""" Test the ColumnTransformer. """ import numpy as np from scipy import sparse import pytest from sklearn.utils.testing import assert_raises from sklearn.utils.testing import assert_raise_message from sklearn.utils.testing import assert_equal from sklearn.utils.testing import assert_false from sklearn.utils.testing import assert_dict_equal from sklearn.utils.testing import assert_array_equal from sklearn.utils.testing import assert_allclose_dense_sparse from sklearn.utils.testing import assert_almost_equal from sklearn.base import BaseEstimator from sklearn.externals import six from sklearn.compose import ColumnTransformer, make_column_transformer from sklearn.exceptions import NotFittedError, DataConversionWarning from sklearn.preprocessing import StandardScaler, Normalizer, OneHotEncoder from sklearn.feature_extraction import DictVectorizer class Trans(BaseEstimator): def fit(self, X, y=None): return self def transform(self, X, y=None): # 1D Series -> 2D DataFrame if hasattr(X, 'to_frame'): return X.to_frame() # 1D array -> 2D array if X.ndim == 1: return np.atleast_2d(X).T return X class DoubleTrans(BaseEstimator): def fit(self, X, y=None): return self def transform(self, X): return 2*X class SparseMatrixTrans(BaseEstimator): def fit(self, X, y=None): return self def transform(self, X, y=None): n_samples = len(X) return sparse.eye(n_samples, n_samples).tocsr() class TransNo2D(BaseEstimator): def fit(self, X, y=None): return self def transform(self, X, y=None): return X class TransRaise(BaseEstimator): def fit(self, X, y=None): raise ValueError("specific message") def transform(self, X, y=None): raise ValueError("specific message") def test_column_transformer(): X_array = np.array([[0, 1, 2], [2, 4, 6]]).T X_res_first1D = np.array([0, 1, 2]) X_res_second1D = np.array([2, 4, 6]) X_res_first = X_res_first1D.reshape(-1, 1) X_res_both = X_array cases = [ # single column 1D / 2D (0, X_res_first), ([0], X_res_first), # list-like ([0, 1], X_res_both), (np.array([0, 1]), X_res_both), # slice (slice(0, 1), X_res_first), (slice(0, 2), X_res_both), # boolean mask (np.array([True, False]), X_res_first), ] for selection, res in cases: ct = ColumnTransformer([('trans', Trans(), selection)], remainder='drop') assert_array_equal(ct.fit_transform(X_array), res) assert_array_equal(ct.fit(X_array).transform(X_array), res) # callable that returns any of the allowed specifiers ct = ColumnTransformer([('trans', Trans(), lambda x: selection)], remainder='drop') assert_array_equal(ct.fit_transform(X_array), res) assert_array_equal(ct.fit(X_array).transform(X_array), res) ct = ColumnTransformer([('trans1', Trans(), [0]), ('trans2', Trans(), [1])]) assert_array_equal(ct.fit_transform(X_array), X_res_both) assert_array_equal(ct.fit(X_array).transform(X_array), X_res_both) assert len(ct.transformers_) == 2 # test with transformer_weights transformer_weights = {'trans1': .1, 'trans2': 10} both = ColumnTransformer([('trans1', Trans(), [0]), ('trans2', Trans(), [1])], transformer_weights=transformer_weights) res = np.vstack([transformer_weights['trans1'] * X_res_first1D, transformer_weights['trans2'] * X_res_second1D]).T assert_array_equal(both.fit_transform(X_array), res) assert_array_equal(both.fit(X_array).transform(X_array), res) assert len(both.transformers_) == 2 both = ColumnTransformer([('trans', Trans(), [0, 1])], transformer_weights={'trans': .1}) assert_array_equal(both.fit_transform(X_array), 0.1 * X_res_both) assert_array_equal(both.fit(X_array).transform(X_array), 0.1 * X_res_both) assert len(both.transformers_) == 1 def test_column_transformer_dataframe(): pd = pytest.importorskip('pandas') X_array = np.array([[0, 1, 2], [2, 4, 6]]).T X_df = pd.DataFrame(X_array, columns=['first', 'second']) X_res_first = np.array([0, 1, 2]).reshape(-1, 1) X_res_both = X_array cases = [ # String keys: label based # scalar ('first', X_res_first), # list (['first'], X_res_first), (['first', 'second'], X_res_both), # slice (slice('first', 'second'), X_res_both), # int keys: positional # scalar (0, X_res_first), # list ([0], X_res_first), ([0, 