[scikit-learn] logistic regression results are not stable between solvers
Benoît Presles
benoit.presles at u-bourgogne.fr
Tue Oct 8 14:19:50 EDT 2019
As you can notice in the code below, I do scale the data. I do not get any convergence warning and moreover I always have n_iter_ < max_iter.
> Le 8 oct. 2019 à 19:51, Andreas Mueller <t3kcit at gmail.com> a écrit :
>
> I'm pretty sure SAGA is not converging. Unless you scale the data, SAGA is very slow to converge.
>
>> On 10/8/19 7:19 PM, Benoît Presles wrote:
>> 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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