def test_digits_greedi_ln_sparse(): model1 = FeatureBasedSelection(100, 'sqrt') model2 = FeatureBasedSelection(100, 'log') model = MixtureSelection(100, [model1, model2], [1.0, 0.3], optimizer='greedi', optimizer_kwds={ 'optimizer1': 'lazy', 'optimizer2': 'naive' }, random_state=0) model.fit(X_digits_sparse) assert_array_equal(model.ranking[:85], digits_greedi_ranking[:85]) assert_array_almost_equal(model.gains[:85], digits_greedi_gains[:85], 4) assert_array_almost_equal(model.subset, X_digits_sparse[model.ranking].toarray())
def test_digits_sqrt_sieve_minibatch(): model1 = FeatureBasedSelection(100, 'sqrt') model2 = FeatureBasedSelection(100, 'log') model = MixtureSelection(100, [model1, model2], [1.0, 0.3], random_state=0) model.partial_fit(X_digits[:300]) model.partial_fit(X_digits[300:500]) model.partial_fit(X_digits[500:]) assert_array_equal(model.ranking, digits_sieve_ranking) assert_array_almost_equal(model.gains, digits_sieve_gains, 4) assert_array_almost_equal(model.subset, X_digits[model.ranking])