1], X_res_both), (np.array([0, 1]), X_res_both), # slice (slice(0, 1), X_res_first), (slice(0, 2), X_res_both), # boolean mask (np.array([True, False]), X_res_first), (pd.Series([True, False], index=['first', 'second']), X_res_first), ] for selection, res in cases: ct = ColumnTransformer([('trans', Trans(), selection)], remainder='drop') assert_array_equal(ct.fit_transform(X_df), res) assert_array_equal(ct.fit(X_df).transform(X_df), res) # callable that returns any of the allowed specifiers ct = ColumnTransformer([('trans', Trans(), lambda X: selection)], remainder='drop') assert_array_equal(ct.fit_transform(X_df), res) assert_array_equal(ct.fit(X_df).transform(X_df), res) ct = ColumnTransformer([('trans1', Trans(), ['first']), ('trans2', Trans(), ['second'])]) assert_array_equal(ct.fit_transform(X_df), X_res_both) assert_array_equal(ct.fit(X_df).transform(X_df), X_res_both) assert len(ct.transformers_) == 2 assert ct.transformers_[-1][0] != 'remainder' ct = ColumnTransformer([('trans1', Trans(), [0]), ('trans2', Trans(), [1])]) assert_array_equal(ct.fit_transform(X_df), X_res_both) assert_array_equal(ct.fit(X_df).transform(X_df), X_res_both) assert len(ct.transformers_) == 2 assert ct.transformers_[-1][0] != 'remainder' # test with transformer_weights transformer_weights = {'trans1': .1, 'trans2': 10} both = ColumnTransformer([('trans1', Trans(), ['first']), ('trans2', Trans(), ['second'])], transformer_weights=transformer_weights) res = np.vstack([transformer_weights['trans1'] * X_df['first'], transformer_weights['trans2'] * X_df['second']]).T assert_array_equal(both.fit_transform(X_df), res) assert_array_equal(both.fit(X_df).transform(X_df), res) assert len(both.transformers_) == 2 assert ct.transformers_[-1][0] != 'remainder' # test multiple columns both = ColumnTransformer([('trans', Trans(), ['first', 'second'])], transformer_weights={'trans': .1}) assert_array_equal(both.fit_transform(X_df), 0.1 * X_res_both) assert_array_equal(both.fit(X_df).transform(X_df), 0.1 * X_res_both) assert len(both.transformers_) == 1 assert ct.transformers_[-1][0] != 'remainder' both = ColumnTransformer([('trans', Trans(), [0, 1])], transformer_weights={'trans': .1}) assert_array_equal(both.fit_transform(X_df), 0.1 * X_res_both) assert_array_equal(both.fit(X_df).transform(X_df), 0.1 * X_res_both) assert len(both.transformers_) == 1 assert ct.transformers_[-1][0] != 'remainder' # ensure pandas object is passes through class TransAssert(BaseEstimator): def fit(self, X, y=None): return self def transform(self, X, y=None): assert isinstance(X, (pd.DataFrame, pd.Series)) if isinstance(X, pd.Series): X = X.to_frame() return X ct = ColumnTransformer([('trans', TransAssert(), 'first')], remainder='drop') ct.fit_transform(X_df) ct = ColumnTransformer([('trans', TransAssert(), ['first', 'second'])]) ct.fit_transform(X_df) # integer column spec + integer column names -> still use positional X_df2 = X_df.copy() X_df2.columns = [1, 0] ct = ColumnTransformer([('trans', Trans(), 0)], remainder='drop') assert_array_equal(ct.fit_transform(X_df), X_res_first) assert_array_equal(ct.fit(X_df).transform(X_df), X_res_first) assert len(ct.transformers_) == 2 assert ct.transformers_[-1][0] == 'remainder' assert ct.transformers_[-1][1] == 'drop' assert_array_equal(ct.transformers_[-1][2], [1]) @pytest.mark.parametrize("pandas", [True, False], ids=['pandas', 'numpy']) @pytest.mark.parametrize("column", [[], np.array([False, False])], ids=['list', 'bool']) def test_column_transformer_empty_columns(pandas, column): # test case that ensures that the column transformer does also work when # a given transformer doesn't have any columns to work on X_array = np.array([[0, 1, 2], [2, 4, 6]]).T X_res_both = X_array if pandas: pd = pytest.importorskip('pandas') X = pd.DataFrame(X_array, columns=['first', 'second']) else: X = X_array ct = ColumnTransformer([('trans1', Trans(), [0, 1]), ('trans2', Trans(), column)]) assert_array_equal(ct.fit_transform(X), X_res_both) assert_array_equal(ct.fit(X).transform(X), X_res_both) assert len(ct.transformers_) == 2 