1034 lines
39 KiB
Python
1034 lines
39 KiB
Python
"""
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Test the ColumnTransformer.
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"""
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import numpy as np
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from scipy import sparse
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import pytest
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from sklearn.utils.testing import assert_raises
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from sklearn.utils.testing import assert_raise_message
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from sklearn.utils.testing import assert_equal
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from sklearn.utils.testing import assert_dict_equal
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from sklearn.utils.testing import assert_array_equal
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from sklearn.utils.testing import assert_allclose_dense_sparse
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from sklearn.utils.testing import assert_almost_equal
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from sklearn.base import BaseEstimator
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from sklearn.compose import ColumnTransformer, make_column_transformer
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from sklearn.exceptions import NotFittedError, DataConversionWarning
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from sklearn.preprocessing import StandardScaler, Normalizer, OneHotEncoder
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from sklearn.feature_extraction import DictVectorizer
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class Trans(BaseEstimator):
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def fit(self, X, y=None):
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return self
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def transform(self, X, y=None):
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# 1D Series -> 2D DataFrame
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if hasattr(X, 'to_frame'):
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return X.to_frame()
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# 1D array -> 2D array
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if X.ndim == 1:
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return np.atleast_2d(X).T
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return X
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class DoubleTrans(BaseEstimator):
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def fit(self, X, y=None):
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return self
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def transform(self, X):
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return 2*X
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class SparseMatrixTrans(BaseEstimator):
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def fit(self, X, y=None):
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return self
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def transform(self, X, y=None):
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n_samples = len(X)
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return sparse.eye(n_samples, n_samples).tocsr()
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class TransNo2D(BaseEstimator):
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def fit(self, X, y=None):
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return self
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def transform(self, X, y=None):
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return X
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class TransRaise(BaseEstimator):
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def fit(self, X, y=None):
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raise ValueError("specific message")
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def transform(self, X, y=None):
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raise ValueError("specific message")
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def test_column_transformer():
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X_array = np.array([[0, 1, 2], [2, 4, 6]]).T
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X_res_first1D = np.array([0, 1, 2])
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X_res_second1D = np.array([2, 4, 6])
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X_res_first = X_res_first1D.reshape(-1, 1)
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X_res_both = X_array
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cases = [
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# single column 1D / 2D
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(0, X_res_first),
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([0], X_res_first),
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# list-like
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([0, 1], X_res_both),
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(np.array([0, 1]), X_res_both),
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# slice
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(slice(0, 1), X_res_first),
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(slice(0, 2), X_res_both),
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# boolean mask
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(np.array([True, False]), X_res_first),
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]
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for selection, res in cases:
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ct = ColumnTransformer([('trans', Trans(), selection)],
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remainder='drop')
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assert_array_equal(ct.fit_transform(X_array), res)
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assert_array_equal(ct.fit(X_array).transform(X_array), res)
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# callable that returns any of the allowed specifiers
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ct = ColumnTransformer([('trans', Trans(), lambda x: selection)],
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remainder='drop')
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assert_array_equal(ct.fit_transform(X_array), res)
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assert_array_equal(ct.fit(X_array).transform(X_array), res)
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ct = ColumnTransformer([('trans1', Trans(), [0]),
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('trans2', Trans(), [1])])
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assert_array_equal(ct.fit_transform(X_array), X_res_both)
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assert_array_equal(ct.fit(X_array).transform(X_array), X_res_both)
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assert len(ct.transformers_) == 2
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# test with transformer_weights
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transformer_weights = {'trans1': .1, 'trans2': 10}
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both = ColumnTransformer([('trans1', Trans(), [0]),
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('trans2', Trans(), [1])],
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transformer_weights=transformer_weights)
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res = np.vstack([transformer_weights['trans1'] * X_res_first1D,
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transformer_weights['trans2'] * X_res_second1D]).T
