[scikit-learn] logistic regression results are not stable between solvers

[Guillaume Lemaître]

Could you generate more samples, set penalty to none, reduce the tolerance and check the coefficients instead of predictions. This is sure to be sure that this is not only a numerical error. 




Sent from my phone - sorry to be brief and potential misspell.



	  Original Message  



From: benoit.presles at u-bourgogne.fr
Sent: 8 October 2019 20:27
To: scikit-learn at python.org
Reply to: scikit-learn at python.org
Subject: [scikit-learn] logistic regression results are not stable between solvers


Dear scikit-learn users,

I am using logistic regression to make some predictions. On my own data,
I do not get the same results between solvers. I managed to reproduce
this issue on synthetic data (see the code below).
All solvers seem to converge (n_iter_ < max_iter), so why do I get
different results?
If results between solvers are not stable, which one to choose?


Best regards,
Ben

------------------------------------------

Here is the code I used to generate synthetic data:

from sklearn.datasets import make_classification
from sklearn.model_selection import StratifiedShuffleSplit
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LogisticRegression
#
RANDOM_SEED = 2
#
X_sim, y_sim = make_classification(n_samples=200,
                           n_features=45,
                           n_informative=10,
                           n_redundant=0,
                           n_repeated=0,
                           n_classes=2,
                           n_clusters_per_class=1,
                           random_state=RANDOM_SEED,
                           shuffle=False)
#
sss = StratifiedShuffleSplit(n_splits=10, test_size=0.2,
random_state=RANDOM_SEED)
for train_index_split, test_index_split in sss.split(X_sim, y_sim):
    X_split_train, X_split_test = X_sim[train_index_split],
X_sim[test_index_split]
    y_split_train, y_split_test = y_sim[train_index_split],
y_sim[test_index_split]
    ss = StandardScaler()
    X_split_train = ss.fit_transform(X_split_train)
    X_split_test = ss.transform(X_split_test)
    #
    classifier_lbfgs = LogisticRegression(fit_intercept=True,
max_iter=20000000, verbose=1, random_state=RANDOM_SEED, C=1e9,
                                    solver='lbfgs')
    classifier_lbfgs.fit(X_split_train, y_split_train)
    print('classifier lbfgs iter:',  classifier_lbfgs.n_iter_)
    classifier_saga = LogisticRegression(fit_intercept=True,
max_iter=20000000, verbose=1, random_state=RANDOM_SEED, C=1e9,
                                    solver='saga')
    classifier_saga.fit(X_split_train, y_split_train)
    print('classifier saga iter:', classifier_saga.n_iter_)
    #
    y_pred_lbfgs = classifier_lbfgs.predict(X_split_test)
    y_pred_saga  = classifier_saga.predict(X_split_test)
    #
    if (y_pred_lbfgs==y_pred_saga).all() == False:
        print('lbfgs does not give the same results as saga :-( !')
        exit()

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