assert isinstance(ct.transformers_[1][1], Trans) ct = ColumnTransformer([('trans1', Trans(), column), ('trans2', Trans(), [0, 1])]) assert_array_equal(ct.fit_transform(X), X_res_both) assert_array_equal(ct.fit(X).transform(X), X_res_both) assert len(ct.transformers_) == 2 assert isinstance(ct.transformers_[0][1], Trans) ct = ColumnTransformer([('trans', Trans(), column)], remainder='passthrough') assert_array_equal(ct.fit_transform(X), X_res_both) assert_array_equal(ct.fit(X).transform(X), X_res_both) assert len(ct.transformers_) == 2 # including remainder assert isinstance(ct.transformers_[0][1], Trans) fixture = np.array([[], [], []]) ct = ColumnTransformer([('trans', Trans(), column)], remainder='drop') assert_array_equal(ct.fit_transform(X), fixture) assert_array_equal(ct.fit(X).transform(X), fixture) assert len(ct.transformers_) == 2 # including remainder assert isinstance(ct.transformers_[0][1], Trans) def test_column_transformer_sparse_array(): X_sparse = sparse.eye(3, 2).tocsr() # no distinction between 1D and 2D X_res_first = X_sparse[:, 0] X_res_both = X_sparse for col in [0, [0], slice(0, 1)]: for remainder, res in [('drop', X_res_first), ('passthrough', X_res_both)]: ct = ColumnTransformer([('trans', Trans(), col)], remainder=remainder, sparse_threshold=0.8) assert sparse.issparse(ct.fit_transform(X_sparse)) assert_allclose_dense_sparse(ct.fit_transform(X_sparse), res) assert_allclose_dense_sparse(ct.fit(X_sparse).transform(X_sparse), res) for col in [[0, 1], slice(0, 2)]: ct = ColumnTransformer([('trans', Trans(), col)], sparse_threshold=0.8) assert sparse.issparse(ct.fit_transform(X_sparse)) assert_allclose_dense_sparse(ct.fit_transform(X_sparse), X_res_both) assert_allclose_dense_sparse(ct.fit(X_sparse).transform(X_sparse), X_res_both) def test_column_transformer_list(): X_list = [ [1, float('nan'), 'a'], [0, 0, 'b'] ] expected_result = np.array([ [1, float('nan'), 1, 0], [-1, 0, 0, 1], ]) ct = ColumnTransformer([ ('numerical', StandardScaler(), [0, 1]), ('categorical', OneHotEncoder(), [2]), ]) with pytest.warns(DataConversionWarning): # TODO: this warning is not very useful in this case, would be good # to get rid of it assert_array_equal(ct.fit_transform(X_list), expected_result) assert_array_equal(ct.fit(X_list).transform(X_list), expected_result) def test_column_transformer_sparse_stacking(): X_array = np.array([[0, 1, 2], [2, 4, 6]]).T col_trans = ColumnTransformer([('trans1', Trans(), [0]), ('trans2', SparseMatrixTrans(), 1)], sparse_threshold=0.8) col_trans.fit(X_array) X_trans = col_trans.transform(X_array) assert sparse.issparse(X_trans) assert_equal(X_trans.shape, (X_trans.shape[0], X_trans.shape[0] + 1)) assert_array_equal(X_trans.toarray()[:, 1:], np.eye(X_trans.shape[0])) assert len(col_trans.transformers_) == 2 assert col_trans.transformers_[-1][0] != 'remainder' col_trans = ColumnTransformer([('trans1', Trans(), [0]), ('trans2', SparseMatrixTrans(), 1)], sparse_threshold=0.1) col_trans.fit(X_array) X_trans = col_trans.transform(X_array) assert not sparse.issparse(X_trans) assert X_trans.shape == (X_trans.shape[0], X_trans.shape[0] + 1) assert_array_equal(X_trans[:, 1:], np.eye(X_trans.shape[0])) def test_column_transformer_mixed_cols_sparse(): df = np.array([['a', 1, True], ['b', 2, False]], dtype='O') ct = make_column_transformer( (OneHotEncoder(), [0]), ('passthrough', [1, 2]), sparse_threshold=1.0 ) # this shouldn't fail, since boolean can be coerced into a numeric # See: https://github.com/scikit-learn/scikit-learn/issues/11912 X_trans = ct.fit_transform(df) assert X_trans.getformat() == 'csr' assert_array_equal(X_trans.toarray(), np.array([[1, 0, 1, 1], [0, 1, 2, 0]])) ct = make_column_transformer( (OneHotEncoder(), [0]), ('passthrough', [0]), sparse_threshold=1.0 ) with pytest.raises(ValueError, match="For a sparse output, all columns should"): # this fails since strings `a` and `b` cannot be # coerced into a numeric. ct.fit_transform(df) def test_column_transformer_sparse_threshold(): X_array = np.array([['a', 'b'], ['A', 'B']], dtype=object).T # above data has sparsity of 