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assert_array_equal(both.fit_transform(X_array), res)
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assert_array_equal(both.fit(X_array).transform(X_array), res)
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assert len(both.transformers_) == 2
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both = ColumnTransformer([('trans', Trans(), [0, 1])],
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transformer_weights={'trans': .1})
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assert_array_equal(both.fit_transform(X_array), 0.1 * X_res_both)
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assert_array_equal(both.fit(X_array).transform(X_array), 0.1 * X_res_both)
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assert len(both.transformers_) == 1
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def test_column_transformer_dataframe():
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pd = pytest.importorskip('pandas')
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X_array = np.array([[0, 1, 2], [2, 4, 6]]).T
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X_df = pd.DataFrame(X_array, columns=['first', 'second'])
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X_res_first = np.array([0, 1, 2]).reshape(-1, 1)
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X_res_both = X_array
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cases = [
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# String keys: label based
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# scalar
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('first', X_res_first),
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# list
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(['first'], X_res_first),
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(['first', 'second'], X_res_both),
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# slice
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(slice('first', 'second'), X_res_both),
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# int keys: positional
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# scalar
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(0, X_res_first),
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# list
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([0], X_res_first),
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([0, 1], X_res_both),
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(np.array([0, 1]), X_res_both),
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# slice
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(slice(0, 1), X_res_first),
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(slice(0, 2), X_res_both),
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# boolean mask
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(np.array([True, False]), X_res_first),
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(pd.Series([True, False], index=['first', 'second']), X_res_first),
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]
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for selection, res in cases:
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ct = ColumnTransformer([('trans', Trans(), selection)],
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remainder='drop')
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assert_array_equal(ct.fit_transform(X_df), res)
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assert_array_equal(ct.fit(X_df).transform(X_df), res)
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# callable that returns any of the allowed specifiers
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ct = ColumnTransformer([('trans', Trans(), lambda X: selection)],
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remainder='drop')
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assert_array_equal(ct.fit_transform(X_df), res)
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assert_array_equal(ct.fit(X_df).transform(X_df), res)
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ct = ColumnTransformer([('trans1', Trans(), ['first']),
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('trans2', Trans(), ['second'])])
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assert_array_equal(ct.fit_transform(X_df), X_res_both)
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assert_array_equal(ct.fit(X_df).transform(X_df), X_res_both)
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assert len(ct.transformers_) == 2
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assert ct.transformers_[-1][0] != 'remainder'
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ct = ColumnTransformer([('trans1', Trans(), [0]),
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('trans2', Trans(), [1])])
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assert_array_equal(ct.fit_transform(X_df), X_res_both)
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assert_array_equal(ct.fit(X_df).transform(X_df), X_res_both)
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assert len(ct.transformers_) == 2
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assert ct.transformers_[-1][0] != 'remainder'
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# test with transformer_weights
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transformer_weights = {'trans1': .1, 'trans2': 10}
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both = ColumnTransformer([('trans1', Trans(), ['first']),
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('trans2', Trans(), ['second'])],
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transformer_weights=transformer_weights)
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res = np.vstack([transformer_weights['trans1'] * X_df['first'],
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transformer_weights['trans2'] * X_df['second']]).T
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assert_array_equal(both.fit_transform(X_df), res)
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assert_array_equal(both.fit(X_df).transform(X_df), res)
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assert len(both.transformers_) == 2
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assert ct.transformers_[-1][0] != 'remainder'
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# test multiple columns
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both = ColumnTransformer([('trans', Trans(), ['first', 'second'])],
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transformer_weights={'trans': .1})
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assert_array_equal(both.fit_transform(X_df), 0.1 * X_res_both)
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assert_array_equal(both.fit(X_df).transform(X_df), 0.1 * X_res_both)
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assert len(both.transformers_) == 1
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assert ct.transformers_[-1][0] != 'remainder'
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both = ColumnTransformer([('trans', Trans(), [0, 1])],
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transformer_weights={'trans': .1})
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assert_array_equal(both.fit_transform(X_df), 0.1 * X_res_both)
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assert_array_equal(both.fit(X_df).transform(X_df), 0.1 * X_res_both)
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assert len(both.transformers_) == 1
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assert ct.transformers_[-1][0] != 'remainder'
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# ensure pandas object is passes through
