolerance: 4.672e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.202e+07, tolerance: 4.404e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.397e+07, tolerance: 4.794e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.372e+07, tolerance: 4.744e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.399e+07, tolerance: 4.799e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.506e+07, tolerance: 5.012e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.424e+07, tolerance: 4.848e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.468e+07, tolerance: 4.935e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.532e+07, tolerance: 5.064e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.350e+07, tolerance: 4.699e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.336e+07, tolerance: 4.672e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.202e+07, tolerance: 4.404e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.397e+07, tolerance: 4.794e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.372e+07, tolerance: 4.744e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.399e+07, tolerance: 4.799e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.506e+07, tolerance: 5.012e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.424e+07, tolerance: 4.848e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.468e+07, tolerance: 4.935e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.532e+07, tolerance: 5.064e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.350e+07, tolerance: 4.699e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.336e+07, tolerance: 4.672e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.202e+07, tolerance: 4.404e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.397e+07, tolerance: 4.794e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.372e+07, tolerance: 4.744e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.399e+07, tolerance: 4.799e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.506e+07, tolerance: 5.012e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.424e+07, tolerance: 4.848e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.468e+07, tolerance: 4.935e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.532e+07, tolerance: 5.064e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.350e+07, tolerance: 4.699e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.336e+07, tolerance: 4.672e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.202e+07, tolerance: 4.404e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.397e+07, tolerance: 4.794e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.372e+07, tolerance: 4.744e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.399e+07, tolerance: 4.799e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.506e+07, tolerance: 5.012e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.424e+07, tolerance: 4.848e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.467e+07, tolerance: 4.935e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.532e+07, tolerance: 5.064e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.349e+07, tolerance: 4.699e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.336e+07, tolerance: 4.672e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.202e+07, tolerance: 4.404e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.397e+07, tolerance: 4.794e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.372e+07, tolerance: 4.744e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.399e+07, tolerance: 4.799e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.506e+07, tolerance: 5.012e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.424e+07, tolerance: 4.848e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.467e+07, tolerance: 4.935e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.532e+07, tolerance: 5.064e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.349e+07, tolerance: 4.699e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.336e+07, tolerance: 4.672e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.202e+07, tolerance: 4.404e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.397e+07, tolerance: 4.794e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.372e+07, tolerance: 4.744e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.399e+07, tolerance: 4.799e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.506e+07, tolerance: 5.012e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.424e+07, tolerance: 4.848e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.467e+07, tolerance: 4.935e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.532e+07, tolerance: 5.064e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.349e+07, tolerance: 4.699e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.336e+07, tolerance: 4.672e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.202e+07, tolerance: 4.404e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.397e+07, tolerance: 4.794e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.371e+07, tolerance: 4.744e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.399e+07, tolerance: 4.799e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.506e+07, tolerance: 5.012e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.424e+07, tolerance: 4.848e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.467e+07, tolerance: 4.935e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.531e+07, tolerance: 5.064e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.349e+07, tolerance: 4.699e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.336e+07, tolerance: 4.672e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.202e+07, tolerance: 4.404e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.396e+07, tolerance: 4.794e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.371e+07, tolerance: 4.744e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.399e+07, tolerance: 4.799e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.506e+07, tolerance: 5.012e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.424e+07, tolerance: 4.848e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.467e+07, tolerance: 4.935e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.531e+07, tolerance: 5.064e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.349e+07, tolerance: 4.699e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.335e+07, tolerance: 4.672e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.202e+07, tolerance: 4.404e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.396e+07, tolerance: 4.794e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.371e+07, tolerance: 4.744e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.399e+07, tolerance: 4.799e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.505e+07, tolerance: 5.012e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.424e+07, tolerance: 4.848e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.467e+07, tolerance: 4.935e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.531e+07, tolerance: 5.064e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.349e+07, tolerance: 4.699e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.335e+07, tolerance: 4.672e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.201e+07, tolerance: 4.404e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.396e+07, tolerance: 4.794e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.371e+07, tolerance: 4.744e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.398e+07, tolerance: 4.799e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.505e+07, tolerance: 5.012e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.423e+07, tolerance: 4.848e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.467e+07, tolerance: 4.935e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.531e+07, tolerance: 5.064e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.349e+07, tolerance: 4.699e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.335e+07, tolerance: 4.672e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.201e+07, tolerance: 4.404e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.396e+07, tolerance: 4.794e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.371e+07, tolerance: 4.744e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.398e+07, tolerance: 4.799e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.505e+07, tolerance: 5.012e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.423e+07, tolerance: 4.848e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.467e+07, tolerance: 4.935e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.531e+07, tolerance: 5.064e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.349e+07, tolerance: 4.699e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.335e+07, tolerance: 4.672e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.201e+07, tolerance: 4.404e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.396e+07, tolerance: 4.794e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.370e+07, tolerance: 4.744e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.398e+07, tolerance: 4.799e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.505e+07, tolerance: 5.012e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.423e+07, tolerance: 4.848e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.466e+07, tolerance: 4.935e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.530e+07, tolerance: 5.064e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.348e+07, tolerance: 4.699e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.334e+07, tolerance: 4.672e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.201e+07, tolerance: 4.404e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.395e+07, tolerance: 4.794e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.370e+07, tolerance: 4.744e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.398e+07, tolerance: 4.799e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.504e+07, tolerance: 5.012e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.422e+07, tolerance: 4.848e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.466e+07, tolerance: 4.935e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.530e+07, tolerance: 5.064e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.348e+07, tolerance: 4.699e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.334e+07, tolerance: 4.672e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.200e+07, tolerance: 4.404e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.395e+07, tolerance: 4.794e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.370e+07, tolerance: 4.744e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.397e+07, tolerance: 4.799e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.504e+07, tolerance: 5.012e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.422e+07, tolerance: 4.848e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.465e+07, tolerance: 4.935e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.530e+07, tolerance: 5.064e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.347e+07, tolerance: 4.699e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.334e+07, tolerance: 4.672e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.200e+07, tolerance: 4.404e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.394e+07, tolerance: 4.794e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.369e+07, tolerance: 4.744e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.397e+07, tolerance: 4.799e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.503e+07, tolerance: 5.012e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.421e+07, tolerance: 4.848e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.465e+07, tolerance: 4.935e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.529e+07, tolerance: 5.064e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.347e+07, tolerance: 4.699e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.333e+07, tolerance: 4.672e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.199e+07, tolerance: 4.404e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.394e+07, tolerance: 4.794e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.368e+07, tolerance: 4.744e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.396e+07, tolerance: 4.799e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.503e+07, tolerance: 5.012e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.421e+07, tolerance: 4.848e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.464e+07, tolerance: 4.935e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.528e+07, tolerance: 5.064e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.346e+07, tolerance: 4.699e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.332e+07, tolerance: 4.672e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.198e+07, tolerance: 4.404e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.393e+07, tolerance: 4.794e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.367e+07, tolerance: 4.744e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.395e+07, tolerance: 4.799e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.502e+07, tolerance: 5.012e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.420e+07, tolerance: 4.848e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.463e+07, tolerance: 4.935e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.527e+07, tolerance: 5.064e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.345e+07, tolerance: 4.699e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.331e+07, tolerance: 4.672e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.197e+07, tolerance: 4.404e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.392e+07, tolerance: 4.794e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.366e+07, tolerance: 4.744e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.394e+07, tolerance: 4.799e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.500e+07, tolerance: 5.012e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.419e+07, tolerance: 4.848e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.462e+07, tolerance: 4.935e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.526e+07, tolerance: 5.064e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.344e+07, tolerance: 4.699e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.330e+07, tolerance: 4.672e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.196e+07, tolerance: 4.404e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.390e+07, tolerance: 4.794e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.365e+07, tolerance: 4.744e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.392e+07, tolerance: 4.799e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.499e+07, tolerance: 5.012e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.417e+07, tolerance: 4.848e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.461e+07, tolerance: 4.935e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.525e+07, tolerance: 5.064e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.342e+07, tolerance: 4.699e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.328e+07, tolerance: 4.672e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.194e+07, tolerance: 4.404e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.389e+07, tolerance: 4.794e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.363e+07, tolerance: 4.744e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.390e+07, tolerance: 4.799e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.497e+07, tolerance: 5.012e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.415e+07, tolerance: 4.848e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.459e+07, tolerance: 4.935e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.523e+07, tolerance: 5.064e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.340e+07, tolerance: 4.699e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.326e+07, tolerance: 4.672e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.192e+07, tolerance: 4.404e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.386e+07, tolerance: 4.794e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.361e+07, tolerance: 4.744e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.388e+07, tolerance: 4.799e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.495e+07, tolerance: 5.012e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.413e+07, tolerance: 4.848e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.457e+07, tolerance: 4.935e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.520e+07, tolerance: 5.064e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.338e+07, tolerance: 4.699e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.323e+07, tolerance: 4.672e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.190e+07, tolerance: 4.404e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.384e+07, tolerance: 4.794e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.358e+07, tolerance: 4.744e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.385e+07, tolerance: 4.799e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.492e+07, tolerance: 5.012e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.410e+07, tolerance: 4.848e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.454e+07, tolerance: 4.935e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.517e+07, tolerance: 5.064e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.335e+07, tolerance: 4.699e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.320e+07, tolerance: 4.672e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.187e+07, tolerance: 4.404e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.380e+07, tolerance: 4.794e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.354e+07, tolerance: 4.744e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.382e+07, tolerance: 4.799e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.488e+07, tolerance: 5.012e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.407e+07, tolerance: 4.848e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.450e+07, tolerance: 4.935e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.514e+07, tolerance: 5.064e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.331e+07, tolerance: 4.699e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.316e+07, tolerance: 4.672e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.183e+07, tolerance: 4.404e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.376e+07, tolerance: 4.794e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.350e+07, tolerance: 4.744e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.377e+07, tolerance: 4.799e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.484e+07, tolerance: 5.012e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.402e+07, tolerance: 4.848e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.445e+07, tolerance: 4.935e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.509e+07, tolerance: 5.064e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.326e+07, tolerance: 4.699e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.311e+07, tolerance: 4.672e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.178e+07, tolerance: 4.404e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.371e+07, tolerance: 4.794e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.344e+07, tolerance: 4.744e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.371e+07, tolerance: 4.799e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.478e+07, tolerance: 5.012e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.397e+07, tolerance: 4.848e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.440e+07, tolerance: 4.935e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.503e+07, tolerance: 5.064e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.320e+07, tolerance: 4.699e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.304e+07, tolerance: 4.672e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.172e+07, tolerance: 4.404e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.364e+07, tolerance: 4.794e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.337e+07, tolerance: 4.744e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.364e+07, tolerance: 4.799e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.471e+07, tolerance: 5.012e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.390e+07, tolerance: 4.848e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.433e+07, tolerance: 4.935e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.496e+07, tolerance: 5.064e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.312e+07, tolerance: 4.699e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.296e+07, tolerance: 4.672e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.164e+07, tolerance: 4.404e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.356e+07, tolerance: 4.794e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.328e+07, tolerance: 4.744e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.356e+07, tolerance: 4.799e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.462e+07, tolerance: 5.012e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.381e+07, tolerance: 4.848e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.424e+07, tolerance: 4.935e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.487e+07, tolerance: 5.064e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.303e+07, tolerance: 4.699e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.287e+07, tolerance: 4.672e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.154e+07, tolerance: 4.404e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.346e+07, tolerance: 4.794e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.317e+07, tolerance: 4.744e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.345e+07, tolerance: 4.799e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.451e+07, tolerance: 5.012e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.370e+07, tolerance: 4.848e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.413e+07, tolerance: 4.935e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.476e+07, tolerance: 5.064e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.291e+07, tolerance: 4.699e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.274e+07, tolerance: 4.672e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.143e+07, tolerance: 4.404e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.333e+07, tolerance: 4.794e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.304e+07, tolerance: 4.744e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.331e+07, tolerance: 4.799e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.438e+07, tolerance: 5.012e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.357e+07, tolerance: 4.848e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.400e+07, tolerance: 4.935e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.462e+07, tolerance: 5.064e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.276e+07, tolerance: 4.699e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.260e+07, tolerance: 4.672e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.128e+07, tolerance: 4.404e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.318e+07, tolerance: 4.794e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.287e+07, tolerance: 4.744e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.315e+07, tolerance: 4.799e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.422e+07, tolerance: 5.012e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.341e+07, tolerance: 4.848e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.383e+07, tolerance: 4.935e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.445e+07, tolerance: 5.064e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.259e+07, tolerance: 4.699e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.241e+07, tolerance: 4.672e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.111e+07, tolerance: 4.404e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.299e+07, tolerance: 4.794e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.268e+07, tolerance: 4.744e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.295e+07, tolerance: 4.799e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.402e+07, tolerance: 5.012e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.321e+07, tolerance: 4.848e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.363e+07, tolerance: 4.935e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.425e+07, tolerance: 5.064e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.237e+07, tolerance: 4.699e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.220e+07, tolerance: 4.672e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.090e+07, tolerance: 4.404e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.277e+07, tolerance: 4.794e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.244e+07, tolerance: 4.744e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.271e+07, tolerance: 4.799e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.378e+07, tolerance: 5.012e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.297e+07, tolerance: 4.848e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.339e+07, tolerance: 4.935e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.400e+07, tolerance: 5.064e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.211e+07, tolerance: 4.699e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.194e+07, tolerance: 4.672e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.065e+07, tolerance: 4.404e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.250e+07, tolerance: 4.794e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.215e+07, tolerance: 4.744e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.242e+07, tolerance: 4.799e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.349e+07, tolerance: 5.012e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.269e+07, tolerance: 4.848e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.311e+07, tolerance: 4.935e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.371e+07, tolerance: 5.064e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.181e+07, tolerance: 4.699e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.163e+07, tolerance: 4.672e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.035e+07, tolerance: 4.404e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.218e+07, tolerance: 4.794e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.182e+07, tolerance: 4.744e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.208e+07, tolerance: 4.799e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.315e+07, tolerance: 5.012e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.235e+07, tolerance: 4.848e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.277e+07, tolerance: 4.935e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.336e+07, tolerance: 5.064e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.144e+07, tolerance: 4.699e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.127e+07, tolerance: 4.672e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.000e+07, tolerance: 4.404e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.182e+07, tolerance: 4.794e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.143e+07, tolerance: 4.744e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.169e+07, tolerance: 4.799e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.276e+07, tolerance: 5.012e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.196e+07, tolerance: 4.848e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.238e+07, tolerance: 4.935e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.296e+07, tolerance: 5.064e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.102e+07, tolerance: 4.699e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.087e+07, tolerance: 4.672e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.961e+07, tolerance: 4.404e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.140e+07, tolerance: 4.794e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.099e+07, tolerance: 4.744e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.124e+07, tolerance: 4.799e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.231e+07, tolerance: 5.012e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.152e+07, tolerance: 4.848e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.193e+07, tolerance: 4.935e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.250e+07, tolerance: 5.064e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.054e+07, tolerance: 4.699e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.041e+07, tolerance: 4.672e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.917e+07, tolerance: 4.404e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.094e+07, tolerance: 4.794e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.049e+07, tolerance: 4.744e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.074e+07, tolerance: 4.799e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.181e+07, tolerance: 5.012e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.102e+07, tolerance: 4.848e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.143e+07, tolerance: 4.935e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.199e+07, tolerance: 5.064e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.000e+07, tolerance: 4.699e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.991e+07, tolerance: 4.672e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.868e+07, tolerance: 4.404e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.043e+07, tolerance: 4.794e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.995e+07, tolerance: 4.744e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.019e+07, tolerance: 4.799e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.127e+07, tolerance: 5.012e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.048e+07, tolerance: 4.848e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.088e+07, tolerance: 4.935e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.143e+07, tolerance: 5.064e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.941e+07, tolerance: 4.699e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.937e+07, tolerance: 4.672e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.816e+07, tolerance: 4.404e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.988e+07, tolerance: 4.794e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.937e+07, tolerance: 4.744e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.959e+07, tolerance: 4.799e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.068e+07, tolerance: 5.012e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.989e+07, tolerance: 4.848e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.029e+07, tolerance: 4.935e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.082e+07, tolerance: 5.064e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.878e+07, tolerance: 4.699e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.880e+07, tolerance: 4.672e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.761e+07, tolerance: 4.404e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.931e+07, tolerance: 4.794e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.877e+07, tolerance: 4.744e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.897e+07, tolerance: 4.799e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.006e+07, tolerance: 5.012e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.928e+07, tolerance: 4.848e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.967e+07, tolerance: 4.935e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.019e+07, tolerance: 5.064e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.812e+07, tolerance: 4.699e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.822e+07, tolerance: 4.672e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.705e+07, tolerance: 4.404e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.873e+07, tolerance: 4.794e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.816e+07, tolerance: 4.744e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.834e+07, tolerance: 4.799e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.944e+07, tolerance: 5.012e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.865e+07, tolerance: 4.848e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.904e+07, tolerance: 4.935e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.954e+07, tolerance: 5.064e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.744e+07, tolerance: 4.699e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.765e+07, tolerance: 4.672e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.649e+07, tolerance: 4.404e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.815e+07, tolerance: 4.794e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.755e+07, tolerance: 4.744e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.771e+07, tolerance: 4.799e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.881e+07, tolerance: 5.012e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.802e+07, tolerance: 4.848e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.841e+07, tolerance: 4.935e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.889e+07, tolerance: 5.064e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.677e+07, tolerance: 4.699e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.708e+07, tolerance: 4.672e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.594e+07, tolerance: 4.404e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.758e+07, tolerance: 4.794e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.696e+07, tolerance: 4.744e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.709e+07, tolerance: 4.799e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.820e+07, tolerance: 5.012e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.741e+07, tolerance: 4.848e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.780e+07, tolerance: 4.935e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.826e+07, tolerance: 5.064e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.612e+07, tolerance: 4.699e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.654e+07, tolerance: 4.672e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.542e+07, tolerance: 4.404e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.704e+07, tolerance: 4.794e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.640e+07, tolerance: 4.744e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.650e+07, tolerance: 4.799e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.762e+07, tolerance: 5.012e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.682e+07, tolerance: 4.848e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.721e+07, tolerance: 4.935e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.766e+07, tolerance: 5.064e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.549e+07, tolerance: 4.699e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.604e+07, tolerance: 4.672e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.494e+07, tolerance: 4.404e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.654e+07, tolerance: 4.794e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.588e+07, tolerance: 4.744e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.595e+07, tolerance: 4.799e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.708e+07, tolerance: 5.012e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.627e+07, tolerance: 4.848e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.666e+07, tolerance: 4.935e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.709e+07, tolerance: 5.064e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.491e+07, tolerance: 4.699e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.557e+07, tolerance: 4.672e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.449e+07, tolerance: 4.404e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.607e+07, tolerance: 4.794e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.541e+07, tolerance: 4.744e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.544e+07, tolerance: 4.799e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.658e+07, tolerance: 5.012e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.577e+07, tolerance: 4.848e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.615e+07, tolerance: 4.935e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.656e+07, tolerance: 5.064e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.437e+07, tolerance: 4.699e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.515e+07, tolerance: 4.672e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.408e+07, tolerance: 4.404e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.564e+07, tolerance: 4.794e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.498e+07, tolerance: 4.744e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.498e+07, tolerance: 4.799e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.613e+07, tolerance: 5.012e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.531e+07, tolerance: 4.848e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.569e+07, tolerance: 4.935e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.608e+07, tolerance: 5.064e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.389e+07, tolerance: 4.699e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.477e+07, tolerance: 4.672e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.372e+07, tolerance: 4.404e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.525e+07, tolerance: 4.794e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.459e+07, tolerance: 4.744e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.456e+07, tolerance: 4.799e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.572e+07, tolerance: 5.012e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.489e+07, tolerance: 4.848e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.528e+07, tolerance: 4.935e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.565e+07, tolerance: 