def test_set_feature_union_steps(): mult2 = Mult(2) mult2.get_feature_names = lambda: ['x2'] mult3 = Mult(3) mult3.get_feature_names = lambda: ['x3'] mult5 = Mult(5) mult5.get_feature_names = lambda: ['x5'] ft = FeatureUnion([('m2', mult2), ('m3', mult3)]) assert_array_equal([[2, 3]], ft.transform(np.asarray([[1]]))) assert ['m2__x2', 'm3__x3'] == ft.get_feature_names() # Directly setting attr ft.transformer_list = [('m5', mult5)] assert_array_equal([[5]], ft.transform(np.asarray([[1]]))) assert ['m5__x5'] == ft.get_feature_names() # Using set_params ft.set_params(transformer_list=[('mock', mult3)]) assert_array_equal([[3]], ft.transform(np.asarray([[1]]))) assert ['mock__x3'] == ft.get_feature_names() # Using set_params to replace single step ft.set_params(mock=mult5) assert_array_equal([[5]], ft.transform(np.asarray([[1]]))) assert ['mock__x5'] == ft.get_feature_names()
def test_feature_union_weights(): # test feature union with transformer weights X = iris.data y = iris.target pca = PCA(n_components=2, svd_solver='randomized', random_state=0) select = SelectKBest(k=1) # test using fit followed by transform fs = FeatureUnion([("pca", pca), ("select", select)], transformer_weights={"pca": 10}) fs.fit(X, y) X_transformed = fs.transform(X) # test using fit_transform fs = FeatureUnion([("pca", pca), ("select", select)], transformer_weights={"pca": 10}) X_fit_transformed = fs.fit_transform(X, y) # test it works with transformers missing fit_transform fs = FeatureUnion([("mock", Transf()), ("pca", pca), ("select", select)], transformer_weights={"mock": 10}) X_fit_transformed_wo_method = fs.fit_transform(X, y) # check against expected result # We use a different pca object to control the random_state stream assert_array_almost_equal(X_transformed[:, :-1], 10 * pca.fit_transform(X)) assert_array_equal(X_transformed[:, -1], select.fit_transform(X, y).ravel()) assert_array_almost_equal(X_fit_transformed[:, :-1], 10 * pca.fit_transform(X)) assert_array_equal(X_fit_transformed[:, -1], select.fit_transform(X, y).ravel()) assert X_fit_transformed_wo_method.shape == (X.shape[0], 7)
def test_feature_union_parallel(): # test that n_jobs work for FeatureUnion X = JUNK_FOOD_DOCS fs = FeatureUnion([ ("words", CountVectorizer(analyzer='word')), ("chars", CountVectorizer(analyzer='char')), ]) fs_parallel = FeatureUnion([ ("words", CountVectorizer(analyzer='word')), ("chars", CountVectorizer(analyzer='char')), ], n_jobs=2) fs_parallel2 = FeatureUnion([ ("words", CountVectorizer(analyzer='word')), ("chars", CountVectorizer(analyzer='char')), ], n_jobs=2) fs.fit(X) X_transformed = fs.transform(X) assert X_transformed.shape[0] == len(X) fs_parallel.fit(X) X_transformed_parallel = fs_parallel.transform(X) assert X_transformed.shape == X_transformed_parallel.shape assert_array_equal(X_transformed.toarray(), X_transformed_parallel.toarray()) # fit_transform should behave the same X_transformed_parallel2 = fs_parallel2.fit_transform(X) assert_array_equal(X_transformed.toarray(), X_transformed_parallel2.toarray()) # transformers should stay fit after fit_transform X_transformed_parallel2 = fs_parallel2.transform(X) assert_array_equal(X_transformed.toarray(), X_transformed_parallel2.toarray())
def test_feature_union(): # basic sanity check for feature union X = iris.data X -= X.mean(axis=0) y = iris.target svd = TruncatedSVD(n_components=2, random_state=0) select = SelectKBest(k=1) fs = FeatureUnion([("svd", svd), ("select", select)]) fs.fit(X, y) X_transformed = fs.transform(X) assert X_transformed.shape == (X.shape[0], 3) # check if it does the expected thing assert_array_almost_equal(X_transformed[:, :-1], svd.fit_transform(X)) assert_array_equal(X_transformed[:, -1], select.fit_transform(X, y).ravel()) # test if it also works for sparse input # We use a different svd object to control the random_state stream fs = FeatureUnion([("svd", svd), ("select", select)]) X_sp = sparse.csr_matrix(X) X_sp_transformed = fs.fit_transform(X_sp, y) assert_array_almost_equal(X_transformed, X_sp_transformed.toarray()) # Test clone fs2 = assert_no_warnings(clone, fs) assert fs.transformer_list[0][1] is not fs2.transformer_list[0][1] # test setting parameters fs.set_params(select__k=2) assert fs.fit_transform(X, y).shape == (X.shape[0], 4) # test it works with transformers missing fit_transform fs = FeatureUnion([("mock", Transf()), ("svd", svd), ("select", select)]) X_transformed = fs.fit_transform(X, y) assert X_transformed.shape == (X.shape[0], 8) # test error if some elements do not support transform assert_raises_regex( TypeError, 'All estimators should implement fit and ' 'transform.*\\bNoTrans\\b', FeatureUnion, [("transform", Transf()), ("no_transform", NoTrans())]) # test that init accepts tuples fs = FeatureUnion((("svd", svd), ("select", select))) fs.fit(X, y)