4 / 8 = 0.5 # apply threshold even if all sparse col_trans = ColumnTransformer([('trans1', OneHotEncoder(), [0]), ('trans2', OneHotEncoder(), [1])], sparse_threshold=0.2) res = col_trans.fit_transform(X_array) assert not sparse.issparse(res) assert not col_trans.sparse_output_ # mixed -> sparsity of (4 + 2) / 8 = 0.75 for thres in [0.75001, 1]: col_trans = ColumnTransformer( [('trans1', OneHotEncoder(sparse=True), [0]), ('trans2', OneHotEncoder(sparse=False), [1])], sparse_threshold=thres) res = col_trans.fit_transform(X_array) assert sparse.issparse(res) assert col_trans.sparse_output_ for thres in [0.75, 0]: col_trans = ColumnTransformer( [('trans1', OneHotEncoder(sparse=True), [0]), ('trans2', OneHotEncoder(sparse=False), [1])], sparse_threshold=thres) res = col_trans.fit_transform(X_array) assert not sparse.issparse(res) assert not col_trans.sparse_output_ # if nothing is sparse -> no sparse for thres in [0.33, 0, 1]: col_trans = ColumnTransformer( [('trans1', OneHotEncoder(sparse=False), [0]), ('trans2', OneHotEncoder(sparse=False), [1])], sparse_threshold=thres) res = col_trans.fit_transform(X_array) assert not sparse.issparse(res) assert not col_trans.sparse_output_ def test_column_transformer_error_msg_1D(): X_array = np.array([[0., 1., 2.], [2., 4., 6.]]).T col_trans = ColumnTransformer([('trans', StandardScaler(), 0)]) assert_raise_message(ValueError, "1D data passed to a transformer", col_trans.fit, X_array) assert_raise_message(ValueError, "1D data passed to a transformer", col_trans.fit_transform, X_array) col_trans = ColumnTransformer([('trans', TransRaise(), 0)]) for func in [col_trans.fit, col_trans.fit_transform]: assert_raise_message(ValueError, "specific message", func, X_array) def test_2D_transformer_output(): X_array = np.array([[0, 1, 2], [2, 4, 6]]).T # if one transformer is dropped, test that name is still correct ct = ColumnTransformer([('trans1', 'drop', 0), ('trans2', TransNo2D(), 1)]) assert_raise_message(ValueError, "the 'trans2' transformer should be 2D", ct.fit_transform, X_array) # because fit is also doing transform, this raises already on fit assert_raise_message(ValueError, "the 'trans2' transformer should be 2D", ct.fit, X_array) def test_2D_transformer_output_pandas(): pd = pytest.importorskip('pandas') X_array = np.array([[0, 1, 2], [2, 4, 6]]).T X_df = pd.DataFrame(X_array, columns=['col1', 'col2']) # if one transformer is dropped, test that name is still correct ct = ColumnTransformer([('trans1', TransNo2D(), 'col1')]) assert_raise_message(ValueError, "the 'trans1' transformer should be 2D", ct.fit_transform, X_df) # because fit is also doing transform, this raises already on fit assert_raise_message(ValueError, "the 'trans1' transformer should be 2D", ct.fit, X_df) @pytest.mark.parametrize("remainder", ['drop', 'passthrough']) def test_column_transformer_invalid_columns(remainder): X_array = np.array([[0, 1, 2], [2, 4, 6]]).T # general invalid for col in [1.5, ['string', 1], slice(1, 's'), np.array([1.])]: ct = ColumnTransformer([('trans', Trans(), col)], remainder=remainder) assert_raise_message(ValueError, "No valid specification", ct.fit, X_array) # invalid for arrays for col in ['string', ['string', 'other'], slice('a', 'b')]: ct = ColumnTransformer([('trans', Trans(), col)], remainder=remainder) assert_raise_message(ValueError, "Specifying the columns", ct.fit, X_array) def test_column_transformer_invalid_transformer(): class NoTrans(BaseEstimator): def fit(self, X, y=None): return self def predict(self, X): return X X_array = np.array([[0, 1, 2], [2, 4, 6]]).T ct = ColumnTransformer([('trans', NoTrans(), [0])]) assert_raise_message(TypeError, "All estimators should implement fit", ct.fit, X_array) def test_make_column_transformer(): scaler = StandardScaler() norm = Normalizer() ct = make_column_transformer((scaler, 'first'), (norm, ['second'])) names, transformers, columns = zip(*ct.transformers) assert_equal(names, ("standardscaler", "normalizer")) assert_equal(transformers, (scaler, norm)) assert_equal(columns, ('first', ['second'])) # XXX remove in v0.22 