[scikit-learn] logistic regression results are not stable between solvers
Andreas Mueller
t3kcit at gmail.com
Wed Jan 8 15:53:47 EST 2020
Hi Ben.
Liblinear and l-bfgs might both converge but to different solutions,
given that the intercept is penalized.
There is also problems with ill-conditioned problems that are hard to
detect.
My impression of SAGA was that the convergence checks are too loose and
we should improve them.
Have you checked the objective of the l-bfgs and liblinear solvers? With
ill-conditioned data the objectives could be similar with different
solutions.
It's not intended for scikit-learn to warn about ill-conditioned
problems, I think, only convergence issues.
Hth,
Andy
On 1/8/20 3:31 PM, Benoît Presles wrote:
> With lbfgs n_iter_ = 48, with saga n_iter_ = 326581, with liblinear
> n_iter_ = 64.
>
>
> On 08/01/2020 21:18, Guillaume Lemaître wrote:
>> We issue convergence warning. Can you check n_iter to be sure that
>> you did not convergence to the stated convergence?
>>
>> On Wed, 8 Jan 2020 at 20:53, Benoît Presles
>> <benoit.presles at u-bourgogne.fr
>> <mailto:benoit.presles at u-bourgogne.fr>> wrote:
>>
>> Dear sklearn users,
>>
>> I still have some issues concerning logistic regression.
>> I did compare on the same data (simulated data) sklearn with
>> three different solvers (lbfgs, saga, liblinear) and statsmodels.
>>
>> When everything goes well, I get the same results between lbfgs,
>> saga, liblinear and statsmodels. When everything goes wrong, all
>> the results are different.
>>
>> In fact, when everything goes wrong, statsmodels gives me a
>> convergence warning (Warning: Maximum number of iterations has
>> been exceeded. Current function value: inf Iterations: 20000) +
>> an error (numpy.linalg.LinAlgError: Singular matrix).
>>
>> Why sklearn does not tell me anything? How can I know that I have
>> convergence issues with sklearn?
>>
>>
>> Thanks for your help,
>> 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
>> import statsmodels.api as sm
>> #
>> RANDOM_SEED = 2
>> #
>> X_sim, y_sim = make_classification(n_samples=200,
>> n_features=20,
>> 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=0, random_state=RANDOM_SEED, C=1e9,
>> solver='lbfgs',
>> penalty='none', tol=1e-6)
>> classifier_lbfgs.fit(X_split_train, y_split_train)
>> print('classifier lbfgs iter:', classifier_lbfgs.n_iter_)
>> print(classifier_lbfgs.intercept_)
>> print(classifier_lbfgs.coef_)
>> #
>> classifier_saga = LogisticRegression(fit_intercept=True,
>> max_iter=20000000, verbose=0, random_state=RANDOM_SEED, C=1e9,
>> solver='saga',
>> penalty='none', tol=1e-6)
>> classifier_saga.fit(X_split_train, y_split_train)
>> print('classifier saga iter:', classifier_saga.n_iter_)
>> print(classifier_saga.intercept_)
>> print(classifier_saga.coef_)
>> #
>> classifier_liblinear = LogisticRegression(fit_intercept=True,
>> max_iter=20000000, verbose=0, random_state=RANDOM_SEED,
>> C=1e9,
>> solver='liblinear', penalty='l2', tol=1e-6)
>> classifier_liblinear.fit(X_split_train, y_split_train)
>> print('classifier liblinear iter:', classifier_liblinear.n_iter_)
>> print(classifier_liblinear.intercept_)
>> print(classifier_liblinear.coef_)
>> # statsmodels
>> logit = sm.Logit(y_split_train,
>> sm.tools.add_constant(X_split_train))
>> logit_res = logit.fit(maxiter=20000)
>> print("Coef statsmodels")
>> print(logit_res.params)
>>
>>
>>
>> On 11/10/2019 15:42, Andreas Mueller wrote:
>>>
>>>
>>> On 10/10/19 1:14 PM, Benoît Presles wrote:
>>>>
>>>> Thanks for your answers.
>>>>
>>>> On my real data, I do not have so many samples. I have a bit
>>>> more than 200 samples in total and I also would like to get
>>>> some results with unpenalized logisitic regression.
>>>> What do you suggest? Should I switch to the lbfgs solver?
>>> Yes.
>>>> Am I sure that with this solver I will not have any convergence
>>>> issue and always get the good result? Indeed, I did not get any
>>>> convergence warning with saga, so I thought everything was
>>>> fine. I noticed some issues only when I decided to test several
>>>> solvers. Without comparing the results across solvers, how to
>>>> be sure that the optimisation goes well? Shouldn't scikit-learn
>>>> warn the user somehow if it is not the case?
>>> We should attempt to warn in the SAGA solver if it doesn't
>>> converge. That it doesn't raise a convergence warning should
>>> probably be considered a bug.
>>> It uses the maximum weight change as a stopping criterion right now.
>>> We could probably compute the dual objective once in the end to
>>> see if we converged, right? Or is that not possible with SAGA?
>>> If not, we might want to caution that no convergence warning
>>> will be raised.
