you should always set the random state in liblinear



Lars Buitinck <notifications@github.com> schrieb:

>> +    target = iris.target_names[iris.target]
>> +    clf = logistic.LogisticRegression().fit(iris.data, target)
>> +
>> +    pred_d_d = clf.decision_function(iris.data)
>> +
>> +    clf.sparsify()
>> +    assert_true(sp.issparse(clf.coef_))
>> +    pred_s_d = clf.decision_function(iris.data)
>> +
>> +    sp_data = sp.coo_matrix(iris.data)
>> +    pred_s_s = clf.decision_function(sp_data)
>> +
>> +    clf.densify()
>> +    pred_d_s = clf.decision_function(sp_data)
>> +
>> +    assert_array_equal(pred_d_d, pred_s_d)
>
>Also, if you hand `tol=1e-5` (or lower) to the classifier's
>constructor, does that prevent the test from failing?
>
>---
>Reply to this email directly or view it on GitHub:
>https://github.com/scikit-learn/scikit-learn/pull/1706/files#r3172922

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