5.064e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.345e+07, tolerance: 4.699e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.442e+07, tolerance: 4.672e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.340e+07, tolerance: 4.404e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.491e+07, tolerance: 4.794e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.425e+07, tolerance: 4.744e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.419e+07, tolerance: 4.799e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.535e+07, tolerance: 5.012e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.452e+07, tolerance: 4.848e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.490e+07, tolerance: 4.935e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.526e+07, tolerance: 5.064e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.306e+07, tolerance: 4.699e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.412e+07, tolerance: 4.672e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.311e+07, tolerance: 4.404e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.460e+07, tolerance: 4.794e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.395e+07, tolerance: 4.744e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.386e+07, tolerance: 4.799e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.502e+07, tolerance: 5.012e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.419e+07, tolerance: 4.848e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.457e+07, tolerance: 4.935e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.490e+07, tolerance: 5.064e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.272e+07, tolerance: 4.699e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.385e+07, tolerance: 4.672e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.286e+07, tolerance: 4.404e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.431e+07, tolerance: 4.794e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.368e+07, tolerance: 4.744e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.356e+07, tolerance: 4.799e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.473e+07, tolerance: 5.012e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.389e+07, tolerance: 4.848e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.428e+07, tolerance: 4.935e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.459e+07, tolerance: 5.064e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.242e+07, tolerance: 4.699e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.360e+07, tolerance: 4.672e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.264e+07, tolerance: 4.404e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.406e+07, tolerance: 4.794e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.345e+07, tolerance: 4.744e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.330e+07, tolerance: 4.799e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.447e+07, tolerance: 5.012e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.363e+07, tolerance: 4.848e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.401e+07, tolerance: 4.935e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.431e+07, tolerance: 5.064e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.215e+07, tolerance: 4.699e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.339e+07, tolerance: 4.672e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.244e+07, tolerance: 4.404e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.383e+07, tolerance: 4.794e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.324e+07, tolerance: 4.744e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.306e+07, tolerance: 4.799e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.423e+07, tolerance: 5.012e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.339e+07, tolerance: 4.848e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.378e+07, tolerance: 4.935e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.406e+07, tolerance: 5.064e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.192e+07, tolerance: 4.699e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.319e+07, tolerance: 4.672e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.227e+07, tolerance: 4.404e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.363e+07, tolerance: 4.794e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.305e+07, tolerance: 4.744e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.285e+07, tolerance: 4.799e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.402e+07, tolerance: 5.012e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.318e+07, tolerance: 4.848e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.356e+07, tolerance: 4.935e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.383e+07, tolerance: 5.064e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.171e+07, tolerance: 4.699e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.301e+07, tolerance: 4.672e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.211e+07, tolerance: 4.404e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.343e+07, tolerance: 4.794e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.288e+07, tolerance: 4.744e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.266e+07, tolerance: 4.799e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.383e+07, tolerance: 5.012e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.299e+07, tolerance: 4.848e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.337e+07, tolerance: 4.935e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.362e+07, tolerance: 5.064e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.153e+07, tolerance: 4.699e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.285e+07, tolerance: 4.672e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.197e+07, tolerance: 4.404e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.325e+07, tolerance: 4.794e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.273e+07, tolerance: 4.744e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.249e+07, tolerance: 4.799e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.365e+07, tolerance: 5.012e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.281e+07, tolerance: 4.848e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.319e+07, tolerance: 4.935e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.343e+07, tolerance: 5.064e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.137e+07, tolerance: 4.699e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.270e+07, tolerance: 4.672e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.184e+07, tolerance: 4.404e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.309e+07, tolerance: 4.794e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.259e+07, tolerance: 4.744e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.232e+07, tolerance: 4.799e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.348e+07, tolerance: 5.012e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.265e+07, tolerance: 4.848e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.303e+07, tolerance: 4.935e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.326e+07, tolerance: 5.064e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.122e+07, tolerance: 4.699e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.255e+07, tolerance: 4.672e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.172e+07, tolerance: 4.404e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.293e+07, tolerance: 4.794e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.245e+07, tolerance: 4.744e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.217e+07, tolerance: 4.799e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.332e+07, tolerance: 5.012e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.250e+07, tolerance: 4.848e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.287e+07, tolerance: 4.935e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.309e+07, tolerance: 5.064e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.109e+07, tolerance: 4.699e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.242e+07, tolerance: 4.672e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.160e+07, tolerance: 4.404e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.277e+07, tolerance: 4.794e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.233e+07, tolerance: 4.744e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.203e+07, tolerance: 4.799e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.317e+07, tolerance: 5.012e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.236e+07, tolerance: 4.848e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.272e+07, tolerance: 4.935e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.294e+07, tolerance: 5.064e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.096e+07, tolerance: 4.699e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.229e+07, tolerance: 4.672e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.149e+07, tolerance: 4.404e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.262e+07, tolerance: 4.794e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.221e+07, tolerance: 4.744e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.190e+07, tolerance: 4.799e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.303e+07, tolerance: 5.012e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.222e+07, tolerance: 4.848e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.257e+07, tolerance: 4.935e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.279e+07, tolerance: 5.064e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.085e+07, tolerance: 4.699e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.216e+07, tolerance: 4.672e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.138e+07, tolerance: 4.404e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.248e+07, tolerance: 4.794e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.209e+07, tolerance: 4.744e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.177e+07, tolerance: 4.799e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.289e+07, tolerance: 5.012e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.209e+07, tolerance: 4.848e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.243e+07, tolerance: 4.935e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.265e+07, tolerance: 5.064e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.074e+07, tolerance: 4.699e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.204e+07, tolerance: 4.672e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.128e+07, tolerance: 4.404e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.234e+07, tolerance: 4.794e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.198e+07, tolerance: 4.744e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.165e+07, tolerance: 4.799e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.275e+07, tolerance: 5.012e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.196e+07, tolerance: 4.848e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.229e+07, tolerance: 4.935e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.251e+07, tolerance: 5.064e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.064e+07, tolerance: 4.699e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.192e+07, tolerance: 4.672e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.118e+07, tolerance: 4.404e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.220e+07, tolerance: 4.794e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.187e+07, tolerance: 4.744e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.153e+07, tolerance: 4.799e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.262e+07, tolerance: 5.012e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.185e+07, tolerance: 4.848e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.216e+07, tolerance: 4.935e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.238e+07, tolerance: 5.064e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.055e+07, tolerance: 4.699e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.180e+07, tolerance: 4.672e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.109e+07, tolerance: 4.404e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.208e+07, tolerance: 4.794e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.176e+07, tolerance: 4.744e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.142e+07, tolerance: 4.799e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.250e+07, tolerance: 5.012e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.173e+07, tolerance: 4.848e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.203e+07, tolerance: 4.935e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.226e+07, tolerance: 5.064e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.046e+07, tolerance: 4.699e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.169e+07, tolerance: 4.672e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.100e+07, tolerance: 4.404e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.195e+07, tolerance: 4.794e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.166e+07, tolerance: 4.744e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.132e+07, tolerance: 4.799e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.239e+07, tolerance: 5.012e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.163e+07, tolerance: 4.848e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.191e+07, tolerance: 4.935e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.214e+07, tolerance: 5.064e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.038e+07, tolerance: 4.699e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.159e+07, tolerance: 4.672e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.092e+07, tolerance: 4.404e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.184e+07, tolerance: 4.794e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.157e+07, tolerance: 4.744e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.123e+07, tolerance: 4.799e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.228e+07, tolerance: 5.012e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.153e+07, tolerance: 4.848e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.179e+07, tolerance: 4.935e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.203e+07, tolerance: 5.064e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.030e+07, tolerance: 4.699e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.149e+07, tolerance: 4.672e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.084e+07, tolerance: 4.404e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.173e+07, tolerance: 4.794e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.148e+07, tolerance: 4.744e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.114e+07, tolerance: 4.799e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.218e+07, tolerance: 5.012e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.144e+07, tolerance: 4.848e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.168e+07, tolerance: 4.935e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.193e+07, tolerance: 5.064e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.023e+07, tolerance: 4.699e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.140e+07, tolerance: 4.672e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.077e+07, tolerance: 4.404e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.163e+07, tolerance: 4.794e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.139e+07, tolerance: 4.744e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.106e+07, tolerance: 4.799e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.209e+07, tolerance: 5.012e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.136e+07, tolerance: 4.848e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.158e+07, tolerance: 4.935e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.184e+07, tolerance: 5.064e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.017e+07, tolerance: 4.699e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.132e+07, tolerance: 4.672e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.070e+07, tolerance: 4.404e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.154e+07, tolerance: 4.794e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.132e+07, tolerance: 4.744e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.099e+07, tolerance: 4.799e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.201e+07, tolerance: 5.012e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.128e+07, tolerance: 4.848e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.149e+07, tolerance: 4.935e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.175e+07, tolerance: 5.064e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.011e+07, tolerance: 4.699e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.125e+07, tolerance: 4.672e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.064e+07, tolerance: 4.404e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.146e+07, tolerance: 4.794e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.124e+07, tolerance: 4.744e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.093e+07, tolerance: 4.799e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.194e+07, tolerance: 5.012e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.122e+07, tolerance: 4.848e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.140e+07, tolerance: 4.935e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.168e+07, tolerance: 5.064e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.006e+07, tolerance: 4.699e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.118e+07, tolerance: 4.672e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.059e+07, tolerance: 4.404e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.139e+07, tolerance: 4.794e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.118e+07, tolerance: 4.744e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.088e+07, tolerance: 4.799e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.187e+07, tolerance: 5.012e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.116e+07, tolerance: 4.848e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.133e+07, tolerance: 4.935e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.161e+07, tolerance: 5.064e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.001e+07, tolerance: 4.699e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.112e+07, tolerance: 4.672e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.054e+07, tolerance: 4.404e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.132e+07, tolerance: 4.794e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.111e+07, tolerance: 4.744e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.083e+07, tolerance: 4.799e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.181e+07, tolerance: 5.012e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.111e+07, tolerance: 4.848e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.126e+07, tolerance: 4.935e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.155e+07, tolerance: 5.064e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.968e+06, tolerance: 4.699e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.107e+07, tolerance: 4.672e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.050e+07, tolerance: 4.404e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.126e+07, tolerance: 4.794e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.106e+07, tolerance: 4.744e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.079e+07, tolerance: 4.799e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.176e+07, tolerance: 5.012e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.107e+07, tolerance: 4.848e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.120e+07, tolerance: 4.935e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.150e+07, tolerance: 5.064e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.931e+06, tolerance: 4.699e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.103e+07, tolerance: 4.672e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.047e+07, tolerance: 4.404e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.121e+07, tolerance: 4.794e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.101e+07, tolerance: 4.744e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.075e+07, tolerance: 4.799e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.172e+07, tolerance: 5.012e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.104e+07, tolerance: 4.848e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.115e+07, tolerance: 4.935e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.145e+07, tolerance: 5.064e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.899e+06, tolerance: 4.699e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.100e+07, tolerance: 4.672e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.044e+07, tolerance: 4.404e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.117e+07, tolerance: 4.794e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.097e+07, tolerance: 4.744e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.072e+07, tolerance: 4.799e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.168e+07, tolerance: 5.012e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.101e+07, tolerance: 4.848e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.111e+07, tolerance: 4.935e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.141e+07, tolerance: 5.064e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.871e+06, tolerance: 4.699e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.097e+07, tolerance: 4.672e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.041e+07, tolerance: 4.404e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.113e+07, tolerance: 4.794e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.093e+07, tolerance: 4.744e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.069e+07, tolerance: 4.799e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.165e+07, tolerance: 5.012e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.098e+07, tolerance: 4.848e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.107e+07, tolerance: 4.935e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.137e+07, tolerance: 5.064e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.848e+06, tolerance: 4.699e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.094e+07, tolerance: 4.672e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.039e+07, tolerance: 4.404e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.110e+07, tolerance: 4.794e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.089e+07, tolerance: 4.744e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.067e+07, tolerance: 4.799e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.162e+07, tolerance: 5.012e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.096e+07, tolerance: 4.848e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.104e+07, tolerance: 4.935e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.135e+07, tolerance: 5.064e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.828e+06, tolerance: 4.699e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.092e+07, tolerance: 4.672e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.037e+07, tolerance: 4.404e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.107e+07, tolerance: 4.794e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.086e+07, tolerance: 4.744e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.065e+07, tolerance: 4.799e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.160e+07, tolerance: 5.012e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.094e+07, tolerance: 4.848e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.101e+07, tolerance: 4.935e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.132e+07, tolerance: 5.064e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.811e+06, tolerance: 4.699e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.091e+07, tolerance: 4.672e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.035e+07, tolerance: 4.404e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.105e+07, tolerance: 4.794e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.084e+07, tolerance: 4.744e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.064e+07, tolerance: 4.799e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.158e+07, tolerance: 5.012e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.093e+07, tolerance: 4.848e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.099e+07, tolerance: 4.935e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.130e+07, tolerance: 5.064e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.797e+06, tolerance: 4.699e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.089e+07, tolerance: 4.672e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.034e+07, tolerance: 4.404e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.103e+07, tolerance: 4.794e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.082e+07, tolerance: 4.744e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.063e+07, tolerance: 4.799e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.156e+07, tolerance: 5.012e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.092e+07, tolerance: 4.848e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.097e+07, tolerance: 4.935e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.128e+07, tolerance: 5.064e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.786e+06, tolerance: 4.699e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.088e+07, tolerance: 4.672e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.033e+07, tolerance: 4.404e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.102e+07, tolerance: 4.794e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.080e+07, tolerance: 4.744e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.061e+07, tolerance: 4.799e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.155e+07, tolerance: 5.012e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.091e+07, tolerance: 4.848e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.096e+07, tolerance: 4.935e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.127e+07, tolerance: 5.064e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.776e+06, tolerance: 4.699e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.087e+07, tolerance: 4.672e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.032e+07, tolerance: 4.404e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.101e+07, tolerance: 4.794e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.079e+07, tolerance: 4.744e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.061e+07, tolerance: 4.799e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.154e+07, tolerance: 5.012e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.090e+07, tolerance: 4.848e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.095e+07, tolerance: 4.935e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.126e+07, tolerance: 5.064e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.768e+06, tolerance: 4.699e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.086e+07, tolerance: 4.672e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.031e+07, tolerance: 4.404e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.100e+07, tolerance: 4.794e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.078e+07, tolerance: 4.744e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.060e+07, tolerance: 4.799e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.153e+07, tolerance: 5.012e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.089e+07, tolerance: 4.848e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.094e+07, tolerance: 4.935e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.125e+07, tolerance: 5.064e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.762e+06, tolerance: 4.699e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.086e+07, tolerance: 4.672e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.031e+07, tolerance: 4.404e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.099e+07, tolerance: 4.794e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.077e+07, tolerance: 4.744e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.059e+07, tolerance: 4.799e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.152e+07, tolerance: 5.012e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.089e+07, tolerance: 4.848e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.093e+07, tolerance: 4.935e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.124e+07, tolerance: 5.064e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.757e+06, tolerance: 4.699e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.085e+07, tolerance: 4.672e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.030e+07, tolerance: 4.404e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.098e+07, tolerance: 4.794e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.076e+07, tolerance: 4.744e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.059e+07, tolerance: 4.799e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.152e+07, tolerance: 5.012e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.089e+07, tolerance: 4.848e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.093e+07, tolerance: 4.935e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.124e+07, tolerance: 5.064e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.753e+06, tolerance: 4.699e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.085e+07, tolerance: 4.672e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.030e+07, tolerance: 4.404e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.098e+07, tolerance: 4.794e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.075e+07, tolerance: 4.744e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.058e+07, tolerance: 4.799e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.151e+07, tolerance: 5.012e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.088e+07, tolerance: 4.848e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.092e+07, tolerance: 4.935e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.123e+07, tolerance: 5.064e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.750e+06, tolerance: 4.699e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.085e+07, tolerance: 4.672e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.030e+07, tolerance: 4.404e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.097e+07, tolerance: 4.794e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.075e+07, tolerance: 4.744e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.058e+07, tolerance: 4.799e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.151e+07, tolerance: 5.012e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.088e+07, tolerance: 4.848e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.092e+07, tolerance: 4.935e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.123e+07, tolerance: 5.064e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.747e+06, tolerance: 4.699e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.085e+07, tolerance: 4.672e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.029e+07, tolerance: 4.404e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.097e+07, tolerance: 4.794e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.074e+07, tolerance: 4.744e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.058e+07, tolerance: 4.799e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.151e+07, tolerance: 5.012e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.088e+07, tolerance: 4.848e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.091e+07, tolerance: 4.935e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.122e+07, tolerance: 5.064e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.745e+06, tolerance: 4.699e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.084e+07, tolerance: 4.672e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.029e+07, tolerance: 4.404e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.097e+07, tolerance: 4.794e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.074e+07, tolerance: 4.744e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.058e+07, tolerance: 4.799e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.150e+07, tolerance: 5.012e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.088e+07, tolerance: 4.848e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.091e+07, tolerance: 4.935e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.122e+07, tolerance: 5.064e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.743e+06, tolerance: 4.699e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.084e+07, tolerance: 4.672e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.029e+07, tolerance: 4.404e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.096e+07, tolerance: 4.794e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.074e+07, tolerance: 4.744e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.058e+07, tolerance: 4.799e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.150e+07, tolerance: 5.012e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.087e+07, tolerance: 4.848e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.091e+07, tolerance: 4.935e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.122e+07, tolerance: 5.064e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.742e+06, tolerance: 4.699e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.084e+07, tolerance: 4.672e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.029e+07, tolerance: 4.404e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.096e+07, tolerance: 4.794e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.073e+07, tolerance: 4.744e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.057e+07, tolerance: 4.799e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.150e+07, tolerance: 5.012e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.087e+07, tolerance: 4.848e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.091e+07, tolerance: 4.935e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.122e+07, tolerance: 5.064e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.741e+06, tolerance: 4.699e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.084e+07, tolerance: 4.672e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.029e+07, tolerance: 4.404e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.096e+07, tolerance: 4.794e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.073e+07, tolerance: 4.744e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.057e+07, tolerance: 4.799e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.150e+07, tolerance: 5.012e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.087e+07, tolerance: 4.848e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.091e+07, tolerance: 4.935e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.122e+07, tolerance: 5.064e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.740e+06, tolerance: 4.699e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.084e+07, tolerance: 4.672e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.029e+07, tolerance: 4.404e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.096e+07, tolerance: 4.794e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.073e+07, tolerance: 4.744e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.057e+07, tolerance: 4.799e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.150e+07, tolerance: 5.012e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.087e+07, tolerance: 4.848e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.091e+07, tolerance: 4.935e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.122e+07, tolerance: 5.064e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.739e+06, tolerance: 4.699e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.084e+07, tolerance: 4.672e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.029e+07, tolerance: 4.404e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.096e+07, tolerance: 4.794e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.073e+07, tolerance: 4.744e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.057e+07, tolerance: 4.799e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.150e+07, tolerance: 5.012e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.087e+07, tolerance: 4.848e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.090e+07, tolerance: 4.935e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.122e+07, tolerance: 5.064e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.739e+06, tolerance: 4.699e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.084e+07, tolerance: 4.672e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.029e+07, tolerance: 4.404e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.096e+07, tolerance: 4.794e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.073e+07, tolerance: 4.744e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.057e+07, tolerance: 4.799e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.150e+07, tolerance: 5.012e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.087e+07, tolerance: 4.848e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.090e+07, tolerance: 4.935e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.122e+07, tolerance: 5.064e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.738e+06, tolerance: 4.699e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.513e+07, tolerance: 5.332e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(GridSearchCV(cv=KFold(n_splits=10, random_state=0, shuffle=True),
estimator=Pipeline(steps=[('scaler', StandardScaler()),
('ridge',
ElasticNet(alpha=10.0, l1_ratio=0))]),
param_grid={'ridge__alpha': array([2.22093791e+05, 1.76005531e+05, 1.39481373e+05, 1.10536603e+05,
8.75983676e+04, 6.94202082e+04, 5.50143278e+04, 4.35979140e+04,
3.45506012e+04, 2.73807606e+04, 2.1...