with pytest.warns(DeprecationWarning, match='`make_column_transformer` now expects'): ct1 = make_column_transformer(([0], norm)) ct2 = make_column_transformer((norm, [0])) X_array = np.array([[0, 1, 2], [2, 4, 6]]).T assert_almost_equal(ct1.fit_transform(X_array), ct2.fit_transform(X_array)) with pytest.warns(DeprecationWarning, match='`make_column_transformer` now expects'): make_column_transformer(('first', 'drop')) with pytest.warns(DeprecationWarning, match='`make_column_transformer` now expects'): make_column_transformer(('passthrough', 'passthrough'), ('first', 'drop')) def test_make_column_transformer_pandas(): pd = pytest.importorskip('pandas') X_array = np.array([[0, 1, 2], [2, 4, 6]]).T X_df = pd.DataFrame(X_array, columns=['first', 'second']) norm = Normalizer() # XXX remove in v0.22 with pytest.warns(DeprecationWarning, match='`make_column_transformer` now expects'): ct1 = make_column_transformer((X_df.columns, norm)) ct2 = make_column_transformer((norm, X_df.columns)) assert_almost_equal(ct1.fit_transform(X_df), ct2.fit_transform(X_df)) def test_make_column_transformer_kwargs(): scaler = StandardScaler() norm = Normalizer() ct = make_column_transformer((scaler, 'first'), (norm, ['second']), n_jobs=3, remainder='drop', sparse_threshold=0.5) assert_equal(ct.transformers, make_column_transformer( (scaler, 'first'), (norm, ['second'])).transformers) assert_equal(ct.n_jobs, 3) assert_equal(ct.remainder, 'drop') assert_equal(ct.sparse_threshold, 0.5) # invalid keyword parameters should raise an error message assert_raise_message( TypeError, 'Unknown keyword arguments: "transformer_weights"', make_column_transformer, (scaler, 'first'), (norm, ['second']), transformer_weights={'pca': 10, 'Transf': 1} ) def test_make_column_transformer_remainder_transformer(): scaler = StandardScaler() norm = Normalizer() remainder = StandardScaler() ct = make_column_transformer((scaler, 'first'), (norm, ['second']), remainder=remainder) assert ct.remainder == remainder def test_column_transformer_get_set_params(): ct = ColumnTransformer([('trans1', StandardScaler(), [0]), ('trans2', StandardScaler(), [1])]) exp = {'n_jobs': None, 'remainder': 'drop', 'sparse_threshold': 0.3, 'trans1': ct.transformers[0][1], 'trans1__copy': True, 'trans1__with_mean': True, 'trans1__with_std': True, 'trans2': ct.transformers[1][1], 'trans2__copy': True, 'trans2__with_mean': True, 'trans2__with_std': True, 'transformers': ct.transformers, 'transformer_weights': None} assert_dict_equal(ct.get_params(), exp) ct.set_params(trans1__with_mean=False) assert_false(ct.get_params()['trans1__with_mean']) ct.set_params(trans1='passthrough') exp = {'n_jobs': None, 'remainder': 'drop', 'sparse_threshold': 0.3, 'trans1': 'passthrough', 'trans2': ct.transformers[1][1], 'trans2__copy': True, 'trans2__with_mean': True, 'trans2__with_std': True, 'transformers': ct.transformers, 'transformer_weights': None} assert_dict_equal(ct.get_params(), exp) def test_column_transformer_named_estimators(): X_array = np.array([[0., 1., 2.], [2., 4., 6.]]).T ct = ColumnTransformer([('trans1', StandardScaler(), [0]), ('trans2', StandardScaler(with_std=False), [1])]) assert_false(hasattr(ct, 'transformers_')) ct.fit(X_array) assert hasattr(ct, 'transformers_') assert isinstance(ct.named_transformers_['trans1'], StandardScaler) assert isinstance(ct.named_transformers_.trans1, StandardScaler) assert isinstance(ct.named_transformers_['trans2'], StandardScaler) assert isinstance(ct.named_transformers_.trans2, StandardScaler) assert_false(ct.named_transformers_.trans2.with_std) # check it are fitted transformers assert_equal(ct.named_transformers_.trans1.mean_, 1.) def test_column_transformer_cloning(): X_array = np.array([[0., 1., 2.], [2., 4., 6.]]).T ct = ColumnTransformer([('trans', StandardScaler(), [0])]) ct.fit(X_array) assert_false(hasattr(ct.transformers[0][1], 'mean_')) assert hasattr(ct.transformers_[0][1], 'mean_') ct = ColumnTransformer([('trans', StandardScaler(), [0])]) ct.fit_transform(X_array) assert_false(hasattr(ct.transformers[0][1], 'mean_')) assert