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class TransAssert(BaseEstimator):
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def fit(self, X, y=None):
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return self
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def transform(self, X, y=None):
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assert isinstance(X, (pd.DataFrame, pd.Series))
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if isinstance(X, pd.Series):
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X = X.to_frame()
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return X
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ct = ColumnTransformer([('trans', TransAssert(), 'first')],
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remainder='drop')
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ct.fit_transform(X_df)
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ct = ColumnTransformer([('trans', TransAssert(), ['first', 'second'])])
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ct.fit_transform(X_df)
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# integer column spec + integer column names -> still use positional
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X_df2 = X_df.copy()
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X_df2.columns = [1, 0]
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ct = ColumnTransformer([('trans', Trans(), 0)], remainder='drop')
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assert_array_equal(ct.fit_transform(X_df), X_res_first)
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assert_array_equal(ct.fit(X_df).transform(X_df), X_res_first)
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assert len(ct.transformers_) == 2
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assert ct.transformers_[-1][0] == 'remainder'
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assert ct.transformers_[-1][1] == 'drop'
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assert_array_equal(ct.transformers_[-1][2], [1])
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@pytest.mark.parametrize("pandas", [True, False], ids=['pandas', 'numpy'])
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@pytest.mark.parametrize("column", [[], np.array([False, False])],
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ids=['list', 'bool'])
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def test_column_transformer_empty_columns(pandas, column):
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# test case that ensures that the column transformer does also work when
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# a given transformer doesn't have any columns to work on
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X_array = np.array([[0, 1, 2], [2, 4, 6]]).T
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X_res_both = X_array
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if pandas:
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pd = pytest.importorskip('pandas')
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X = pd.DataFrame(X_array, columns=['first', 'second'])
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else:
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X = X_array
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ct = ColumnTransformer([('trans1', Trans(), [0, 1]),
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('trans2', Trans(), column)])
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assert_array_equal(ct.fit_transform(X), X_res_both)
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assert_array_equal(ct.fit(X).transform(X), X_res_both)
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assert len(ct.transformers_) == 2
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assert isinstance(ct.transformers_[1][1], Trans)
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ct = ColumnTransformer([('trans1', Trans(), column),
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('trans2', Trans(), [0, 1])])
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assert_array_equal(ct.fit_transform(X), X_res_both)
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assert_array_equal(ct.fit(X).transform(X), X_res_both)
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assert len(ct.transformers_) == 2
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assert isinstance(ct.transformers_[0][1], Trans)
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ct = ColumnTransformer([('trans', Trans(), column)],
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remainder='passthrough')
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assert_array_equal(ct.fit_transform(X), X_res_both)
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assert_array_equal(ct.fit(X).transform(X), X_res_both)
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assert len(ct.transformers_) == 2 # including remainder
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assert isinstance(ct.transformers_[0][1], Trans)
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fixture = np.array([[], [], []])
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ct = ColumnTransformer([('trans', Trans(), column)],
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remainder='drop')
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assert_array_equal(ct.fit_transform(X), fixture)
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assert_array_equal(ct.fit(X).transform(X), fixture)
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assert len(ct.transformers_) == 2 # including remainder
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assert isinstance(ct.transformers_[0][1], Trans)
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def test_column_transformer_sparse_array():
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X_sparse = sparse.eye(3, 2).tocsr()
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# no distinction between 1D and 2D
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X_res_first = X_sparse[:, 0]
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X_res_both = X_sparse
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for col in [0, [0], slice(0, 1)]:
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for remainder, res in [('drop', X_res_first),
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('passthrough', X_res_both)]:
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ct = ColumnTransformer([('trans', Trans(), col)],
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remainder=remainder,
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sparse_threshold=0.8)
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assert sparse.issparse(ct.fit_transform(X_sparse))
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assert_allclose_dense_sparse(ct.fit_transform(X_sparse), res)
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assert_allclose_dense_sparse(ct.fit(X_sparse).transform(X_sparse),
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res)
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for col in [[0, 1], slice(0, 2)]:
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ct = ColumnTransformer([('trans', Trans(), col)],
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sparse_threshold=0.8)
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assert sparse.issparse(ct.fit_transform(X_sparse))
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assert_allclose_dense_sparse(ct.fit_transform(X_sparse), X_res_both)
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assert_allclose_dense_sparse(ct.fit(X_sparse).transform(X_sparse),
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X_res_both)