4.67486141e-03, 3.70474772e-03, 2.93594921e-03, 2.32668954e-03,
1.84386167e-03, 1.46122884e-03, 1.15799887e-03, 9.17694298e-04,
7.27257037e-04, 5.76338765e-04, 4.56738615e-04, 3.61957541e-04,
2.86845161e-04, 2.27319885e-04, 1.80147121e-04, 1.42763513e-04,
1.13137642e-04, 8.96596467e-05, 7.10537367e-05, 5.63088712e-05,
4.46238174e-05, 3.53636122e-05, 2.80250579e-05, 2.22093791e-05])})In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
GridSearchCV(cv=KFold(n_splits=10, random_state=0, shuffle=True),
estimator=Pipeline(steps=[('scaler', StandardScaler()),
('ridge',
ElasticNet(alpha=10.0, l1_ratio=0))]),
param_grid={'ridge__alpha': array([2.22093791e+05, 1.76005531e+05, 1.39481373e+05, 1.10536603e+05,
8.75983676e+04, 6.94202082e+04, 5.50143278e+04, 4.35979140e+04,
3.45506012e+04, 2.73807606e+04, 2.1...
4.67486141e-03, 3.70474772e-03, 2.93594921e-03, 2.32668954e-03,
1.84386167e-03, 1.46122884e-03, 1.15799887e-03, 9.17694298e-04,
7.27257037e-04, 5.76338765e-04, 4.56738615e-04, 3.61957541e-04,
2.86845161e-04, 2.27319885e-04, 1.80147121e-04, 1.42763513e-04,
1.13137642e-04, 8.96596467e-05, 7.10537367e-05, 5.63088712e-05,
4.46238174e-05, 3.53636122e-05, 2.80250579e-05, 2.22093791e-05])})Pipeline(steps=[('scaler', StandardScaler()),
('ridge', ElasticNet(alpha=10.0, l1_ratio=0))])StandardScaler()
ElasticNet(alpha=10.0, l1_ratio=0)
r2_fig, ax = subplots(figsize=(8,8))
ax.errorbar(-np.log(lambdas),
grid_r2.cv_results_['mean_test_score'],
yerr = grid_r2.cv_results_['std_test_score'] / np.sqrt(K)
)
ax.set_xlabel('$-\log(\lambda)$', fontsize=20)
ax.set_ylabel('Cross -validated $R^2$', fontsize=20);
- 교차 검증 오차를 통해서 초매개변수를 선택
ridgeCV = skl.ElasticNetCV(alphas=lambdas, l1_ratio=0, cv=kfold)
pipeCV = Pipeline(steps=[('scaler', scaler), ('ridge', ridgeCV)])
pipeCV.fit(X, Y)/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 23360008.18374924, tolerance: 4672.060092440771
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 23359931.65152095, tolerance: 4672.060092440771
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 23359835.080517568, tolerance: 4672.060092440771
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 23359713.22446398, tolerance: 4672.060092440771
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 23359559.46391353, tolerance: 4672.060092440771
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 23359365.447029922, tolerance: 4672.060092440771
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 23359120.6365163, tolerance: 4672.060092440771
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 23358811.738240395, tolerance: 4672.060092440771
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 23358421.980775755, tolerance: 4672.060092440771
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 23357930.207136612, tolerance: 4672.060092440771
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 23357309.730031174, tolerance: 4672.060092440771
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 23356526.889509488, tolerance: 4672.060092440771
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 23355539.23634904, tolerance: 4672.060092440771
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 23354293.24519922, tolerance: 4672.060092440771
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 23352721.437566437, tolerance: 4672.060092440771
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 23350738.76521436, tolerance: 4672.060092440771
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 23348238.06843002, tolerance: 4672.060092440771
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 23345084.379780084, tolerance: 4672.060092440771
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 23341107.791451532, tolerance: 4672.060092440771
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 23336094.54235502, tolerance: 4672.060092440771
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 23329775.909903746, tolerance: 4672.060092440771
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 23321814.412198476, tolerance: 4672.060092440771
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 23311786.74317354, tolerance: 4672.060092440771
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 23299162.784232218, tolerance: 4672.060092440771
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 23283279.976032775, tolerance: 4672.060092440771
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 23263312.319184583, tolerance: 4672.060092440771
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 23238233.344879705, tolerance: 4672.060092440771
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 23206772.622058388, tolerance: 4672.060092440771
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 23167365.845977396, tolerance: 4672.060092440771
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 23118099.42640926, tolerance: 4672.060092440771
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 23056651.953954533, tolerance: 4672.060092440771
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22980237.206046075, tolerance: 4672.060092440771
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22885556.707329776, tolerance: 4672.060092440771
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22768774.453563232, tolerance: 4672.060092440771
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22625532.16143886, tolerance: 4672.060092440771
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22451029.676813804, tolerance: 4672.060092440771
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22240200.32477626, tolerance: 4672.060092440771
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21988011.931526598, tolerance: 4672.060092440771
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21689916.30885172, tolerance: 4672.060092440771
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21342447.68596508, tolerance: 4672.060092440771
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20943930.15921538, tolerance: 4672.060092440771
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20495198.28909821, tolerance: 4672.060092440771
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20000177.559322283, tolerance: 4672.060092440771
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 19466139.44222783, tolerance: 4672.060092440771
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18903470.89259221, tolerance: 4672.060092440771
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18324897.081966624, tolerance: 4672.060092440771
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 17744250.00734387, tolerance: 4672.060092440771
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 17175025.58676852, tolerance: 4672.060092440771
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16629046.52619614, tolerance: 4672.060092440771
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16115507.893058583, tolerance: 4672.060092440771
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15640546.906808402, tolerance: 4672.060092440771
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15207314.486560233, tolerance: 4672.060092440771
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14816403.882194025, tolerance: 4672.060092440771
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14466447.482806414, tolerance: 4672.060092440771
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14154719.856758073, tolerance: 4672.060092440771
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13877649.969955917, tolerance: 4672.060092440771
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13631212.11296679, tolerance: 4672.060092440771
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13411210.001552952, tolerance: 4672.060092440771
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13213485.727373026, tolerance: 4672.060092440771
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13034081.699863115, tolerance: 4672.060092440771
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12869371.50365531, tolerance: 4672.060092440771
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12716164.663350802, tolerance: 4672.060092440771
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12571784.937590647, tolerance: 4672.060092440771
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12434121.027959855, tolerance: 4672.060092440771
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12301649.315310962, tolerance: 4672.060092440771
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12173428.130794892, tolerance: 4672.060092440771
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12049062.15666455, tolerance: 4672.060092440771
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11928635.51722824, tolerance: 4672.060092440771
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11812614.527894203, tolerance: 4672.060092440771
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11701725.961154336, tolerance: 4672.060092440771
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11596822.384790171, tolerance: 4672.060092440771
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11498750.134131065, tolerance: 4672.060092440771
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11408235.853997387, tolerance: 4672.060092440771
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11325803.93120049, tolerance: 4672.060092440771
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11251730.828344157, tolerance: 4672.060092440771
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11186035.428497167, tolerance: 4672.060092440771
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11128498.926243482, tolerance: 4672.060092440771
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11078704.704544256, tolerance: 4672.060092440771
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11036088.22737443, tolerance: 4672.060092440771
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10999988.612386206, tolerance: 4672.060092440771
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10969696.114698745, tolerance: 4672.060092440771
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10944492.241221206, tolerance: 4672.060092440771
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10923681.106938187, tolerance: 4672.060092440771
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10906611.907129792, tolerance: 4672.060092440771
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10892693.190993745, tolerance: 4672.060092440771
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10881400.131175667, tolerance: 4672.060092440771
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10872276.253023634, tolerance: 4672.060092440771
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10864931.143496089, tolerance: 4672.060092440771
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10859035.540626436, tolerance: 4672.060092440771
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10854314.971122248, tolerance: 4672.060092440771
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10850542.823952977, tolerance: 4672.060092440771
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10847533.476348778, tolerance: 4672.060092440771
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10845135.858255884, tolerance: 4672.060092440771
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10843227.66422545, tolerance: 4672.060092440771
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10841710.296569405, tolerance: 4672.060092440771
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10840504.542116864, tolerance: 4672.060092440771
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10839546.93662817, tolerance: 4672.060092440771
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10838786.746149871, tolerance: 4672.060092440771
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10838183.48531509, tolerance: 4672.060092440771
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10837704.892811378, tolerance: 4672.060092440771
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22021807.255443227, tolerance: 4404.419138231837
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22021731.729488757, tolerance: 4404.419138231837
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22021636.42827446, tolerance: 4404.419138231837
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22021516.17453771, tolerance: 4404.419138231837
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22021364.43592119, tolerance: 4404.419138231837
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22021172.970491543, tolerance: 4404.419138231837
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22020931.379647505, tolerance: 4404.419138231837
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22020626.544292755, tolerance: 4404.419138231837
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22020241.913906105, tolerance: 4404.419138231837
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22019756.6103082, tolerance: 4404.419138231837
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22019144.298109498, tolerance: 4404.419138231837
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22018371.76155092, tolerance: 4404.419138231837
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22017397.112138756, tolerance: 4404.419138231837
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22016167.532437038, tolerance: 4404.419138231837
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22014616.437802956, tolerance: 4404.419138231837
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22012659.90880659, tolerance: 4404.419138231837
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22010192.211545315, tolerance: 4404.419138231837
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22007080.179999877, tolerance: 4404.419138231837
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22003156.183034074, tolerance: 4404.419138231837
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21998209.338000633, tolerance: 4404.419138231837
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21991974.56331279, tolerance: 4404.419138231837
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21984118.98533017, tolerance: 4404.419138231837
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21974225.134579882, tolerance: 4404.419138231837
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21961770.291046746, tolerance: 4404.419138231837
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21946101.28330544, tolerance: 4404.419138231837
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21926404.037766844, tolerance: 4404.419138231837
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21901667.254819766, tolerance: 4404.419138231837
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21870639.824578017, tolerance: 4404.419138231837
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21831782.085636448, tolerance: 4404.419138231837
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21783211.91724537, tolerance: 4404.419138231837
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21722648.12785825, tolerance: 4404.419138231837
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21647355.891083278, tolerance: 4404.419138231837
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21554102.31889948, tolerance: 4404.419138231837
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21439134.801442485, tolerance: 4404.419138231837
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21298200.365636967, tolerance: 4404.419138231837
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21126630.321608968, tolerance: 4404.419138231837
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20919519.18944641, tolerance: 4404.419138231837
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20672027.223884203, tolerance: 4404.419138231837
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20379827.179002505, tolerance: 4404.419138231837
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20039693.079171706, tolerance: 4404.419138231837
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 19650188.506881192, tolerance: 4404.419138231837
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 19212357.62287572, tolerance: 4404.419138231837
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18730268.372360863, tolerance: 4404.419138231837
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18211230.494756356, tolerance: 4404.419138231837
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 17665540.38528687, tolerance: 4404.419138231837
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 17105703.884344697, tolerance: 4404.419138231837
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16545235.1176295, tolerance: 4404.419138231837
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15997267.444157388, tolerance: 4404.419138231837
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15473276.145049509, tolerance: 4404.419138231837
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14982169.18339979, tolerance: 4404.419138231837
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14529874.632212078, tolerance: 4404.419138231837
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14119403.174914066, tolerance: 4404.419138231837
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13751253.4378757, tolerance: 4404.419138231837
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13423986.905185537, tolerance: 4404.419138231837
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13134820.569312058, tolerance: 4404.419138231837
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12880140.610058099, tolerance: 4404.419138231837
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12655899.251463126, tolerance: 4404.419138231837
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12457900.393142547, tolerance: 4404.419138231837
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12282001.646348769, tolerance: 4404.419138231837
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12124264.23864129, tolerance: 4404.419138231837
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11981074.862099757, tolerance: 4404.419138231837
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11849251.614065427, tolerance: 4404.419138231837
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11726134.404125093, tolerance: 4404.419138231837
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11609651.41163489, tolerance: 4404.419138231837
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11498349.137896976, tolerance: 4404.419138231837
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11391375.282655219, tolerance: 4404.419138231837
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11288410.749135757, tolerance: 4404.419138231837
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11189557.309981026, tolerance: 4404.419138231837
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11095197.172379192, tolerance: 4404.419138231837
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11005846.396467881, tolerance: 4404.419138231837
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10922024.204901973, tolerance: 4404.419138231837
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10844155.171114372, tolerance: 4404.419138231837
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10772512.877874088, tolerance: 4404.419138231837
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10707204.11794719, tolerance: 4404.419138231837
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10648184.400712928, tolerance: 4404.419138231837
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10595290.607132455, tolerance: 4404.419138231837
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10548276.416932933, tolerance: 4404.419138231837
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10506840.294972528, tolerance: 4404.419138231837
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10470642.298260268, tolerance: 4404.419138231837
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10439311.845972273, tolerance: 4404.419138231837
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10412451.637911418, tolerance: 4404.419138231837
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10389642.61847619, tolerance: 4404.419138231837
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10370452.45556138, tolerance: 4404.419138231837
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10354447.26983874, tolerance: 4404.419138231837
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10341204.639331182, tolerance: 4404.419138231837
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10330325.55858644, tolerance: 4404.419138231837
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10321443.651717404, tolerance: 4404.419138231837
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10314230.902284523, tolerance: 4404.419138231837
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10308400.00142859, tolerance: 4404.419138231837
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10303703.928348558, tolerance: 4404.419138231837
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10299933.56924538, tolerance: 4404.419138231837
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10296914.150918042, tolerance: 4404.419138231837
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10294501.121979909, tolerance: 4404.419138231837
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10292575.938928057, tolerance: 4404.419138231837
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10291042.052342515, tolerance: 4404.419138231837
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10289821.25979676, tolerance: 4404.419138231837
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10288850.499799212, tolerance: 4404.419138231837
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10288079.100527694, tolerance: 4404.419138231837
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10287466.460910317, tolerance: 4404.419138231837
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10286980.122580368, tolerance: 4404.419138231837
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 23969362.935222156, tolerance: 4793.933906024956
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 23969282.654424485, tolerance: 4793.933906024956
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 23969181.353435393, tolerance: 4793.933906024956
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 23969053.52909124, tolerance: 4793.933906024956
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 23968892.237849258, tolerance: 4793.933906024956
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 23968688.71900646, tolerance: 4793.933906024956
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 23968431.919487976, tolerance: 4793.933906024956
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 23968107.894567393, tolerance: 4793.933906024956
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 23967699.052249804, tolerance: 4793.933906024956
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 23967183.200727485, tolerance: 4793.933906024956
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 23966532.34789499, tolerance: 4793.933906024956
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 23965711.188879542, tolerance: 4793.933906024956
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 23964675.201291952, tolerance: 4793.933906024956
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 23963368.247704044, tolerance: 4793.933906024956
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 23961719.559855826, tolerance: 4793.933906024956
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 23959639.94831616, tolerance: 4793.933906024956
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 23957017.043702308, tolerance: 4793.933906024956
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 23953709.330023296, tolerance: 4793.933906024956
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 23949538.676292956, tolerance: 4793.933906024956
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 23944281.00868393, tolerance: 4793.933906024956
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 23937654.69242269, tolerance: 4793.933906024956
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 23929306.112193406, tolerance: 4793.933906024956
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 23918791.856649436, tolerance: 4793.933906024956
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 23905556.83602224, tolerance: 4793.933906024956
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 23888907.608620822, tolerance: 4793.933906024956
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 23867980.190871228, tolerance: 4793.933906024956
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 23841701.722861085, tolerance: 4793.933906024956
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 23808745.629229248, tolerance: 4793.933906024956
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 23767480.46100299, tolerance: 4793.933906024956
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 23715913.579622705, tolerance: 4793.933906024956
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 23651632.451356784, tolerance: 4793.933906024956
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 23571748.80001198, tolerance: 4793.933906024956
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 23472854.45797503, tolerance: 4793.933906024956
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 23351002.595441118, tolerance: 4793.933906024956
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 23201733.924276076, tolerance: 4793.933906024956
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 23020173.65561262, tolerance: 4793.933906024956
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22801229.537349567, tolerance: 4793.933906024956
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22539920.79277737, tolerance: 4793.933906024956
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22231857.286452442, tolerance: 4793.933906024956
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21873862.39892775, tolerance: 4793.933906024956
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21464689.09156748, tolerance: 4793.933906024956
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21005721.117572185, tolerance: 4793.933906024956
