sklearn.linear_model.LogisticRegression returns different coefficients every time although random_state is set

Question

I'm fitting a logistic regression model and am setting the random state to a fixed value.

Every time I do a "fit" I get different coefficients, example:

classifier_instance.fit(train_examples_features, train_examples_labels)
LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,
          intercept_scaling=1, penalty='l2', random_state=1, tol=0.0001)

>>> classifier_instance.raw_coef_
array([[ 0.071101940040772596  ,  0.05143724979709707323,  0.071101940040772596  , -0.04089477198935181912, -0.0407380696457252528 ,  0.03622160087086594843,  0.01055345545606742319,
         0.01071861708285645406, -0.36248634699444892693, -0.06159019047096317423,  0.02370064668025737009,  0.02370064668025737009, -0.03159781822495803805,  0.11221150783553821006,
         0.02728295348681779309,  0.071101940040772596  ,  0.071101940040772596  ,  0.                    ,  0.10882033432637286396,  0.64630314505709030026,  0.09617956519989406816,
         0.0604133873444507169 ,  0.                    ,  0.04111685986987245051,  0.                    ,  0.                    ,  0.18312324521915510078,  0.071101940040772596  ,
         0.071101940040772596  ,  0.                    , -0.59561802045324663268, -0.61490898457874587635,  1.07812569991461248975,  0.071101940040772596  ]])

classifier_instance.fit(train_examples_features, train_examples_labels)
LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,
          intercept_scaling=1, penalty='l2', random_state=1, tol=0.0001)

>>> classifier_instance.raw_coef_
array([[ 0.07110193825129411394,  0.05143724970282205489,  0.07110193825129411394, -0.04089477178162870957, -0.04073806899140903354,  0.03622160048165772028,  0.010553455400928528  ,
         0.01071860364222424096, -0.36248635488413910588, -0.06159021545062405567,  0.02370064608376460866,  0.02370064608376460866, -0.03159783710841745225,  0.11221149816037970237,
         0.02728295411479400578,  0.07110193825129411394,  0.07110193825129411394,  0.                    ,  0.10882033461822394893,  0.64630314701686075729,  0.09617956493834901865,
         0.06041338563697066372,  0.                    ,  0.04111676713793514099,  0.                    ,  0.                    ,  0.18312324401049043243,  0.07110193825129411394,
         0.07110193825129411394,  0.                    , -0.59561803345113684127, -0.61490899867901249731,  1.07812569539027203191,  0.07110193825129411394]])

I'm using version 0.14, the docs specify "The underlying C implementation uses a random number generator to select features when fitting the model. It is thus not uncommon, to have slightly different results for the same input data. If that happens, try with a smaller tol parameter."

I thought that setting the random state would make sure there is no randomness but apparently this is not the case. Is this a bug or desired behavior?

Answer 1

It's not really desired, but it's a known issue that is very hard to fix. The thing is that LogisticRegression models are trained with Liblinear, which does not allow setting its random seed in a completely robust way. When you explicitly set the random_state, a best effort is made to set Liblinear's random seed, but that may fail.