>>>
>>>>
>>>> At last, I was using saga because I also wanted to do some
>>>> feature selection by using l1 penalty which is not supported by
>>>> lbfgs...
>>> You can use liblinear then.
>>>
>>>
>>>>
>>>> Best regards,
>>>> Ben
>>>>
>>>>
>>>> Le 09/10/2019 à 23:39, Guillaume Lemaître a écrit :
>>>>> Ups I did not see the answer of Roman. Sorry about that. It is
>>>>> coming back to the same conclusion :)
>>>>>
>>>>> On Wed, 9 Oct 2019 at 23:37, Guillaume Lemaître
>>>>> <g.lemaitre58 at gmail.com <mailto:g.lemaitre58 at gmail.com>> wrote:
>>>>>
>>>>> Uhm actually increasing to 10000 samples solve the
>>>>> convergence issue.
>>>>> SAGA is not designed to work with a so small sample size
>>>>> most probably.
>>>>>
>>>>> On Wed, 9 Oct 2019 at 23:36, Guillaume Lemaître
>>>>> <g.lemaitre58 at gmail.com <mailto:g.lemaitre58 at gmail.com>>
>>>>> wrote:
>>>>>
>>>>> I slightly change the bench such that it uses pipeline
>>>>> and plotted the coefficient:
>>>>>
>>>>> https://gist.github.com/glemaitre/8fcc24bdfc7dc38ca0c09c56e26b9386
>>>>>
>>>>> I only see one of the 10 splits where SAGA is not
>>>>> converging, otherwise the coefficients
>>>>> look very close (I don't attach the figure here but
>>>>> they can be plotted using the snippet).
>>>>> So apart from this second split, the other differences
>>>>> seems to be numerical instability.
>>>>>
>>>>> Where I have some concern is regarding the convergence
>>>>> rate of SAGA but I have no
>>>>> intuition to know if this is normal or not.
>>>>>
>>>>> On Wed, 9 Oct 2019 at 23:22, Roman Yurchak
>>>>> <rth.yurchak at gmail.com <mailto:rth.yurchak at gmail.com>>
>>>>> wrote:
>>>>>
>>>>> Ben,
>>>>>
>>>>> I can confirm your results with penalty='none' and
>>>>> C=1e9. In both cases,
>>>>> you are running a mostly unpenalized logisitic
>>>>> regression. Usually
>>>>> that's less numerically stable than with a small
>>>>> regularization,
>>>>> depending on the data collinearity.
>>>>>
>>>>> Running that same code with
>>>>> - larger penalty ( smaller C values)
>>>>> - or larger number of samples
>>>>> yields for me the same coefficients (up to some
>>>>> tolerance).
>>>>>
>>>>> You can also see that SAGA convergence is not good
>>>>> by the fact that it
>>>>> needs 196000 epochs/iterations to converge.
>>>>>
>>>>> Actually, I have often seen convergence issues
>>>>> with SAG on small
>>>>> datasets (in unit tests), not fully sure why.
>>>>>
>>>>> --
>>>>> Roman
>>>>>
>>>>> On 09/10/2019 22:10, serafim loukas wrote:
>>>>> > The predictions across solver are exactly the
>>>>> same when I run the code.
>>>>> > I am using 0.21.3 version. What is yours?
>>>>> >
>>>>> >
>>>>> > In [13]: import sklearn
>>>>> >
>>>>> > In [14]: sklearn.__version__
>>>>> > Out[14]: '0.21.3'
>>>>> >
>>>>> >
>>>>> > Serafeim
>>>>> >
>>>>> >
>>>>> >
>>>>> >> On 9 Oct 2019, at 21:44, Benoît Presles
>>>>> <benoit.presles at u-bourgogne.fr
>>>>> <mailto:benoit.presles at u-bourgogne.fr>
>>>>> >> <mailto:benoit.presles at u-bourgogne.fr
>>>>> <mailto:benoit.presles at u-bourgogne.fr>>> wrote:
>>>>> >>
>>>>> >> (y_pred_lbfgs==y_pred_saga).all() == False
>>>>> >
>>>>> >
>>>>> > _______________________________________________
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>>>>> <mailto:scikit-learn at python.org>
>>>>> >
>>>>> https://mail.python.org/mailman/listinfo/scikit-learn
>>>>> >
>>>>>
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>>>>>
>>>>>
>>>>>
>>>>> --
>>>>> Guillaume Lemaitre
>>>>> Scikit-learn @ Inria Foundation
>>>>> https://glemaitre.github.io/
>>>>>
>>>>>
>>>>>
>>>>> --
>>>>> Guillaume Lemaitre
>>>>> Scikit-learn @ Inria Foundation
>>>>> https://glemaitre.github.io/
>>>>>
>>>>>
>>>>>
>>>>> --
>>>>> Guillaume Lemaitre
>>>>> Scikit-learn @ Inria Foundation
>>>>> https://glemaitre.github.io/
>>>>>
>>>>> _______________________________________________
>>>>> scikit-learn mailing list
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>>>>> https://mail.python.org/mailman/listinfo/scikit-learn
>>>>
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>>
>> --
>> Guillaume Lemaitre
>> Scikit-learn @ Inria Foundation
>> https://glemaitre.github.io/
>>
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