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20501497.11737298, tolerance: 4793.933906024956
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 19959873.24214556, tolerance: 4793.933906024956
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 19391680.093358003, tolerance: 4793.933906024956
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18809842.669925276, tolerance: 4793.933906024956
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18228089.63369193, tolerance: 4793.933906024956
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 17659516.47147378, tolerance: 4793.933906024956
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 17115318.431894735, tolerance: 4793.933906024956
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16603945.861676544, tolerance: 4793.933906024956
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16130788.845743028, tolerance: 4793.933906024956
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15698339.955700424, tolerance: 4793.933906024956
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15306677.321152397, tolerance: 4793.933906024956
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14954081.113639068, tolerance: 4793.933906024956
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14637631.360441223, tolerance: 4793.933906024956
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14353700.338976268, tolerance: 4793.933906024956
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14098316.53498585, tolerance: 4793.933906024956
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13867420.001759825, tolerance: 4793.933906024956
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13657045.940844085, tolerance: 4793.933906024956
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13463469.711476438, tolerance: 4793.933906024956
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13283332.150628842, tolerance: 4793.933906024956
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13113748.467538731, tolerance: 4793.933906024956
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12952393.010714943, tolerance: 4793.933906024956
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12797548.002884515, tolerance: 4793.933906024956
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12648106.527975334, tolerance: 4793.933906024956
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12503527.014654484, tolerance: 4793.933906024956
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12363745.571023857, tolerance: 4793.933906024956
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12229060.31977495, tolerance: 4793.933906024956
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12100005.325759532, tolerance: 4793.933906024956
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11977230.012443915, tolerance: 4793.933906024956
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11861395.042893251, tolerance: 4793.933906024956
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11753090.441656087, tolerance: 4793.933906024956
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11652778.032778341, tolerance: 4793.933906024956
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11560757.935574878, tolerance: 4793.933906024956
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11477156.859927, tolerance: 4793.933906024956
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11401933.787235692, tolerance: 4793.933906024956
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11334896.943716813, tolerance: 4793.933906024956
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11275725.872363407, tolerance: 4793.933906024956
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11223994.251256235, tolerance: 4793.933906024956
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11179191.912904287, tolerance: 4793.933906024956
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11140746.633266253, tolerance: 4793.933906024956
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11108046.643612491, tolerance: 4793.933906024956
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11080463.759770539, tolerance: 4793.933906024956
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11057375.683530247, tolerance: 4793.933906024956
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11038185.430002041, tolerance: 4793.933906024956
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11022336.229300449, tolerance: 4793.933906024956
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11009321.241906976, tolerance: 4793.933906024956
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10998688.419250423, tolerance: 4793.933906024956
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10990041.450273681, tolerance: 4793.933906024956
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10983037.908753928, tolerance: 4793.933906024956
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10977385.603949329, tolerance: 4793.933906024956
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10972837.915374227, tolerance: 4793.933906024956
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10969188.673915684, tolerance: 4793.933906024956
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10966266.978196425, tolerance: 4793.933906024956
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10963932.20745184, tolerance: 4793.933906024956
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10962069.397958165, tolerance: 4793.933906024956
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10960585.078665143, tolerance: 4793.933906024956
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10959403.607474027, tolerance: 4793.933906024956
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10958464.009792931, tolerance: 4793.933906024956
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10957717.293908352, tolerance: 4793.933906024956
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 23718152.552540563, tolerance: 4743.698787426185
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 23718063.16236956, tolerance: 4743.698787426185
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 23717950.366978314, tolerance: 4743.698787426185
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 23717808.03885842, tolerance: 4743.698787426185
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 23717628.446746748, tolerance: 4743.698787426185
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 23717401.83613046, tolerance: 4743.698787426185
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 23717115.90017622, tolerance: 4743.698787426185
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 23716755.112551343, tolerance: 4743.698787426185
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 23716299.886228412, tolerance: 4743.698787426185
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 23715725.513113357, tolerance: 4743.698787426185
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 23715000.827752545, tolerance: 4743.698787426185
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 23714086.52389826, tolerance: 4743.698787426185
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 23712933.034671307, tolerance: 4743.698787426185
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 23711477.864653155, tolerance: 4743.698787426185
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 23709642.234535035, tolerance: 4743.698787426185
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 23707326.86489392, tolerance: 4743.698787426185
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 23704406.684116423, tolerance: 4743.698787426185
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 23700724.195319675, tolerance: 4743.698787426185
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 23696081.177368, tolerance: 4743.698787426185
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 23690228.325290486, tolerance: 4743.698787426185
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 23682852.356123734, tolerance: 4743.698787426185
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 23673560.01989752, tolerance: 4743.698787426185
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 23661858.36792415, tolerance: 4743.698787426185
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 23647130.553039856, tolerance: 4743.698787426185
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 23628606.389119867, tolerance: 4743.698787426185
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 23605326.913904868, tolerance: 4743.698787426185
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 23576102.33429781, tolerance: 4743.698787426185
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 23539463.070559546, tolerance: 4743.698787426185
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 23493604.278750658, tolerance: 4743.698787426185
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 23436325.392207418, tolerance: 4743.698787426185
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 23364968.108188108, tolerance: 4743.698787426185
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 23276359.11785145, tolerance: 4743.698787426185
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 23166767.981876273, tolerance: 4743.698787426185
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 23031895.99093769, tolerance: 4743.698787426185
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22866918.33391181, tolerance: 4743.698787426185
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22666608.36817078, tolerance: 4743.698787426185
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22425576.917205125, tolerance: 4743.698787426185
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22138657.302723926, tolerance: 4743.698787426185
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21801452.72003801, tolerance: 4743.698787426185
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21411031.047958862, tolerance: 4743.698787426185
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20966701.3361118, tolerance: 4743.698787426185
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20470743.23458558, tolerance: 4743.698787426185
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 19928906.408142142, tolerance: 4743.698787426185
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 19350484.05728986, tolerance: 4743.698787426185
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18747823.294264685, tolerance: 4743.698787426185
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18135269.732139282, tolerance: 4743.698787426185
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 17527715.3300169, tolerance: 4743.698787426185
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16939054.08079045, tolerance: 4743.698787426185
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16380881.783788582, tolerance: 4743.698787426185
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15861686.458036942, tolerance: 4743.698787426185
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15386610.56035734, tolerance: 4743.698787426185
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14957703.062636636, tolerance: 4743.698787426185
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14574481.04156234, tolerance: 4743.698787426185
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14234605.113814974, tolerance: 4743.698787426185
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13934519.384265509, tolerance: 4743.698787426185
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13669976.708612256, tolerance: 4743.698787426185
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13436431.795330161, tolerance: 4743.698787426185
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13229321.807068529, tolerance: 4743.698787426185
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13044266.002226219, tolerance: 4743.698787426185
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12877211.197272088, tolerance: 4743.698787426185
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12724539.087189114, tolerance: 4743.698787426185
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12583142.24119436, tolerance: 4743.698787426185
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12450470.513354821, tolerance: 4743.698787426185
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12324547.80210198, tolerance: 4743.698787426185
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12203958.897479549, tolerance: 4743.698787426185
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12087806.9284921, tolerance: 4743.698787426185
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11975643.92871233, tolerance: 4743.698787426185
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11867380.233022789, tolerance: 4743.698787426185
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11763181.815673837, tolerance: 4743.698787426185
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11663366.929977568, tolerance: 4743.698787426185
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11568313.662399476, tolerance: 4743.698787426185
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11478388.0500719, tolerance: 4743.698787426185
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11393898.32223807, tolerance: 4743.698787426185
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11315075.103664974, tolerance: 4743.698787426185
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11242071.420932187, tolerance: 4743.698787426185
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11174972.258744128, tolerance: 4743.698787426185
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11113803.18383054, tolerance: 4743.698787426185
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11058531.525704803, tolerance: 4743.698787426185
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11009059.8711239, tolerance: 4743.698787426185
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10965216.941782635, tolerance: 4743.698787426185
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10926752.677026037, tolerance: 4743.698787426185
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10893342.196969915, tolerance: 4743.698787426185
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10864599.173972998, tolerance: 4743.698787426185
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10840095.572417587, tolerance: 4743.698787426185
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10819383.284681633, tolerance: 4743.698787426185
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10802013.859138891, tolerance: 4743.698787426185
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10787554.222508082, tolerance: 4743.698787426185
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10775597.908142125, tolerance: 4743.698787426185
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10765772.248861115, tolerance: 4743.698787426185
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10757742.30601608, tolerance: 4743.698787426185
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10751212.272803359, tolerance: 4743.698787426185
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10745924.962948397, tolerance: 4743.698787426185
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10741659.893372206, tolerance: 4743.698787426185
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10738230.404375711, tolerance: 4743.698787426185
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10735480.207996719, tolerance: 4743.698787426185
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10733279.694537025, tolerance: 4743.698787426185
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10731522.25533614, tolerance: 4743.698787426185
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10730120.804287568, tolerance: 4743.698787426185
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10729004.610913701, tolerance: 4743.698787426185
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10728116.500716342, tolerance: 4743.698787426185
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 23993190.216468286, tolerance: 4798.705308680193
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 23993102.1504539, tolerance: 4798.705308680193
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 23992991.02582505, tolerance: 4798.705308680193
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 23992850.805755217, tolerance: 4798.705308680193
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 23992673.87336195, tolerance: 4798.705308680193
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 23992450.618389774, tolerance: 4798.705308680193
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 23992168.915918656, tolerance: 4798.705308680193
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 23991813.468975212, tolerance: 4798.705308680193
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 23991364.979649443, tolerance: 4798.705308680193
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 23990799.104192726, tolerance: 4798.705308680193
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 23990085.136140276, tolerance: 4798.705308680193
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 23989184.347208634, tolerance: 4798.705308680193
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 23988047.89789546, tolerance: 4798.705308680193
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 23986614.20755563, tolerance: 4798.705308680193
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 23984805.646308575, tolerance: 4798.705308680193
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 23982524.37738004, tolerance: 4798.705308680193
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 23979647.137235105, tolerance: 4798.705308680193
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 23976018.69093477, tolerance: 4798.705308680193
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 23971443.640502226, tolerance: 4798.705308680193
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 23965676.1940963, tolerance: 4798.705308680193
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 23958407.423757527, tolerance: 4798.705308680193
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 23949249.451457817, tolerance: 4798.705308680193
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 23937715.912259247, tolerance: 4798.705308680193
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 23923197.95983087, tolerance: 4798.705308680193
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 23904935.02196265, tolerance: 4798.705308680193
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 23881979.51360767, tolerance: 4798.705308680193
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 23853154.823481794, tolerance: 4798.705308680193
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 23817006.186726194, tolerance: 4798.705308680193
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 23771744.658706415, tolerance: 4798.705308680193
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 23715185.48088642, tolerance: 4798.705308680193
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 23644683.90040059, tolerance: 4798.705308680193
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 23557074.23575185, tolerance: 4798.705308680193
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 23448621.93504027, tolerance: 4798.705308680193
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 23315003.697874893, tolerance: 4798.705308680193
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 23151337.23756504, tolerance: 4798.705308680193
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22952289.051368214, tolerance: 4798.705308680193
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22712293.54747018, tolerance: 4798.705308680193
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22425916.285525773, tolerance: 4798.705308680193
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22088382.488468938, tolerance: 4798.705308680193
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21696263.48387246, tolerance: 4798.705308680193
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21248265.25504506, tolerance: 4798.705308680193
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20745999.93222313, tolerance: 4798.705308680193
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20194561.35583904, tolerance: 4798.705308680193
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 19602701.49373528, tolerance: 4798.705308680193
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18982448.629555777, tolerance: 4798.705308680193
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18348132.51432006, tolerance: 4798.705308680193
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 17714955.430162724, tolerance: 4798.705308680193
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 17097401.077900045, tolerance: 4798.705308680193
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16507830.897710452, tolerance: 4798.705308680193
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15955548.88507345, tolerance: 4798.705308680193
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15446455.519046593, tolerance: 4798.705308680193
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14983235.707205497, tolerance: 4798.705308680193
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14565905.675969776, tolerance: 4798.705308680193
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14192509.678203955, tolerance: 4798.705308680193
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13859795.57539492, tolerance: 4798.705308680193
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13563771.537402073, tolerance: 4798.705308680193
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13300117.212939696, tolerance: 4798.705308680193
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13064469.278040543, tolerance: 4798.705308680193
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12852618.485491626, tolerance: 4798.705308680193
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12660651.282020504, tolerance: 4798.705308680193
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12485056.352231005, tolerance: 4798.705308680193
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12322804.87923094, tolerance: 4798.705308680193
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12171406.535696577, tolerance: 4798.705308680193
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12028940.06936073, tolerance: 4798.705308680193
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11894055.536185594, tolerance: 4798.705308680193
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11765944.205156298, tolerance: 4798.705308680193
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11644273.223375844, tolerance: 4798.705308680193
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11529086.518683098, tolerance: 4798.705308680193
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11420680.413614385, tolerance: 4798.705308680193
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11319469.289824126, tolerance: 4798.705308680193
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11225860.18794495, tolerance: 4798.705308680193
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11140153.629796524, tolerance: 4798.705308680193
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11062481.60716067, tolerance: 4798.705308680193
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10992784.88529433, tolerance: 4798.705308680193
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10930823.560247108, tolerance: 4798.705308680193
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10876209.63567846, tolerance: 4798.705308680193
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10828449.220638089, tolerance: 4798.705308680193
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10786984.208368395, tolerance: 4798.705308680193
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10751227.376288569, tolerance: 4798.705308680193
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10720588.918586543, tolerance: 4798.705308680193
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10694495.160903752, tolerance: 4798.705308680193
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10672401.235243415, tolerance: 4798.705308680193
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10653799.30562046, tolerance: 4798.705308680193
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10638223.366208568, tolerance: 4798.705308680193
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10625251.283595253, tolerance: 4798.705308680193
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10614504.71874555, tolerance: 4798.705308680193
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10605647.618410952, tolerance: 4798.705308680193
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10598383.90997855, tolerance: 4798.705308680193
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10592454.845016185, tolerance: 4798.705308680193
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10587636.212194255, tolerance: 4798.705308680193
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10583735.479562921, tolerance: 4798.705308680193
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10580588.864085961, tolerance: 4798.705308680193
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10578058.337226743, tolerance: 4798.705308680193
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10576028.612941455, tolerance: 4798.705308680193
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10574404.193668064, tolerance: 4798.705308680193
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10573106.557184074, tolerance: 4798.705308680193
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10572071.55499962, tolerance: 4798.705308680193
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10571247.070095135, tolerance: 4798.705308680193
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10570590.956919884, tolerance: 4798.705308680193
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10570069.265142197, tolerance: 4798.705308680193
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 25060160.14100164, tolerance: 5012.101307256241
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 25060069.4387852, tolerance: 5012.101307256241
