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.pres...@u-bourgogne.fr
<mailto:benoit.pres...@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.lemaitr...@gmail.com <mailto:g.lemaitr...@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.lemaitr...@gmail.com <mailto:g.lemaitr...@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.yurc...@gmail.com <mailto:rth.yurc...@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.pres...@u-bourgogne.fr
<mailto:benoit.pres...@u-bourgogne.fr>
>> <mailto:benoit.pres...@u-bourgogne.fr
<mailto:benoit.pres...@u-bourgogne.fr>>> wrote:
>>
>> (y_pred_lbfgs==y_pred_saga).all() == False
>
>
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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/
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