hasattr(ct.transformers_[0][1], 'mean_') def test_column_transformer_get_feature_names(): X_array = np.array([[0., 1., 2.], [2., 4., 6.]]).T ct = ColumnTransformer([('trans', Trans(), [0, 1])]) # raise correct error when not fitted assert_raises(NotFittedError, ct.get_feature_names) # raise correct error when no feature names are available ct.fit(X_array) assert_raise_message(AttributeError, "Transformer trans (type Trans) does not provide " "get_feature_names", ct.get_feature_names) # working example X = np.array([[{'a': 1, 'b': 2}, {'a': 3, 'b': 4}], [{'c': 5}, {'c': 6}]], dtype=object).T ct = ColumnTransformer( [('col' + str(i), DictVectorizer(), i) for i in range(2)]) ct.fit(X) assert_equal(ct.get_feature_names(), ['col0__a', 'col0__b', 'col1__c']) # passthrough transformers not supported ct = ColumnTransformer([('trans', 'passthrough', [0, 1])]) ct.fit(X) assert_raise_message( NotImplementedError, 'get_feature_names is not yet supported', ct.get_feature_names) ct = ColumnTransformer([('trans', DictVectorizer(), 0)], remainder='passthrough') ct.fit(X) assert_raise_message( NotImplementedError, 'get_feature_names is not yet supported', ct.get_feature_names) # drop transformer ct = ColumnTransformer( [('col0', DictVectorizer(), 0), ('col1', 'drop', 1)]) ct.fit(X) assert_equal(ct.get_feature_names(), ['col0__a', 'col0__b']) def test_column_transformer_special_strings(): # one 'drop' -> ignore X_array = np.array([[0., 1., 2.], [2., 4., 6.]]).T ct = ColumnTransformer( [('trans1', Trans(), [0]), ('trans2', 'drop', [1])]) exp = np.array([[0.], [1.], [2.]]) assert_array_equal(ct.fit_transform(X_array), exp) assert_array_equal(ct.fit(X_array).transform(X_array), exp) assert len(ct.transformers_) == 2 assert ct.transformers_[-1][0] != 'remainder' # all 'drop' -> return shape 0 array ct = ColumnTransformer( [('trans1', 'drop', [0]), ('trans2', 'drop', [1])]) assert_array_equal(ct.fit(X_array).transform(X_array).shape, (3, 0)) assert_array_equal(ct.fit_transform(X_array).shape, (3, 0)) assert len(ct.transformers_) == 2 assert ct.transformers_[-1][0] != 'remainder' # 'passthrough' X_array = np.array([[0., 1., 2.], [2., 4., 6.]]).T ct = ColumnTransformer( [('trans1', Trans(), [0]), ('trans2', 'passthrough', [1])]) exp = X_array assert_array_equal(ct.fit_transform(X_array), exp) assert_array_equal(ct.fit(X_array).transform(X_array), exp) assert len(ct.transformers_) == 2 assert ct.transformers_[-1][0] != 'remainder' # None itself / other string is not valid for val in [None, 'other']: ct = ColumnTransformer( [('trans1', Trans(), [0]), ('trans2', None, [1])]) assert_raise_message(TypeError, "All estimators should implement", ct.fit_transform, X_array) assert_raise_message(TypeError, "All estimators should implement", ct.fit, X_array) def test_column_transformer_remainder(): X_array = np.array([[0, 1, 2], [2, 4, 6]]).T X_res_first = np.array([0, 1, 2]).reshape(-1, 1) X_res_second = np.array([2, 4, 6]).reshape(-1, 1) X_res_both = X_array # default drop ct = ColumnTransformer([('trans1', Trans(), [0])]) assert_array_equal(ct.fit_transform(X_array), X_res_first) assert_array_equal(ct.fit(X_array).transform(X_array), X_res_first) assert len(ct.transformers_) == 2 assert ct.transformers_[-1][0] == 'remainder' assert ct.transformers_[-1][1] == 'drop' assert_array_equal(ct.transformers_[-1][2], [1]) # specify passthrough ct = ColumnTransformer([('trans', Trans(), [0])], remainder='passthrough') assert_array_equal(ct.fit_transform(X_array), X_res_both) assert_array_equal(ct.fit(X_array).transform(X_array), X_res_both) assert len(ct.transformers_) == 2 assert ct.transformers_[-1][0] == 'remainder' assert ct.transformers_[-1][1] == 'passthrough' assert_array_equal(ct.transformers_[-1][2], [1]) # column order is not preserved (passed through added to end) ct = ColumnTransformer([('trans1', Trans(), [1])], remainder='passthrough') assert_array_equal(ct.fit_transform(X_array), X_res_both[:, ::-1]) assert_array_equal(ct.fit(X_array).transform(X_array), X_res_both[:, ::-1]) assert len(ct.transformers_) == 2 assert ct.transformers_[-1][0] == 