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 25059954.987802915, tolerance: 5012.101307256241
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 25059810.570599925, tolerance: 5012.101307256241
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 25059628.34242146, tolerance: 5012.101307256241
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 25059398.405557044, tolerance: 5012.101307256241
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 25059108.272498444, tolerance: 5012.101307256241
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 25058742.188958976, tolerance: 5012.101307256241
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 25058280.280316647, tolerance: 5012.101307256241
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 25057697.475654986, tolerance: 5012.101307256241
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 25056962.151819006, tolerance: 5012.101307256241
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 25056034.42521205, tolerance: 5012.101307256241
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 25054864.00074905, tolerance: 5012.101307256241
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 25053387.46463828, tolerance: 5012.101307256241
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 25051524.87954047, tolerance: 5012.101307256241
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 25049175.50607634, tolerance: 5012.101307256241
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 25046212.432464346, tolerance: 5012.101307256241
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 25042475.843112443, tolerance: 5012.101307256241
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 25037764.59628269, tolerance: 5012.101307256241
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 25031825.710006386, tolerance: 5012.101307256241
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 25024341.274789188, tolerance: 5012.101307256241
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 25014912.223768357, tolerance: 5012.101307256241
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 25003038.301666614, tolerance: 5012.101307256241
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 24988093.494496554, tolerance: 5012.101307256241
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 24969296.132803254, tolerance: 5012.101307256241
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 24945672.89643695, tolerance: 5012.101307256241
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 24916016.08328385, tolerance: 5012.101307256241
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 24878833.842606038, tolerance: 5012.101307256241
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 24832293.740410846, tolerance: 5012.101307256241
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 24774161.19431865, tolerance: 5012.101307256241
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 24701736.21671112, tolerance: 5012.101307256241
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 24611794.803662773, tolerance: 5012.101307256241
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 24500545.452634897, tolerance: 5012.101307256241
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 24363616.788398754, tolerance: 5012.101307256241
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 24196098.83965655, tolerance: 5012.101307256241
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 23992667.075517353, tolerance: 5012.101307256241
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 23747822.538549077, tolerance: 5012.101307256241
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 23456279.260323606, tolerance: 5012.101307256241
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 23113516.032943502, tolerance: 5012.101307256241
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22716477.926429573, tolerance: 5012.101307256241
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22264361.70345448, tolerance: 5012.101307256241
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21759355.655783582, tolerance: 5012.101307256241
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21207149.316009674, tolerance: 5012.101307256241
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20617014.746538576, tolerance: 5012.101307256241
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20001319.311039746, tolerance: 5012.101307256241
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 19374464.928279184, tolerance: 5012.101307256241
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18751422.263323694, tolerance: 5012.101307256241
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18146166.802996222, tolerance: 5012.101307256241
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 17570358.61802567, tolerance: 5012.101307256241
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 17032519.711057153, tolerance: 5012.101307256241
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16537796.20459885, tolerance: 5012.101307256241
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16088224.89170718, tolerance: 5012.101307256241
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15683319.013026882, tolerance: 5012.101307256241
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15320768.10409543, tolerance: 5012.101307256241
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14997092.73037363, tolerance: 5012.101307256241
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14708169.493395785, tolerance: 5012.101307256241
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14449610.319768617, tolerance: 5012.101307256241
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14217022.919547275, tolerance: 5012.101307256241
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14006193.099225435, tolerance: 5012.101307256241
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13813222.696748316, tolerance: 5012.101307256241
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13634641.705916973, tolerance: 5012.101307256241
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13467499.43451291, tolerance: 5012.101307256241
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13309431.3128608, tolerance: 5012.101307256241
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13158694.686916083, tolerance: 5012.101307256241
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13014166.787185304, tolerance: 5012.101307256241
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12875300.352634186, tolerance: 5012.101307256241
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12742037.288628515, tolerance: 5012.101307256241
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12614687.673311125, tolerance: 5012.101307256241
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12493788.32245413, tolerance: 5012.101307256241
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12379959.460104084, tolerance: 5012.101307256241
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12273778.362493103, tolerance: 5012.101307256241
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12175685.150433403, tolerance: 5012.101307256241
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12085929.124585677, tolerance: 5012.101307256241
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12004555.620476471, tolerance: 5012.101307256241
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11931425.302362565, tolerance: 5012.101307256241
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11866252.407024788, tolerance: 5012.101307256241
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11808647.402525872, tolerance: 5012.101307256241
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11758152.983881485, tolerance: 5012.101307256241
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11714268.541919895, tolerance: 5012.101307256241
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11676464.352791257, tolerance: 5012.101307256241
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11644190.280789498, tolerance: 5012.101307256241
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11616883.967791997, tolerance: 5012.101307256241
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11593981.298538677, tolerance: 5012.101307256241
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11574929.218921289, tolerance: 5012.101307256241
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11559199.182616182, tolerance: 5012.101307256241
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11546299.053453077, tolerance: 5012.101307256241
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11535781.854612717, tolerance: 5012.101307256241
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11527250.710176667, tolerance: 5012.101307256241
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11520360.17232067, tolerance: 5012.101307256241
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11514814.644780107, tolerance: 5012.101307256241
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11510364.79780179, tolerance: 5012.101307256241
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11506802.822704159, tolerance: 5012.101307256241
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11503957.209841758, tolerance: 5012.101307256241
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11501687.537798516, tolerance: 5012.101307256241
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11499879.583047425, tolerance: 5012.101307256241
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11498440.918481238, tolerance: 5012.101307256241
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11497297.069032585, tolerance: 5012.101307256241
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11496388.227513082, tolerance: 5012.101307256241
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11495666.495538749, tolerance: 5012.101307256241
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11495093.595021984, tolerance: 5012.101307256241
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 24241749.016041555, tolerance: 4848.416545138158
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 24241661.635391492, tolerance: 4848.416545138158
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 24241551.375600625, tolerance: 4848.416545138158
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 24241412.246842574, tolerance: 4848.416545138158
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 24241236.69154455, tolerance: 4848.416545138158
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 24241015.17429489, tolerance: 4848.416545138158
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 24240735.664621312, tolerance: 4848.416545138158
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 24240382.984738883, tolerance: 4848.416545138158
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 24239937.9871505, tolerance: 4848.416545138158
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 24239376.517929558, tolerance: 4848.416545138158
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 24238668.110175204, tolerance: 4848.416545138158
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 24237774.33795617, tolerance: 4848.416545138158
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 24236646.743385393, tolerance: 4848.416545138158
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 24235224.227504663, tolerance: 4848.416545138158
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 24233429.768480003, tolerance: 4848.416545138158
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 24231166.297163926, tolerance: 4848.416545138158
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 24228311.519224122, tolerance: 4848.416545138158
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 24224711.42361211, tolerance: 4848.416545138158
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 24220172.158135463, tolerance: 4848.416545138158
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 24214449.88372392, tolerance: 4848.416545138158
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 24207238.139993068, tolerance: 4848.416545138158
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 24198152.168017723, tolerance: 4848.416545138158
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 24186709.54702459, tolerance: 4848.416545138158
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 24172306.420363653, tolerance: 4848.416545138158
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 24154188.531312905, tolerance: 4848.416545138158
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 24131416.29268112, tolerance: 4848.416545138158
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 24102823.226972908, tolerance: 4848.416545138158
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 24066967.41537096, tolerance: 4848.416545138158
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 24022076.201683275, tolerance: 4848.416545138158
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 23965985.477786575, tolerance: 4848.416545138158
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 23896076.648992844, tolerance: 4848.416545138158
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 23809217.101452745, tolerance: 4848.416545138158
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 23701713.923008207, tolerance: 4848.416545138158
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 23569295.8958591, tolerance: 4848.416545138158
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 23407145.17042295, tolerance: 4848.416545138158
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 23210006.619784985, tolerance: 4848.416545138158
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22972407.540192883, tolerance: 4848.416545138158
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22689019.34526101, tolerance: 4848.416545138158
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22355180.847954225, tolerance: 4848.416545138158
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21967573.972641073, tolerance: 4848.416545138158
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21524994.631253265, tolerance: 4848.416545138158
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21029099.66247564, tolerance: 4848.416545138158
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20484953.983405787, tolerance: 4848.416545138158
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 19901181.621193204, tolerance: 4848.416545138158
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 19289571.4621536, tolerance: 4848.416545138158
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18664112.451007403, tolerance: 4848.416545138158
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18039601.18218479, tolerance: 4848.416545138158
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 17430108.189934365, tolerance: 4848.416545138158
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16847637.70380956, tolerance: 4848.416545138158
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16301243.4899397, tolerance: 4848.416545138158
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15796707.388921894, tolerance: 4848.416545138158
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15336721.596804978, tolerance: 4848.416545138158
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14921406.267428795, tolerance: 4848.416545138158
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14548966.01851347, tolerance: 4848.416545138158
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14216327.062564816, tolerance: 4848.416545138158
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13919665.353069827, tolerance: 4848.416545138158
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13654801.475299358, tolerance: 4848.416545138158
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13417480.241023816, tolerance: 4848.416545138158
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13203568.321865387, tolerance: 4848.416545138158
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13009199.401368106, tolerance: 4848.416545138158
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12830884.885159975, tolerance: 4848.416545138158
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12665598.204476709, tolerance: 4848.416545138158
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12510835.370489676, tolerance: 4848.416545138158
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12364652.125465607, tolerance: 4848.416545138158
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12225676.134705573, tolerance: 4848.416545138158
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12093090.480046617, tolerance: 4848.416545138158
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11966583.857462365, tolerance: 4848.416545138158
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11846265.292348985, tolerance: 4848.416545138158
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11732547.120654093, tolerance: 4848.416545138158
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11626007.571183149, tolerance: 4848.416545138158
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11527250.538526248, tolerance: 4848.416545138158
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11436782.351748317, tolerance: 4848.416545138158
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11354922.227879947, tolerance: 4848.416545138158
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11281755.407033287, tolerance: 4848.416545138158
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11217128.309148837, tolerance: 4848.416545138158
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11160676.744737044, tolerance: 4848.416545138158
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11111873.741066093, tolerance: 4848.416545138158
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11070083.581077809, tolerance: 4848.416545138158
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11034612.061561838, tolerance: 4848.416545138158
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11004747.664072685, tolerance: 4848.416545138158
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10979792.356167257, tolerance: 4848.416545138158
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10959083.107134497, tolerance: 4848.416545138158
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10942005.973854408, tolerance: 4848.416545138158
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10928004.453095328, tolerance: 4848.416545138158
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10916583.384855049, tolerance: 4848.416545138158
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10907309.387686582, tolerance: 4848.416545138158
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10899808.664953867, tolerance: 4848.416545138158
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10893762.95630223, tolerance: 4848.416545138158
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10888904.337944634, tolerance: 4848.416545138158
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10885009.467719551, tolerance: 4848.416545138158
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10881893.736927697, tolerance: 4848.416545138158
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10879405.654536711, tolerance: 4848.416545138158
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10877421.66902858, tolerance: 4848.416545138158
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10875841.537448753, tolerance: 4848.416545138158
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10874584.281055834, tolerance: 4848.416545138158
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10873584.719252251, tolerance: 4848.416545138158
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10872790.543822171, tolerance: 4848.416545138158
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10872159.879371013, tolerance: 4848.416545138158
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10871659.269314919, tolerance: 4848.416545138158
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10871262.026546085, tolerance: 4848.416545138158
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 24676723.629267626, tolerance: 4935.415058300707
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 24676631.547972377, tolerance: 4935.415058300707
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 24676515.356855128, tolerance: 4935.415058300707
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 24676368.74395455, tolerance: 4935.415058300707
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 24676183.745285068, tolerance: 4935.415058300707
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 24675950.312720336, tolerance: 4935.415058300707
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 24675655.769006718, tolerance: 4935.415058300707
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 24675284.120518126, tolerance: 4935.415058300707
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 24674815.190767128, tolerance: 4935.415058300707
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 24674223.52815971, tolerance: 4935.415058300707
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 24673477.029551797, tolerance: 4935.415058300707
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 24672535.20626036, tolerance: 4935.415058300707
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 24671347.00060841, tolerance: 4935.415058300707
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 24669848.0380184, tolerance: 4935.415058300707
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 24667957.171155907, tolerance: 4935.415058300707
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 24665572.137582924, tolerance: 4935.415058300707
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 24662564.109645408, tolerance: 4935.415058300707
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 24658770.863738805, tolerance: 4935.415058300707
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 24653988.23470595, tolerance: 4935.415058300707
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 24647959.449476432, tolerance: 4935.415058300707
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 24640361.852777734, tolerance: 4935.415058300707
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 24630790.449444044, tolerance: 4935.415058300707
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 24618737.598577287, tolerance: 4935.415058300707
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 24603568.116365544, tolerance: 4935.415058300707
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 24584488.997700956, tolerance: 4935.415058300707
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 24560512.987017136, tolerance: 4935.415058300707
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 24530415.372316275, tolerance: 4935.415058300707
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 24492683.72974999, tolerance: 4935.415058300707
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 24445461.036681805, tolerance: 4935.415058300707
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 24386483.77646313, tolerance: 4935.415058300707
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 24313018.60845527, tolerance: 4935.415058300707
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 24221804.13807195, tolerance: 4935.415058300707
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 24109008.53685584, tolerance: 4935.415058300707
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 23970219.3178026, tolerance: 4935.415058300707
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 23800488.143608585, tolerance: 4935.415058300707
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 23594460.0072916, tolerance: 4935.415058300707
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 23346620.042874303, tolerance: 4935.415058300707
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 23051688.471153036, tolerance: 4935.415058300707
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22705179.164396733, tolerance: 4935.415058300707
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22304104.597895432, tolerance: 4935.415058300707
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21847758.072691828, tolerance: 4935.415058300707
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21338441.03930815, tolerance: 4935.415058300707
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20781950.866087157, tolerance: 4935.415058300707
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20187635.216030374, tolerance: 4935.415058300707
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 19567882.307517804, tolerance: 4935.415058300707
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18937053.511408575, tolerance: 4935.415058300707
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18310034.54749601, tolerance: 4935.415058300707
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 17700710.690699693, tolerance: 4935.415058300707
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 17120695.350521393, tolerance: 4935.415058300707
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16578547.185854774, tolerance: 4935.415058300707
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16079546.590141583, tolerance: 4935.415058300707
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15625944.643002408, tolerance: 4935.415058300707
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15217505.487376707, tolerance: 4935.415058300707
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14852152.096577289, tolerance: 4935.415058300707
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14526573.28055189, tolerance: 4935.415058300707
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14236719.371650148, tolerance: 4935.415058300707
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13978174.138208715, tolerance: 4935.415058300707
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13746425.59687554, tolerance: 4935.415058300707
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13537068.213543057, tolerance: 4935.415058300707
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13345962.35668854, tolerance: 4935.415058300707
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13169364.634795751, tolerance: 4935.415058300707
