def test_digits_sqrt_lazy(): model = FeatureBasedSelection(100, 'sqrt', optimizer='lazy') model.fit(X_digits) assert_array_equal(model.ranking, digits_sqrt_ranking) assert_array_almost_equal(model.gains, digits_sqrt_gains, 4) assert_array_almost_equal(model.subset, X_digits[model.ranking])
def test_digits_sigmoid_two_stage(): model = FeatureBasedSelection(100, 'sigmoid', optimizer='two-stage') model.fit(X_digits) assert_array_equal(model.ranking, digits_sigmoid_ranking) assert_array_almost_equal(model.gains, digits_sigmoid_gains, 4) assert_array_almost_equal(model.subset, X_digits[model.ranking])
def test_digits_inverse_large_greedy(): model = FeatureBasedSelection(100, 'inverse', 100) model.fit(X_digits) assert_array_equal(model.ranking, digits_inverse_ranking) assert_array_almost_equal(model.gains, digits_inverse_gains, 4)
def test_digits_sqrt_select_biggest_first(): model = FeatureBasedSelection(10, 'sqrt', 10) model.fit(X_digits) assert_equal(model.ranking[0], int(X_digits.sum(axis=1).argmax()))
def test_digits_inverse_small_greedy(): model = FeatureBasedSelection(10, 'inverse', 10) model.fit(X_digits) assert_array_equal(model.ranking, digits_inverse_ranking[:10]) assert_array_almost_equal(model.gains, digits_inverse_gains[:10], 4)