'remainder' assert ct.transformers_[-1][1] == 'passthrough' assert_array_equal(ct.transformers_[-1][2], [0]) # passthrough when all actual transformers are skipped ct = ColumnTransformer([('trans1', 'drop', [0])], remainder='passthrough') assert_array_equal(ct.fit_transform(X_array), X_res_second) assert_array_equal(ct.fit(X_array).transform(X_array), X_res_second) assert len(ct.transformers_) == 2 assert ct.transformers_[-1][0] == 'remainder' assert ct.transformers_[-1][1] == 'passthrough' assert_array_equal(ct.transformers_[-1][2], [1]) # error on invalid arg ct = ColumnTransformer([('trans1', Trans(), [0])], remainder=1) assert_raise_message( ValueError, "remainder keyword needs to be one of \'drop\', \'passthrough\', " "or estimator.", ct.fit, X_array) assert_raise_message( ValueError, "remainder keyword needs to be one of \'drop\', \'passthrough\', " "or estimator.", ct.fit_transform, X_array) # check default for make_column_transformer ct = make_column_transformer((Trans(), [0])) assert ct.remainder == 'drop' @pytest.mark.parametrize("key", [[0], np.array([0]), slice(0, 1), np.array([True, False])]) def test_column_transformer_remainder_numpy(key): # test different ways that columns are specified with passthrough X_array = np.array([[0, 1, 2], [2, 4, 6]]).T X_res_both = X_array ct = ColumnTransformer([('trans1', Trans(), key)], remainder='passthrough') assert_array_equal(ct.fit_transform(X_array), X_res_both) assert_array_equal(ct.fit(X_array).transform(X_array), X_res_both) assert len(ct.transformers_) == 2 assert ct.transformers_[-1][0] == 'remainder' assert ct.transformers_[-1][1] == 'passthrough' assert_array_equal(ct.transformers_[-1][2], [1]) @pytest.mark.parametrize( "key", [[0], slice(0, 1), np.array([True, False]), ['first'], 'pd-index', np.array(['first']), np.array(['first'], dtype=object), slice(None, 'first'), slice('first', 'first')]) def test_column_transformer_remainder_pandas(key): # test different ways that columns are specified with passthrough pd = pytest.importorskip('pandas') if isinstance(key, six.string_types) and key == 'pd-index': key = pd.Index(['first']) X_array = np.array([[0, 1, 2], [2, 4, 6]]).T X_df = pd.DataFrame(X_array, columns=['first', 'second']) X_res_both = X_array ct = ColumnTransformer([('trans1', Trans(), key)], remainder='passthrough') assert_array_equal(ct.fit_transform(X_df), X_res_both) assert_array_equal(ct.fit(X_df).transform(X_df), X_res_both) assert len(ct.transformers_) == 2 assert ct.transformers_[-1][0] == 'remainder' assert ct.transformers_[-1][1] == 'passthrough' assert_array_equal(ct.transformers_[-1][2], [1]) @pytest.mark.parametrize("key", [[0], np.array([0]), slice(0, 1), np.array([True, False, False])]) def test_column_transformer_remainder_transformer(key): X_array = np.array([[0, 1, 2], [2, 4, 6], [8, 6, 4]]).T X_res_both = X_array.copy() # second and third columns are doubled when remainder = DoubleTrans X_res_both[:, 1:3] *= 2 ct = ColumnTransformer([('trans1', Trans(), key)], remainder=DoubleTrans()) assert_array_equal(ct.fit_transform(X_array), X_res_both) assert_array_equal(ct.fit(X_array).transform(X_array), X_res_both) assert len(ct.transformers_) == 2 assert ct.transformers_[-1][0] == 'remainder' assert isinstance(ct.transformers_[-1][1], DoubleTrans) assert_array_equal(ct.transformers_[-1][2], [1, 2]) def test_column_transformer_no_remaining_remainder_transformer(): X_array = np.array([[0, 1, 2], [2, 4, 6], [8, 6, 4]]).T ct = ColumnTransformer([('trans1', Trans(), [0, 1, 2])], remainder=DoubleTrans()) assert_array_equal(ct.fit_transform(X_array), X_array) assert_array_equal(ct.fit(X_array).transform(X_array), X_array) assert len(ct.transformers_) == 1 assert ct.transformers_[-1][0] != 'remainder' def test_column_transformer_drops_all_remainder_transformer(): X_array = np.array([[0, 1, 2], [2, 4, 6], [8, 6, 4]]).T # columns are doubled when remainder = DoubleTrans X_res_both = 2 * X_array.copy()[:, 1:3] ct = ColumnTransformer([('trans1', 'drop', [0])], remainder=DoubleTrans()) assert_array_equal(ct.fit_transform(X_array), X_res_both) assert_array_equal(ct.fit(X_array).transform(X_array), X_res_both) assert