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13004032.478034776, tolerance: 4935.415058300707
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12847300.658728212, tolerance: 4935.415058300707
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12697125.671006862, tolerance: 4935.415058300707
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12552094.560836947, tolerance: 4935.415058300707
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12411397.148843242, tolerance: 4935.415058300707
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12274764.131573228, tolerance: 4935.415058300707
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12142377.19980029, tolerance: 4935.415058300707
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12014759.667509668, tolerance: 4935.415058300707
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11892656.674815329, tolerance: 4935.415058300707
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11776913.748335531, tolerance: 4935.415058300707
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11668362.579533802, tolerance: 4935.415058300707
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11567723.248793688, tolerance: 4935.415058300707
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11475531.371202188, tolerance: 4935.415058300707
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11392095.520191537, tolerance: 4935.415058300707
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11317485.154174458, tolerance: 4935.415058300707
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11251544.071619535, tolerance: 4935.415058300707
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11193921.29886968, tolerance: 4935.415058300707
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11144111.12811159, tolerance: 4935.415058300707
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11101495.935706673, tolerance: 4935.415058300707
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11065387.786289494, tolerance: 4935.415058300707
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11035066.484343873, tolerance: 4935.415058300707
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11009812.504896589, tolerance: 4935.415058300707
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10988933.625402749, tolerance: 4935.415058300707
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10971784.57485082, tolerance: 4935.415058300707
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10957779.702890875, tolerance: 4935.415058300707
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10946399.365546623, tolerance: 4935.415058300707
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10937191.212319348, tolerance: 4935.415058300707
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10929767.749576753, tolerance: 4935.415058300707
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10923801.4874784, tolerance: 4935.415058300707
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10919018.7580953, tolerance: 4935.415058300707
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10915193.022578128, tolerance: 4935.415058300707
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10912138.230743896, tolerance: 4935.415058300707
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10909702.58689405, tolerance: 4935.415058300707
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10907762.917273073, tolerance: 4935.415058300707
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10906219.72255186, tolerance: 4935.415058300707
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10904992.924322572, tolerance: 4935.415058300707
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10904018.268865908, tolerance: 4935.415058300707
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10903244.326463731, tolerance: 4935.415058300707
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10902630.013708944, tolerance: 4935.415058300707
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 25318533.09094828, tolerance: 5063.778189984983
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 25318439.387046777, tolerance: 5063.778189984983
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 25318321.148464017, tolerance: 5063.778189984983
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 25318171.952004917, tolerance: 5063.778189984983
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 25317983.693333846, tolerance: 5063.778189984983
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 25317746.147240274, tolerance: 5063.778189984983
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 25317446.413043533, tolerance: 5063.778189984983
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 25317068.215228993, tolerance: 5063.778189984983
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 25316591.02167693, tolerance: 5063.778189984983
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 25315988.93214875, tolerance: 5063.778189984983
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 25315229.277554207, tolerance: 5063.778189984983
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 25314270.855352297, tolerance: 5063.778189984983
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 25313061.707533117, tolerance: 5063.778189984983
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 25311536.324149918, tolerance: 5063.778189984983
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 25309612.126343787, tolerance: 5063.778189984983
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 25307185.047125567, tolerance: 5063.778189984983
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 25304123.984663352, tolerance: 5063.778189984983
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 25300263.850288767, tolerance: 5063.778189984983
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 25295396.870892968, tolerance: 5063.778189984983
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 25289261.73236316, tolerance: 5063.778189984983
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 25281530.0678227, tolerance: 5063.778189984983
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 25271789.704301007, tolerance: 5063.778189984983
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 25259523.99018396, tolerance: 5063.778189984983
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 25244086.44529556, tolerance: 5063.778189984983
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 25224669.926948067, tolerance: 5063.778189984983
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 25200269.524366837, tolerance: 5063.778189984983
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 25169638.5376171, tolerance: 5063.778189984983
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 25131237.2532896, tolerance: 5063.778189984983
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 25083174.92687478, tolerance: 5063.778189984983
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 25023146.601181027, tolerance: 5063.778189984983
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 24948368.36521715, tolerance: 5063.778189984983
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 24855517.659195222, tolerance: 5063.778189984983
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 24740689.50682029, tolerance: 5063.778189984983
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 24599385.195795257, tolerance: 5063.778189984983
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 24426556.608571474, tolerance: 5063.778189984983
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 24216735.989682443, tolerance: 5063.778189984983
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 23964284.96195009, tolerance: 5063.778189984983
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 23663793.887109637, tolerance: 5063.778189984983
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 23310647.51934884, tolerance: 5063.778189984983
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22901739.786615714, tolerance: 5063.778189984983
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22436267.900166444, tolerance: 5063.778189984983
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21916471.87732373, tolerance: 5063.778189984983
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21348131.988441944, tolerance: 5063.778189984983
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20740626.914853014, tolerance: 5063.778189984983
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20106419.20978738, tolerance: 5063.778189984983
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 19459974.150665656, tolerance: 5063.778189984983
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18816291.393266283, tolerance: 5063.778189984983
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18189361.289357416, tolerance: 5063.778189984983
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 17590882.977112345, tolerance: 5063.778189984983
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 17029485.291127317, tolerance: 5063.778189984983
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16510522.493592925, tolerance: 5063.778189984983
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16036353.189076576, tolerance: 5063.778189984983
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15606913.826929148, tolerance: 5063.778189984983
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15220385.40385893, tolerance: 5063.778189984983
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14873802.092405727, tolerance: 5063.778189984983
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14563526.003412157, tolerance: 5063.778189984983
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14285580.014076447, tolerance: 5063.778189984983
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14035871.961256132, tolerance: 5063.778189984983
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13810355.354922555, tolerance: 5063.778189984983
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13605162.135581084, tolerance: 5063.778189984983
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13416724.537811583, tolerance: 5063.778189984983
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13241886.652275931, tolerance: 5063.778189984983
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13077997.016023709, tolerance: 5063.778189984983
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12922971.34482817, tolerance: 5063.778189984983
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12775316.746379118, tolerance: 5063.778189984983
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12634113.144532332, tolerance: 5063.778189984983
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12498953.017941514, tolerance: 5063.778189984983
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12369846.07983476, tolerance: 5063.778189984983
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12247100.187784849, tolerance: 5063.778189984983
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12131192.716277028, tolerance: 5063.778189984983
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12022647.513201786, tolerance: 5063.778189984983
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11921931.295156265, tolerance: 5063.778189984983
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11829379.727644198, tolerance: 5063.778189984983
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11745157.606840756, tolerance: 5063.778189984983
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11669250.658453051, tolerance: 5063.778189984983
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11601480.650373615, tolerance: 5063.778189984983
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11541532.882659528, tolerance: 5063.778189984983
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11488986.40505293, tolerance: 5063.778189984983
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11443341.340463819, tolerance: 5063.778189984983
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11404042.077559292, tolerance: 5063.778189984983
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11370497.740435531, tolerance: 5063.778189984983
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11342101.615677832, tolerance: 5063.778189984983
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11318249.968582764, tolerance: 5063.778189984983
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11298359.327014832, tolerance: 5063.778189984983
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11281880.792154457, tolerance: 5063.778189984983
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11268310.312801206, tolerance: 5063.778189984983
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11257194.663530076, tolerance: 5063.778189984983
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11248133.59625709, tolerance: 5063.778189984983
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11240779.031358069, tolerance: 5063.778189984983
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11234832.219172042, tolerance: 5063.778189984983
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11230039.669558657, tolerance: 5063.778189984983
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11226188.452716459, tolerance: 5063.778189984983
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11223101.297438147, tolerance: 5063.778189984983
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11220631.777051251, tolerance: 5063.778189984983
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11218659.773823448, tolerance: 5063.778189984983
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11217087.338645061, tolerance: 5063.778189984983
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11215835.00653132, tolerance: 5063.778189984983
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11214838.586028146, tolerance: 5063.778189984983
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11214046.410167772, tolerance: 5063.778189984983
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11213417.016666459, tolerance: 5063.778189984983
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 23496947.169032525, tolerance: 4699.461465074117
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 23496852.863400083, tolerance: 4699.461465074117
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 23496733.865429547, tolerance: 4699.461465074117
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 23496583.710589353, tolerance: 4699.461465074117
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 23496394.24235175, tolerance: 4699.461465074117
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 23496155.16959734, tolerance: 4699.461465074117
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 23495853.508398402, tolerance: 4699.461465074117
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 23495472.878067963, tolerance: 4699.461465074117
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 23494992.613572504, tolerance: 4699.461465074117
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 23494386.646634873, tolerance: 4699.461465074117
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 23493622.095614444, tolerance: 4699.461465074117
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 23492657.488951046, tolerance: 4699.461465074117
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 23491440.52788121, tolerance: 4699.461465074117
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 23489905.27042506, tolerance: 4699.461465074117
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 23487968.58929991, tolerance: 4699.461465074117
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 23485525.720305603, tolerance: 4699.461465074117
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 23482444.673607368, tolerance: 4699.461465074117
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 23478559.22695931, tolerance: 4699.461465074117
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 23473660.15616345, tolerance: 4699.461465074117
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 23467484.283307027, tolerance: 4699.461465074117
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 23459700.83792351, tolerance: 4699.461465074117
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 23449894.532427087, tolerance: 4699.461465074117
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 23437544.65653946, tolerance: 4699.461465074117
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 23421999.407083638, tolerance: 4699.461465074117
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 23402444.609551508, tolerance: 4699.461465074117
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 23377865.99032305, tolerance: 4699.461465074117
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 23347004.278417062, tolerance: 4699.461465074117
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 23308302.738629945, tolerance: 4699.461465074117
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 23259847.39082906, tolerance: 4699.461465074117
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 23199301.332389183, tolerance: 4699.461465074117
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 23123836.489303295, tolerance: 4699.461465074117
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 23030069.058943428, tolerance: 4699.461465074117
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22914009.151324138, tolerance: 4699.461465074117
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22771040.835288074, tolerance: 4699.461465074117
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22595955.731927253, tolerance: 4699.461465074117
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22383070.487189744, tolerance: 4699.461465074117
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22126463.62486345, tolerance: 4699.461465074117
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21820366.3794784, tolerance: 4699.461465074117
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21459729.334616963, tolerance: 4699.461465074117
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21040955.842625808, tolerance: 4699.461465074117
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20562741.223471507, tolerance: 4699.461465074117
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20026889.460414518, tolerance: 4699.461465074117
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 19438916.65954936, tolerance: 4699.461465074117
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18808226.734429274, tolerance: 4699.461465074117
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18147694.20131125, tolerance: 4699.461465074117
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 17472622.541746154, tolerance: 4699.461465074117
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16799230.165234204, tolerance: 4699.461465074117
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16142974.591753783, tolerance: 4699.461465074117
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15517081.675020564, tolerance: 4699.461465074117
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14931570.532097273, tolerance: 4699.461465074117
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14392894.890897019, tolerance: 4699.461465074117
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13904138.864683226, tolerance: 4699.461465074117
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13465582.719837563, tolerance: 4699.461465074117
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13075421.402139833, tolerance: 4699.461465074117
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12730459.629938494, tolerance: 4699.461465074117
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12426683.270393558, tolerance: 4699.461465074117
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12159679.781825444, tolerance: 4699.461465074117
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11924928.385726035, tolerance: 4699.461465074117
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11717998.71323751, tolerance: 4699.461465074117
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11534692.869278003, tolerance: 4699.461465074117
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11371152.624974824, tolerance: 4699.461465074117
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11223940.916220868, tolerance: 4699.461465074117
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11090099.526012365, tolerance: 4699.461465074117
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10967182.326787163, tolerance: 4699.461465074117
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10853263.259389356, tolerance: 4699.461465074117
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10746918.53068873, tolerance: 4699.461465074117
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10647183.180193713, tolerance: 4699.461465074117
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10553483.883620583, tolerance: 4699.461465074117
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10465552.866454318, tolerance: 4699.461465074117
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10383331.355655227, tolerance: 4699.461465074117
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10306873.71748356, tolerance: 4699.461465074117
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10236263.92576194, tolerance: 4699.461465074117
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10171553.529778643, tolerance: 4699.461465074117
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10112725.251639228, tolerance: 4699.461465074117
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10059680.354430916, tolerance: 4699.461465074117
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10012243.14153044, tolerance: 4699.461465074117
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9970174.009876935, tolerance: 4699.461465074117
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9933183.715242703, tolerance: 4699.461465074117
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9900944.85073939, tolerance: 4699.461465074117
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9873100.145330785, tolerance: 4699.461465074117
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9849269.426629864, tolerance: 4699.461465074117
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9829057.363870632, tolerance: 4699.461465074117
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9812062.963064875, tolerance: 4699.461465074117
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9797890.329837687, tolerance: 4699.461465074117
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9786159.323968118, tolerance: 4699.461465074117
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9776514.654582523, tolerance: 4699.461465074117
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9768632.455679577, tolerance: 4699.461465074117
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9762224.039044391, tolerance: 4699.461465074117
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9757037.055582853, tolerance: 4699.461465074117
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9752854.597735004, tolerance: 4699.461465074117
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9749492.866910059, tolerance: 4699.461465074117
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9746797.984370885, tolerance: 4699.461465074117
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9744642.413102694, tolerance: 4699.461465074117
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9742921.331222853, tolerance: 4699.461465074117
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9741549.181419913, tolerance: 4699.461465074117
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9740456.527129674, tolerance: 4699.461465074117
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9739587.276830459, tolerance: 4699.461465074117
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9738896.29050694, tolerance: 4699.461465074117
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9738347.352645943, tolerance: 4699.461465074117
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9737911.479561875, tolerance: 4699.461465074117
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.271e+07, tolerance: 5.332e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(Pipeline(steps=[('scaler', StandardScaler()),
('ridge',
ElasticNetCV(alphas=array([2.22093791e+05, 1.76005531e+05, 1.39481373e+05, 1.10536603e+05,
8.75983676e+04, 6.94202082e+04, 5.50143278e+04, 4.35979140e+04,
3.45506012e+04, 2.73807606e+04, 2.16987845e+04, 1.71959156e+04,
1.36274691e+04, 1.07995362e+04, 8.55844774e+03, 6.78242347e+03,
5.37495461e+03, 4.25955961e+03,...
1.84386167e-03, 1.46122884e-03, 1.15799887e-03, 9.17694298e-04,
7.27257037e-04, 5.76338765e-04, 4.56738615e-04, 3.61957541e-04,
2.86845161e-04, 2.27319885e-04, 1.80147121e-04, 1.42763513e-04,
1.13137642e-04, 8.96596467e-05, 7.10537367e-05, 5.63088712e-05,
4.46238174e-05, 3.53636122e-05, 2.80250579e-05, 2.22093791e-05]),
cv=KFold(n_splits=10, random_state=0, shuffle=True),
l1_ratio=0))])In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
Pipeline(steps=[('scaler', StandardScaler()),
('ridge',
ElasticNetCV(alphas=array([2.22093791e+05, 1.76005531e+05, 1.39481373e+05, 1.10536603e+05,
8.75983676e+04, 6.94202082e+04, 5.50143278e+04, 4.35979140e+04,
3.45506012e+04, 2.73807606e+04, 2.16987845e+04, 1.71959156e+04,
1.36274691e+04, 1.07995362e+04, 8.55844774e+03, 6.78242347e+03,
5.37495461e+03, 4.25955961e+03,...
1.84386167e-03, 1.46122884e-03, 1.15799887e-03, 9.17694298e-04,
7.27257037e-04, 5.76338765e-04, 4.56738615e-04, 3.61957541e-04,
2.86845161e-04, 2.27319885e-04, 1.80147121e-04, 1.42763513e-04,
1.13137642e-04, 8.96596467e-05, 7.10537367e-05, 5.63088712e-05,
4.46238174e-05, 3.53636122e-05, 2.80250579e-05, 2.22093791e-05]),
cv=KFold(n_splits=10, random_state=0, shuffle=True),
l1_ratio=0))])StandardScaler()
ElasticNetCV(alphas=array([2.22093791e+05, 1.76005531e+05, 1.39481373e+05, 1.10536603e+05,
8.75983676e+04, 6.94202082e+04, 5.50143278e+04, 4.35979140e+04,
3.45506012e+04, 2.73807606e+04, 2.16987845e+04, 1.71959156e+04,
1.36274691e+04, 1.07995362e+04, 8.55844774e+03, 6.78242347e+03,
5.37495461e+03, 4.25955961e+03, 3.37562814e+03, 2.67512757e+03,
2.11999285e+03, 1.680058...
1.84386167e-03, 1.46122884e-03, 1.15799887e-03, 9.17694298e-04,
7.27257037e-04, 5.76338765e-04, 4.56738615e-04, 3.61957541e-04,
2.86845161e-04, 2.27319885e-04, 1.80147121e-04, 1.42763513e-04,
1.13137642e-04, 8.96596467e-05, 7.10537367e-05, 5.63088712e-05,
4.46238174e-05, 3.53636122e-05, 2.80250579e-05, 2.22093791e-05]),
cv=KFold(n_splits=10, random_state=0, shuffle=True), l1_ratio=0)tuned_ridge = pipeCV.named_steps['ridge']
ridgeCV_fig, ax = subplots(figsize=(8,8))
ax.errorbar(-np.log(lambdas),
tuned_ridge.mse_path_.mean(1),
yerr=tuned_ridge.mse_path_.std(1) / np.sqrt(K))
ax.axvline(-np.log(tuned_ridge.alpha_), c='k', ls='--')
ax.set_ylim ([50000 ,250000])
ax.set_xlabel('$-\log(\lambda)$', fontsize=20)
ax.set_ylabel('Cross-validated MSE', fontsize=20);
np.min(tuned_ridge.mse_path_.mean(1)), tuned_ridge.coef_(112362.26398006166,
array([ 0. , -222.80877051, 238.77246614, 3.21103754,
-2.93050845, 3.64888723, 108.90953869, -50.81896152,
-105.15731984, 122.00714801, 57.1859509 , 210.35170348,
118.05683748, -150.21959435, 30.36634231, -61.62459095,
77.73832472, 40.07350744, -25.02151514, -13.68429544]))
- 교차 검증 오차 방법의 검증 데이터 셋을 이용한 방법과 비교(일반적으로 데이터가 적은 경우 교차 검증 오차를 사용)
outer_valid = skm.ShuffleSplit(n_splits=1, test_size=0.25, random_state=1)
inner_cv = skm.KFold(n_splits=5, shuffle=True,random_state=2)
ridgeCV = skl.ElasticNetCV(alphas=lambdas, l1_ratio=0, cv=inner_cv)
pipeCV = Pipeline(steps=[('scaler', scaler), ('ridge', ridgeCV)]);
results = skm.cross_validate(pipeCV, X, Y, cv=outer_valid, scoring='neg_mean_squared_error')/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16002961.89304734, tolerance: 3200.6325551004934
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16002909.292721536, tolerance: 3200.6325551004934
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16002842.919898542, tolerance: 3200.6325551004934
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16002759.168901473, tolerance: 3200.6325551004934
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16002653.490324108, tolerance: 3200.6325551004934
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16002520.144170541, tolerance: 3200.6325551004934
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16002351.888507722, tolerance: 3200.6325551004934
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16002139.586836113, tolerance: 3200.6325551004934
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16001871.713040238, tolerance: 3200.6325551004934
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16001533.72733189, tolerance: 3200.6325551004934
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16001107.289774053, tolerance: 3200.6325551004934
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16000569.269442711, tolerance: 3200.6325551004934
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15999890.496647637, tolerance: 3200.6325551004934
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15999034.192416638, tolerance: 3200.6325551004934
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15997953.993094176, tolerance: 3200.6325551004934
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15996591.467783947, tolerance: 3200.6325551004934
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15994873.001788346, tolerance: 3200.6325551004934
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15992705.889472546, tolerance: 3200.6325551004934
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15989973.444502642, tolerance: 3200.6325551004934
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15986528.893835299, tolerance: 3200.6325551004934
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15982187.774395376, tolerance: 3200.6325551004934
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15976718.499356631, tolerance: 3200.6325551004934
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15969830.707495736, tolerance: 3200.6325551004934
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15961160.960501967, tolerance: 3200.6325551004934
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15950255.32070595, tolerance: 3200.6325551004934
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15936548.344581455, tolerance: 3200.6325551004934
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15919338.096469928, tolerance: 3200.6325551004934
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15897756.970098713, tolerance: 3200.6325551004934
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15870738.473491091, tolerance: 3200.6325551004934
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15836980.785622947, tolerance: 3200.6325551004934
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15794908.96193258, tolerance: 3200.6325551004934
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15742639.305781402, tolerance: 3200.6325551004934
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15677951.783964384, tolerance: 3200.6325551004934
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15598279.520216348, tolerance: 3200.6325551004934
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15500728.213326862, tolerance: 3200.6325551004934
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15382142.225333134, tolerance: 3200.6325551004934
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15239236.776243076, tolerance: 3200.6325551004934
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15068814.890988706, tolerance: 3200.6325551004934
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14868080.263148531, tolerance: 3200.6325551004934
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14635039.685599195, tolerance: 3200.6325551004934
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14368959.698660217, tolerance: 3200.6325551004934
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14070805.238626324, tolerance: 3200.6325551004934
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13743554.881437784, tolerance: 3200.6325551004934
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13392276.560592555, tolerance: 3200.6325551004934
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13023877.880913062, tolerance: 3200.6325551004934
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12646520.933576023, tolerance: 3200.6325551004934
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12268792.343592059, tolerance: 3200.6325551004934
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11898803.095559347, tolerance: 3200.6325551004934
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11543417.93091813, tolerance: 3200.6325551004934
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11207766.718773345, tolerance: 3200.6325551004934
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10895093.611569975, tolerance: 3200.6325551004934
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10606899.31299726, tolerance: 3200.6325551004934
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10343266.881240882, tolerance: 3200.6325551004934
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10103247.353431463, tolerance: 3200.6325551004934
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9885208.910573535, tolerance: 3200.6325551004934
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9687100.478192504, tolerance: 3200.6325551004934
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9506625.781409403, tolerance: 3200.6325551004934
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9341352.903950265, tolerance: 3200.6325551004934
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9188793.402093261, tolerance: 3200.6325551004934
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9046478.453631107, tolerance: 3200.6325551004934
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8912045.904589206, tolerance: 3200.6325551004934
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8783339.107432578, tolerance: 3200.6325551004934
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8658509.901020322, tolerance: 3200.6325551004934
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8536113.828113696, tolerance: 3200.6325551004934
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8415183.975072173, tolerance: 3200.6325551004934
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8295269.742745542, tolerance: 3200.6325551004934
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8176429.120013453, tolerance: 3200.6325551004934
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8059168.829305616, tolerance: 3200.6325551004934
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7944335.999206879, tolerance: 3200.6325551004934
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7832975.6452165125, tolerance: 3200.6325551004934
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7726176.614947232, tolerance: 3200.6325551004934
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7624931.461247047, tolerance: 3200.6325551004934
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7530031.627469206, tolerance: 3200.6325551004934
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7442009.746564684, tolerance: 3200.6325551004934
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7361129.146973563, tolerance: 3200.6325551004934
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7287410.635336472, tolerance: 3200.6325551004934
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7220681.0956169395, tolerance: 3200.6325551004934
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7160628.395404587, tolerance: 3200.6325551004934
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7106851.483765794, tolerance: 3200.6325551004934
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7058900.769700816, tolerance: 3200.6325551004934
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7016308.880858171, tolerance: 3200.6325551004934
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6978613.9117776835, tolerance: 3200.6325551004934
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6945376.57102739, tolerance: 3200.6325551004934
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6916191.049528797, tolerance: 3200.6325551004934
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6890688.792446523, tolerance: 3200.6325551004934
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6868535.393320505, tolerance: 3200.6325551004934
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6849422.765040072, tolerance: 3200.6325551004934
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6833060.050953316, tolerance: 3200.6325551004934
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6819166.544534643, tolerance: 3200.6325551004934
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6807468.458908792, tolerance: 3200.6325551004934
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6797699.628345579, tolerance: 3200.6325551004934
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6789604.944998943, tolerance: 3200.6325551004934
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6782944.868629143, tolerance: 3200.6325551004934
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6777499.565630765, tolerance: 3200.6325551004934
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6773071.791554663, tolerance: 3200.6325551004934
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6769488.209512866, tolerance: 3200.6325551004934
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6766599.256783528, tolerance: 3200.6325551004934
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6764277.8922138605, tolerance: 3200.6325551004934
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6762417.6162485825, tolerance: 3200.6325551004934
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6760930.116967763, tolerance: 3200.6325551004934
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15173612.824876541, tolerance: 3034.76265980692
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15173560.331518073, tolerance: 3034.76265980692
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15173494.093703296, tolerance: 3034.76265980692
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15173410.513116252, tolerance: 3034.76265980692
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15173305.049649915, tolerance: 3034.76265980692
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15173171.975059807, tolerance: 3034.76265980692
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15173004.062268814, tolerance: 3034.76265980692
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15172792.19356697, tolerance: 3034.76265980692
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15172524.86661776, tolerance: 3034.76265980692
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15172187.571748765, tolerance: 3034.76265980692
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15171762.007200051, tolerance: 3034.76265980692
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15171225.09050039, tolerance: 3034.76265980692
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15170547.713543423, tolerance: 3034.76265980692
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15169693.175771879, tolerance: 3034.76265980692
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15168615.21359888, tolerance: 3034.76265980692
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15167255.524179865, tolerance: 3034.76265980692
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15165540.657224858, tolerance: 3034.76265980692
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15163378.119038211, tolerance: 3034.76265980692
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15160651.497821938, tolerance: 3034.76265980692
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15157214.378191708, tolerance: 3034.76265980692
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15152882.766135197, tolerance: 3034.76265980692
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15147425.694698602, tolerance: 3034.76265980692
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15140553.6288505, tolerance: 3034.76265980692
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15131904.2417773, tolerance: 3034.76265980692
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15121025.105980715, tolerance: 3034.76265980692
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15107352.85059929, tolerance: 3034.76265980692
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15090188.412868412, tolerance: 3034.76265980692
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15068668.205066575, tolerance: 3034.76265980692
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15041731.400110114, tolerance: 3034.76265980692
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15008084.20895599, tolerance: 3034.76265980692
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14966163.110870237, tolerance: 3034.76265980692
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14914100.653844738, tolerance: 3034.76265980692
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14849699.805850955, tolerance: 3034.76265980692
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14770425.961151278, tolerance: 3034.76265980692
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14673429.416906541, tolerance: 3034.76265980692
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14555614.815015968, tolerance: 3034.76265980692
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14413776.349016689, tolerance: 3034.76265980692
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14244816.178940997, tolerance: 3034.76265980692
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14046055.366934754, tolerance: 3034.76265980692
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13815628.708094304, tolerance: 3034.76265980692
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13552926.20568371, tolerance: 3034.76265980692
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13259008.940702373, tolerance: 3034.76265980692
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12936897.57322831, tolerance: 3034.76265980692
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12591625.616217317, tolerance: 3034.76265980692
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12229982.920676824, tolerance: 3034.76265980692
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11859948.802383406, tolerance: 3034.76265980692
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11489906.8603167, tolerance: 3034.76265980692
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11127805.377401605, tolerance: 3034.76265980692
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10780443.14443526, tolerance: 3034.76265980692
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10453012.587348029, tolerance: 3034.76265980692
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10148944.57852918, tolerance: 3034.76265980692
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9870012.667698367, tolerance: 3034.76265980692
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9616601.23067291, tolerance: 3034.76265980692
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9388032.941233685, tolerance: 3034.76265980692
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9182876.289070567, tolerance: 3034.76265980692
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8999193.791535864, tolerance: 3034.76265980692
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8834727.194341864, tolerance: 3034.76265980692
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8687036.347689658, tolerance: 3034.76265980692
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8553612.383287702, tolerance: 3034.76265980692
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8431979.28023448, tolerance: 3034.76265980692
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8319788.946660168, tolerance: 3034.76265980692
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8214909.054690694, tolerance: 3034.76265980692
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8115501.105643087, tolerance: 3034.76265980692
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8020086.355249518, tolerance: 3034.76265980692
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7927596.538468508, tolerance: 3034.76265980692
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7837403.822275508, tolerance: 3034.76265980692
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7749321.5353355035, tolerance: 3034.76265980692
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7663566.802084752, tolerance: 3034.76265980692
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7580680.550684234, tolerance: 3034.76265980692
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7501409.564666601, tolerance: 3034.76265980692
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7426566.500521766, tolerance: 3034.76265980692
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7356892.242162717, tolerance: 3034.76265980692
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7292946.117485049, tolerance: 3034.76265980692
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7235042.04159694, tolerance: 3034.76265980692
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7183235.551674285, tolerance: 3034.76265980692
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7137353.553695879, tolerance: 3034.76265980692
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7097050.348456498, tolerance: 3034.76265980692
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7061872.012726564, tolerance: 3034.76265980692
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7031315.123405527, tolerance: 3034.76265980692
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7004872.089238531, tolerance: 3034.76265980692
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6982061.123035986, tolerance: 3034.76265980692
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6962442.578610304, tolerance: 3034.76265980692
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6945624.890073834, tolerance: 3034.76265980692
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6931263.466327003, tolerance: 3034.76265980692
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6919055.476661663, tolerance: 3034.76265980692
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6908732.97753915, tolerance: 3034.76265980692
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6900056.292042715, tolerance: 3034.76265980692
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6892808.858171821, tolerance: 3034.76265980692
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6886793.977603597, tolerance: 3034.76265980692
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6881833.233569166, tolerance: 3034.76265980692
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6877765.9749890845, tolerance: 3034.76265980692
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6874449.2071706075, tolerance: 3034.76265980692
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6871757.386867803, tolerance: 3034.76265980692
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6869581.85391294, tolerance: 3034.76265980692
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6867829.838852224, tolerance: 3034.76265980692
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6866423.119345698, tolerance: 3034.76265980692
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6865296.456501944, tolerance: 3034.76265980692
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6864395.947002568, tolerance: 3034.76265980692
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6863677.402652292, tolerance: 3034.76265980692
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6863104.835000147, tolerance: 3034.76265980692
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16000126.775776317, tolerance: 3200.070250165818
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16000067.997791685, tolerance: 3200.070250165818
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15999993.829780782, tolerance: 3200.070250165818
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15999900.24258462, tolerance: 3200.070250165818
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15999782.152469942, tolerance: 3200.070250165818
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15999633.145271106, tolerance: 3200.070250165818
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15999445.12846794, tolerance: 3200.070250165818
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15999207.89243054, tolerance: 3200.070250165818
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15998908.557207119, tolerance: 3200.070250165818
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15998530.875140414, tolerance: 3200.070250165818
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15998054.351968955, tolerance: 3200.070250165818
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15997453.139532344, tolerance: 3200.070250165818
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15996694.641307212, tolerance: 3200.070250165818
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15995737.757220384, tolerance: 3200.070250165818
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15994530.675893761, tolerance: 3200.070250165818
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15993008.099962443, tolerance: 3200.070250165818
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15991087.762599913, tolerance: 3200.070250165818
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15988666.06009735, tolerance: 3200.070250165818
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15985612.585588468, tolerance: 3200.070250165818
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15981763.302383823, tolerance: 3200.070250165818
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15976912.04209659, tolerance: 3200.070250165818
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15970799.954194363, tolerance: 3200.070250165818
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15963102.473251346, tolerance: 3200.070250165818
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15953413.314912455, tolerance: 3200.070250165818
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15941224.973906958, tolerance: 3200.070250165818
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15925905.198558562, tolerance: 3200.070250165818
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15906668.99042816, tolerance: 3200.070250165818
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15882545.878220893, tolerance: 3200.070250165818
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15852342.621036038, tolerance: 3200.070250165818
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15814602.219371138, tolerance: 3200.070250165818
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15767561.301116718, tolerance: 3200.070250165818
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15709109.781098891, tolerance: 3200.070250165818
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15636759.341258612, tolerance: 3200.070250165818
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15547630.840385435, tolerance: 3200.070250165818
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15438475.105455771, tolerance: 3200.070250165818
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15305746.074655257, tolerance: 3200.070250165818
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15145748.542592408, tolerance: 3200.070250165818
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14954882.273867266, tolerance: 3200.070250165818
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14729996.363846604, tolerance: 3200.070250165818
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14468848.510940224, tolerance: 3200.070250165818
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14170631.317143815, tolerance: 3200.070250165818
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13836485.361873373, tolerance: 3200.070250165818
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13469879.089990828, tolerance: 3200.070250165818
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13076719.754361458, tolerance: 3200.070250165818
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12665089.799378188, tolerance: 3200.070250165818
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12244586.676668115, tolerance: 3200.070250165818
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11825360.363691226, tolerance: 3200.070250165818
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11417044.801169299, tolerance: 3200.070250165818
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11027817.645702792, tolerance: 3200.070250165818
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10663776.910200797, tolerance: 3200.070250165818
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10328716.267595597, tolerance: 3200.070250165818
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10024263.647837916, tolerance: 3200.070250165818
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9750266.81973182, tolerance: 3200.070250165818
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9505284.68877341, tolerance: 3200.070250165818
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9287065.610720865, tolerance: 3200.070250165818
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9092940.776433371, tolerance: 3200.070250165818
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8920108.266351486, tolerance: 3200.070250165818
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8765816.866835356, tolerance: 3200.070250165818
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8627473.905486995, tolerance: 3200.070250165818
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8502702.196109632, tolerance: 3200.070250165818
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8389365.458262939, tolerance: 3200.070250165818
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8285575.962199825, tolerance: 3200.070250165818
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8189695.129107311, tolerance: 3200.070250165818
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8100335.848355172, tolerance: 3200.070250165818
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8016371.614804295, tolerance: 3200.070250165818