len(ct.transformers_) == 2 assert ct.transformers_[-1][0] == 'remainder' assert isinstance(ct.transformers_[-1][1], DoubleTrans) assert_array_equal(ct.transformers_[-1][2], [1, 2]) def test_column_transformer_sparse_remainder_transformer(): X_array = np.array([[0, 1, 2], [2, 4, 6], [8, 6, 4]]).T ct = ColumnTransformer([('trans1', Trans(), [0])], remainder=SparseMatrixTrans(), sparse_threshold=0.8) X_trans = ct.fit_transform(X_array) assert sparse.issparse(X_trans) # SparseMatrixTrans creates 3 features for each column. There is # one column in ``transformers``, thus: assert X_trans.shape == (3, 3 + 1) exp_array = np.hstack( (X_array[:, 0].reshape(-1, 1), np.eye(3))) assert_array_equal(X_trans.toarray(), exp_array) assert len(ct.transformers_) == 2 assert ct.transformers_[-1][0] == 'remainder' assert isinstance(ct.transformers_[-1][1], SparseMatrixTrans) assert_array_equal(ct.transformers_[-1][2], [1, 2]) def test_column_transformer_drop_all_sparse_remainder_transformer(): X_array = np.array([[0, 1, 2], [2, 4, 6], [8, 6, 4]]).T ct = ColumnTransformer([('trans1', 'drop', [0])], remainder=SparseMatrixTrans(), sparse_threshold=0.8) X_trans = ct.fit_transform(X_array) assert sparse.issparse(X_trans) # SparseMatrixTrans creates 3 features for each column, thus: assert X_trans.shape == (3, 3) assert_array_equal(X_trans.toarray(), np.eye(3)) assert len(ct.transformers_) == 2 assert ct.transformers_[-1][0] == 'remainder' assert isinstance(ct.transformers_[-1][1], SparseMatrixTrans) assert_array_equal(ct.transformers_[-1][2], [1, 2]) def test_column_transformer_get_set_params_with_remainder(): ct = ColumnTransformer([('trans1', StandardScaler(), [0])], remainder=StandardScaler()) exp = {'n_jobs': None, 'remainder': ct.remainder, 'remainder__copy': True, 'remainder__with_mean': True, 'remainder__with_std': True, 'sparse_threshold': 0.3, 'trans1': ct.transformers[0][1], 'trans1__copy': True, 'trans1__with_mean': True, 'trans1__with_std': True, 'transformers': ct.transformers, 'transformer_weights': None} assert ct.get_params() == exp ct.set_params(remainder__with_std=False) assert not ct.get_params()['remainder__with_std'] ct.set_params(trans1='passthrough') exp = {'n_jobs': None, 'remainder': ct.remainder, 'remainder__copy': True, 'remainder__with_mean': True, 'remainder__with_std': False, 'sparse_threshold': 0.3, 'trans1': 'passthrough', 'transformers': ct.transformers, 'transformer_weights': None} assert ct.get_params() == exp def test_column_transformer_no_estimators(): X_array = np.array([[0, 1, 2], [2, 4, 6], [8, 6, 4]]).astype('float').T ct = ColumnTransformer([], remainder=StandardScaler()) params = ct.get_params() assert params['remainder__with_mean'] X_trans = ct.fit_transform(X_array) assert X_trans.shape == X_array.shape assert len(ct.transformers_) == 1 assert ct.transformers_[-1][0] == 'remainder' assert ct.transformers_[-1][2] == [0, 1, 2] def test_column_transformer_no_estimators_set_params(): ct = ColumnTransformer([]).set_params(n_jobs=2) assert ct.n_jobs == 2 def test_column_transformer_callable_specifier(): # assert that function gets the full array / dataframe X_array = np.array([[0, 1, 2], [2, 4, 6]]).T X_res_first = np.array([[0, 1, 2]]).T def func(X): assert_array_equal(X, X_array) return [0] ct = ColumnTransformer([('trans', Trans(), func)], remainder='drop') assert_array_equal(ct.fit_transform(X_array), X_res_first) assert_array_equal(ct.fit(X_array).transform(X_array), X_res_first) assert callable(ct.transformers[0][2]) assert ct.transformers_[0][2] == [0] pd = pytest.importorskip('pandas') X_df = pd.DataFrame(X_array, columns=['first', 'second']) def func(X): assert_array_equal(X.columns, X_df.columns) assert_array_equal(X.values, X_df.values) return ['first'] ct = ColumnTransformer([('trans', Trans(), func)], remainder='drop') assert_array_equal(ct.fit_transform(X_df), X_res_first) assert_array_equal(ct.fit(X_df).transform(X_df), X_res_first) assert callable(ct.transformers[0][2]) assert ct.transformers_[0][2] == ['first']