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7936951.343980091, tolerance: 3200.070250165818
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7861511.843848038, tolerance: 3200.070250165818
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7789775.818775915, tolerance: 3200.070250165818
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7721724.4917248925, tolerance: 3200.070250165818
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7657540.535692903, tolerance: 3200.070250165818
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7597526.506006071, tolerance: 3200.070250165818
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7542012.431576106, tolerance: 3200.070250165818
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7491270.131107023, tolerance: 3200.070250165818
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7445449.741931628, tolerance: 3200.070250165818
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7404547.164364994, tolerance: 3200.070250165818
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7368402.734578204, tolerance: 3200.070250165818
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7336724.612267197, tolerance: 3200.070250165818
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7309126.908337062, tolerance: 3200.070250165818
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7285172.539342817, tolerance: 3200.070250165818
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7264413.026630251, tolerance: 3200.070250165818
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7246420.465473457, tolerance: 3200.070250165818
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7230809.549602883, tolerance: 3200.070250165818
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7217249.40796195, tolerance: 3200.070250165818
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7205466.206412764, tolerance: 3200.070250165818
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7195238.325929964, tolerance: 3200.070250165818
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7186386.647781853, tolerance: 3200.070250165818
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7178762.875061102, tolerance: 3200.070250165818
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7172238.602284, tolerance: 3200.070250165818
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7166697.001612058, tolerance: 3200.070250165818
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7162027.848205676, tolerance: 3200.070250165818
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7158125.5844214875, tolerance: 3200.070250165818
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7154889.512672238, tolerance: 3200.070250165818
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7152225.062096822, tolerance: 3200.070250165818
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7150045.2620962765, tolerance: 3200.070250165818
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7148271.882784381, tolerance: 3200.070250165818
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7146836.014640992, tolerance: 3200.070250165818
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7145678.080779785, tolerance: 3200.070250165818
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7144747.393668601, tolerance: 3200.070250165818
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7144001.407092082, tolerance: 3200.070250165818
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7143404.805305515, tolerance: 3200.070250165818
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13766426.84442544, tolerance: 2753.3219034862304
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13766379.012219733, tolerance: 2753.3219034862304
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13766318.65599331, tolerance: 2753.3219034862304
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13766242.496938992, tolerance: 2753.3219034862304
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13766146.398082256, tolerance: 2753.3219034862304
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13766025.139807519, tolerance: 2753.3219034862304
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13765872.136748437, tolerance: 2753.3219034862304
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13765679.080773309, tolerance: 2753.3219034862304
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13765435.490848664, tolerance: 2753.3219034862304
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13765128.145612366, tolerance: 2753.3219034862304
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13764740.368286433, tolerance: 2753.3219034862304
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13764251.125810029, tolerance: 2753.3219034862304
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13763633.89441395, tolerance: 2753.3219034862304
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13762855.231859, tolerance: 2753.3219034862304
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13761872.981721638, tolerance: 2753.3219034862304
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13760634.016862668, tolerance: 2753.3219034862304
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13759071.40694565, tolerance: 2753.3219034862304
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13757100.867966292, tolerance: 2753.3219034862304
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13754616.319689387, tolerance: 2753.3219034862304
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13751484.339396803, tolerance: 2753.3219034862304
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13747537.25769523, tolerance: 2753.3219034862304
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13742564.595583744, tolerance: 2753.3219034862304
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13736302.494553428, tolerance: 2753.3219034862304
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13728420.74910962, tolerance: 2753.3219034862304
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13718507.024368448, tolerance: 2753.3219034862304
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13706047.848124273, tolerance: 2753.3219034862304
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13690406.035690319, tolerance: 2753.3219034862304
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13670794.381086987, tolerance: 2753.3219034862304
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13646245.795015216, tolerance: 2753.3219034862304
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13615580.679837886, tolerance: 2753.3219034862304
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13577373.323622871, tolerance: 2753.3219034862304
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13529920.608156206, tolerance: 2753.3219034862304
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13471218.489805978, tolerance: 2753.3219034862304
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13398954.581488006, tolerance: 2753.3219034862304
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13310528.590455975, tolerance: 2753.3219034862304
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13203115.797389355, tolerance: 2753.3219034862304
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13073790.981404452, tolerance: 2753.3219034862304
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12919729.112886172, tolerance: 2753.3219034862304
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12738491.873820402, tolerance: 2753.3219034862304
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12528392.75276841, tolerance: 2753.3219034862304
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12288907.120278116, tolerance: 2753.3219034862304
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12021061.050642934, tolerance: 2753.3219034862304
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11727704.457379242, tolerance: 2753.3219034862304
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11413566.984203365, tolerance: 2753.3219034862304
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11085024.381464425, tolerance: 2753.3219034862304
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10749570.986969214, tolerance: 2753.3219034862304
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10415080.823900377, tolerance: 2753.3219034862304
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10089009.13899466, tolerance: 2753.3219034862304
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9777704.218602655, tolerance: 2753.3219034862304
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9485957.157639354, tolerance: 2753.3219034862304
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9216836.907742975, tolerance: 2753.3219034862304
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8971777.23957061, tolerance: 2753.3219034862304
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8750831.806329561, tolerance: 2753.3219034862304
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8553002.845594909, tolerance: 2753.3219034862304
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8376568.552591971, tolerance: 2753.3219034862304
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8219365.993957073, tolerance: 2753.3219034862304
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8079015.288983261, tolerance: 2753.3219034862304
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7953088.944512307, tolerance: 2753.3219034862304
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7839237.297915908, tolerance: 2753.3219034862304
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7735280.817484563, tolerance: 2753.3219034862304
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7639277.052384115, tolerance: 2753.3219034862304
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7549567.214150181, tolerance: 2753.3219034862304
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7464805.4369224785, tolerance: 2753.3219034862304
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7383972.203368445, tolerance: 2753.3219034862304
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7306371.938584434, tolerance: 2753.3219034862304
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7231613.973232701, tolerance: 2753.3219034862304
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7159576.877369871, tolerance: 2753.3219034862304
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7090358.567763276, tolerance: 2753.3219034862304
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7024217.224121639, tolerance: 2753.3219034862304
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6961509.172985439, tolerance: 2753.3219034862304
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6902628.9417572245, tolerance: 2753.3219034862304
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6847954.742128461, tolerance: 2753.3219034862304
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6797801.388529902, tolerance: 2753.3219034862304
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6752382.798167808, tolerance: 2753.3219034862304
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6711786.944628142, tolerance: 2753.3219034862304
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6675965.981966976, tolerance: 2753.3219034862304
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6644742.448857559, tolerance: 2753.3219034862304
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6617829.550839521, tolerance: 2753.3219034862304
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6594860.867273536, tolerance: 2753.3219034862304
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6575423.588385654, tolerance: 2753.3219034862304
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6559089.833983631, tolerance: 2753.3219034862304
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6545442.225938159, tolerance: 2753.3219034862304
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6534091.895329483, tolerance: 2753.3219034862304
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6524688.873516026, tolerance: 2753.3219034862304
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6516926.039701585, tolerance: 2753.3219034862304
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6510538.426567713, tolerance: 2753.3219034862304
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6505299.778051355, tolerance: 2753.3219034862304
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6501017.943079217, tolerance: 2753.3219034862304
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6497530.1764775505, tolerance: 2753.3219034862304
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6494698.902794322, tolerance: 2753.3219034862304
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6492408.111473336, tolerance: 2753.3219034862304
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6490560.333699323, tolerance: 2753.3219034862304
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6489074.0742422305, tolerance: 2753.3219034862304
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6487881.578697671, tolerance: 2753.3219034862304
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6486926.855244381, tolerance: 2753.3219034862304
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6486163.908028811, tolerance: 2753.3219034862304
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6485555.163897259, tolerance: 2753.3219034862304
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6485070.084972537, tolerance: 2753.3219034862304
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6484683.961142821, tolerance: 2753.3219034862304
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6484376.873671146, tolerance: 2753.3219034862304
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16123836.286658322, tolerance: 3224.8236814135257
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16123762.414447505, tolerance: 3224.8236814135257
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16123669.20004301, tolerance: 3224.8236814135257
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16123551.579596581, tolerance: 3224.8236814135257
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16123403.163871316, tolerance: 3224.8236814135257
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16123215.891543612, tolerance: 3224.8236814135257
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16122979.591935376, tolerance: 3224.8236814135257
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16122681.433587791, tolerance: 3224.8236814135257
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16122305.228986476, tolerance: 3224.8236814135257
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16121830.558093363, tolerance: 3224.8236814135257
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16121231.66375273, tolerance: 3224.8236814135257
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16120476.06005272, tolerance: 3224.8236814135257
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16119522.77977849, tolerance: 3224.8236814135257
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16118320.16851829, tolerance: 3224.8236814135257
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16116803.109996727, tolerance: 3224.8236814135257
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16114889.538918182, tolerance: 3224.8236814135257
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16112476.063036691, tolerance: 3224.8236814135257
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16109432.474341484, tolerance: 3224.8236814135257
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16105594.879294185, tolerance: 3224.8236814135257
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16100757.119470125, tolerance: 3224.8236814135257
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16094660.087017832, tolerance: 3224.8236814135257
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16086978.465806844, tolerance: 3224.8236814135257
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16077304.353326883, tolerance: 3224.8236814135257
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16065127.149018398, tolerance: 3224.8236814135257
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16049809.047450973, tolerance: 3224.8236814135257
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16030555.47624171, tolerance: 3224.8236814135257
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16006379.911872499, tolerance: 3224.8236814135257
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15976062.758394279, tolerance: 3224.8236814135257
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15938104.483596487, tolerance: 3224.8236814135257
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15890674.114698274, tolerance: 3224.8236814135257
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15831555.686060239, tolerance: 3224.8236814135257
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15758097.525340758, tolerance: 3224.8236814135257
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15667172.578206712, tolerance: 3224.8236814135257
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15555162.42074894, tolerance: 3224.8236814135257
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15417983.020182049, tolerance: 3224.8236814135257
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15251175.90859317, tolerance: 3224.8236814135257
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15050092.45331768, tolerance: 3224.8236814135257
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14810198.17774659, tolerance: 3224.8236814135257
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14527514.08283525, tolerance: 3224.8236814135257
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14199187.811678285, tolerance: 3224.8236814135257
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13824146.920817541, tolerance: 3224.8236814135257
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13403734.027286606, tolerance: 3224.8236814135257
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12942174.86967796, tolerance: 3224.8236814135257
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12446711.65903124, tolerance: 3224.8236814135257
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11927272.408043083, tolerance: 3224.8236814135257
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11395650.820912806, tolerance: 3224.8236814135257
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10864314.587176828, tolerance: 3224.8236814135257
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10345084.699656615, tolerance: 3224.8236814135257
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9847974.664610267, tolerance: 3224.8236814135257
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9380422.144947704, tolerance: 3224.8236814135257
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8947015.008946361, tolerance: 3224.8236814135257
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8549670.258610543, tolerance: 3224.8236814135257
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8188124.10139697, tolerance: 3224.8236814135257
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7860558.677097192, tolerance: 3224.8236814135257
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7564216.251072427, tolerance: 3224.8236814135257
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7295907.831051516, tolerance: 3224.8236814135257
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7052382.339382409, tolerance: 3224.8236814135257
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6830565.953165622, tolerance: 3224.8236814135257
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6627701.8718035035, tolerance: 3224.8236814135257
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6441421.548990736, tolerance: 3224.8236814135257
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6269768.62995563, tolerance: 3224.8236814135257
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6111186.722532666, tolerance: 3224.8236814135257
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5964477.842252123, tolerance: 3224.8236814135257
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5828739.876908911, tolerance: 3224.8236814135257
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5703294.550898061, tolerance: 3224.8236814135257
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5587617.998651207, tolerance: 3224.8236814135257
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5481282.987854582, tolerance: 3224.8236814135257
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5383916.678079109, tolerance: 3224.8236814135257
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5295172.882818868, tolerance: 3224.8236814135257
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5214714.536832929, tolerance: 3224.8236814135257
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5142200.898831959, tolerance: 3224.8236814135257
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5077274.992035763, tolerance: 3224.8236814135257
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5019549.576235894, tolerance: 3224.8236814135257
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4968593.444998827, tolerance: 3224.8236814135257
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4923922.319001401, tolerance: 3224.8236814135257
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4884998.717484744, tolerance: 3224.8236814135257
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4851242.938445843, tolerance: 3224.8236814135257
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4822053.963705294, tolerance: 3224.8236814135257
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4796836.33933863, tolerance: 3224.8236814135257
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4775027.808895538, tolerance: 3224.8236814135257
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4756122.72319162, tolerance: 3224.8236814135257
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4739687.53359313, tolerance: 3224.8236814135257
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4725366.495343782, tolerance: 3224.8236814135257
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4712877.711579464, tolerance: 3224.8236814135257
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4702001.540622867, tolerance: 3224.8236814135257
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4692564.773319093, tolerance: 3224.8236814135257
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4684424.413915356, tolerance: 3224.8236814135257
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4677454.213645134, tolerance: 3224.8236814135257
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4671535.662147429, tolerance: 3224.8236814135257
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4666553.5814065635, tolerance: 3224.8236814135257
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4662395.341810594, tolerance: 3224.8236814135257
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4658952.253038291, tolerance: 3224.8236814135257
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4656121.776082109, tolerance: 3224.8236814135257
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4653809.60003749, tolerance: 3224.8236814135257
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4651931.081491574, tolerance: 3224.8236814135257
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4650411.905960143, tolerance: 3224.8236814135257
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4649188.052145973, tolerance: 3224.8236814135257
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4648205.237516068, tolerance: 3224.8236814135257
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4647418.03879132, tolerance: 3224.8236814135257
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:664: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4646788.852992648, tolerance: 3224.8236814135257
model = cd_fast.enet_coordinate_descent_gram(
/root/anaconda3/envs/pypy/lib/python3.10/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.153e+07, tolerance: 3.855e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.
model = cd_fast.enet_coordinate_descent(-results['test_score']array([132393.84003227])- Lasso 회귀분석
import sklearn.preprocessing as skp
scaler = skp.StandardScaler()
lassoCV = skl.ElasticNetCV(n_alphas=100, l1_ratio=1, cv=kfold)
pipeCV = Pipeline(steps=[('scaler', scaler), ('lasso', lassoCV)])
pipeCV.fit(X, Y)
tuned_lasso = pipeCV.named_steps['lasso']
tuned_lasso.alpha_2.9351237625863815lambdas, soln_array = skl.Lasso.path(Xs, Y, l1_ratio=1, n_alphas = 100)[:2]
soln_path = pd.DataFrame(soln_array.T, columns=X.columns[1:], index=-np.log(lambdas))
path_fig , ax = subplots(figsize=(8,8))
soln_path.plot(ax=ax, legend=False)
ax.legend(loc='upper left')
ax.set_xlabel('$-\log(\lambda)$', fontsize=20)
ax.set_ylabel('Standardized coefficiients', fontsize=20);
np.min(tuned_lasso.mse_path_.mean(1))111643.39348122121lassoCV_fig , ax = subplots(figsize=(8,8))
ax.errorbar(-np.log(tuned_lasso.alphas_),
tuned_lasso.mse_path_.mean(1),
yerr=tuned_lasso.mse_path_.std(1) / np.sqrt(K))
ax.axvline(-np.log(tuned_lasso.alpha_), c='k', ls='--')
ax.set_ylim ([50000 ,250000])
ax.set_xlabel('$-\log(\lambda)$', fontsize=20)
ax.set_ylabel('Cross -validated MSE', fontsize=20);
- PCA 회귀분석
- 주성분 분석을 통해서 얻은 주성분을 이용해 회귀분석 시행
- 여기서 주성분은 입력 변수 차원에서만 얻는 것임에 주의
pca = PCA(n_components=2)
linreg = skl.LinearRegression()
pipe = Pipeline([('pca', pca), ('linreg', linreg)])
pipe.fit(X, Y)
pipe.named_steps['linreg'].coef_ # PC 2개로 회귀분석을 진행array([0.09846131, 0.4758765 ])pipe = Pipeline([('scaler', scaler),
('pca', pca),
('linreg', linreg)])
pipe.fit(X, Y)
pipe.named_steps['linreg'].coef_ # 표준화 이후 회귀분석을 진행array([106.36859204, -21.60350456])param_grid = {'pca__n_components': range(1, 20)}
grid = skm.GridSearchCV(pipe, param_grid, cv=kfold, scoring='neg_mean_squared_error')
grid.fit(X, Y)GridSearchCV(cv=KFold(n_splits=10, random_state=0, shuffle=True),
estimator=Pipeline(steps=[('scaler', StandardScaler()),
('pca', PCA(n_components=2)),
('linreg', LinearRegression())]),
param_grid={'pca__n_components': range(1, 20)},
scoring='neg_mean_squared_error')In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
GridSearchCV(cv=KFold(n_splits=10, random_state=0, shuffle=True),
estimator=Pipeline(steps=[('scaler', StandardScaler()),
('pca', PCA(n_components=2)),
('linreg', LinearRegression())]),
param_grid={'pca__n_components': range(1, 20)},
scoring='neg_mean_squared_error')Pipeline(steps=[('scaler', StandardScaler()), ('pca', PCA(n_components=2)),
('linreg', LinearRegression())])StandardScaler()
PCA(n_components=2)
LinearRegression()
- 교차 검증 오차를 통해 어느 정도의 주성분 개수를 선택해야 하는지를 결정하려고 함
- 주성분 개수가 3개 이상으로 내려가면서 큰 변화가 없음을 알 수 있음
pcr_fig , ax = subplots(figsize=(8,8))
n_comp = param_grid['pca__n_components']
ax.errorbar(n_comp, -grid.cv_results_['mean_test_score'], grid.cv_results_['std_test_score'] / np.sqrt(K))
ax.set_ylabel('Cross -validated MSE', fontsize=20)
ax.set_xlabel('# principal components', fontsize=20)
ax.set_xticks(n_comp [::2])
ax.set_ylim ([50000 ,250000])
pipe.named_steps['pca'].explained_variance_ratio_array([0.3831424 , 0.21841076])- PLS (Partial Least Squares Regression)
- $ X = T P^{} + E, Y= U Q^{} + F$ 구조를 가정
- 두 변수 간의 상관관계를 이용해서 \(T,P,U, Q\)를 알아냄
- 여기에서 각 변수들의 차원을 조정해야 하는 문제가 발생, 즉 \(T\)의 열수를 어떻게 해야 하는가 하는 문제가 있음
pls = PLSRegression(n_components=2, scale=True)
pls.fit(X, Y)
param_grid = {'n_components':range(1, 20)}
grid = skm.GridSearchCV(pls, param_grid, cv=kfold, scoring='neg_mean_squared_error')
grid.fit(X, Y)GridSearchCV(cv=KFold(n_splits=10, random_state=0, shuffle=True),
estimator=PLSRegression(),
param_grid={'n_components': range(1, 20)},
scoring='neg_mean_squared_error')In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
GridSearchCV(cv=KFold(n_splits=10, random_state=0, shuffle=True),
estimator=PLSRegression(),
param_grid={'n_components': range(1, 20)},
scoring='neg_mean_squared_error')PLSRegression()
PLSRegression()
- 교차 검증 오차를 통해서 어떻게 차원을 결정해야 하는지를 검토할 수 있음
pls_fig , ax = subplots(figsize=(8,8))
n_comp = param_grid['n_components']
ax.errorbar(n_comp ,
-grid.cv_results_['mean_test_score'],
grid.cv_results_['std_test_score'] / np.sqrt(K))
ax.set_ylabel('Cross -validated MSE', fontsize=20)
ax.set_xlabel('# principal components', fontsize=20)
ax.set_xticks(n_comp [::2])
ax.set_ylim ([50000 ,250000]);