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def test_column_transformer_list():
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X_list = [
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[1, float('nan'), 'a'],
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[0, 0, 'b']
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]
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expected_result = np.array([
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[1, float('nan'), 1, 0],
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[-1, 0, 0, 1],
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])
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ct = ColumnTransformer([
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('numerical', StandardScaler(), [0, 1]),
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('categorical', OneHotEncoder(), [2]),
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])
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with pytest.warns(DataConversionWarning):
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# TODO: this warning is not very useful in this case, would be good
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# to get rid of it
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assert_array_equal(ct.fit_transform(X_list), expected_result)
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assert_array_equal(ct.fit(X_list).transform(X_list), expected_result)
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def test_column_transformer_sparse_stacking():
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X_array = np.array([[0, 1, 2], [2, 4, 6]]).T
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col_trans = ColumnTransformer([('trans1', Trans(), [0]),
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('trans2', SparseMatrixTrans(), 1)],
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sparse_threshold=0.8)
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col_trans.fit(X_array)
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X_trans = col_trans.transform(X_array)
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assert sparse.issparse(X_trans)
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assert_equal(X_trans.shape, (X_trans.shape[0], X_trans.shape[0] + 1))
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assert_array_equal(X_trans.toarray()[:, 1:], np.eye(X_trans.shape[0]))
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assert len(col_trans.transformers_) == 2
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assert col_trans.transformers_[-1][0] != 'remainder'
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col_trans = ColumnTransformer([('trans1', Trans(), [0]),
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('trans2', SparseMatrixTrans(), 1)],
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sparse_threshold=0.1)
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col_trans.fit(X_array)
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X_trans = col_trans.transform(X_array)
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assert not sparse.issparse(X_trans)
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assert X_trans.shape == (X_trans.shape[0], X_trans.shape[0] + 1)
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assert_array_equal(X_trans[:, 1:], np.eye(X_trans.shape[0]))
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def test_column_transformer_mixed_cols_sparse():
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df = np.array([['a', 1, True],
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['b', 2, False]],
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dtype='O')
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ct = make_column_transformer(
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(OneHotEncoder(), [0]),
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('passthrough', [1, 2]),
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sparse_threshold=1.0
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)
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# this shouldn't fail, since boolean can be coerced into a numeric
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# See: https://github.com/scikit-learn/scikit-learn/issues/11912
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X_trans = ct.fit_transform(df)
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assert X_trans.getformat() == 'csr'
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assert_array_equal(X_trans.toarray(), np.array([[1, 0, 1, 1],
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[0, 1, 2, 0]]))
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ct = make_column_transformer(
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(OneHotEncoder(), [0]),
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('passthrough', [0]),
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sparse_threshold=1.0
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)
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with pytest.raises(ValueError,
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match="For a sparse output, all columns should"):
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# this fails since strings `a` and `b` cannot be
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# coerced into a numeric.
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ct.fit_transform(df)
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def test_column_transformer_sparse_threshold():
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X_array = np.array([['a', 'b'], ['A', 'B']], dtype=object).T
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# above data has sparsity of 4 / 8 = 0.5
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# apply threshold even if all sparse
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col_trans = ColumnTransformer([('trans1', OneHotEncoder(), [0]),
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('trans2', OneHotEncoder(), [1])],
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sparse_threshold=0.2)
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res = col_trans.fit_transform(X_array)
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assert not sparse.issparse(res)
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assert not col_trans.sparse_output_
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# mixed -> sparsity of (4 + 2) / 8 = 0.75
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for thres in [0.75001, 1]:
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col_trans = ColumnTransformer(
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[('trans1', OneHotEncoder(sparse=True), [0]),
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('trans2', OneHotEncoder(sparse=False), [1])],
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sparse_threshold=thres)
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res = col_trans.fit_transform(X_array)
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assert sparse.issparse(res)
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assert col_trans.sparse_output_
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for thres in [0.75, 0]:
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col_trans = ColumnTransformer(
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[('trans1', OneHotEncoder(sparse=True), [0]),
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('trans2', OneHotEncoder(sparse=False), [1])],
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sparse_threshold=thres)
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res = col_trans.fit_transform(X_array)
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assert not sparse.issparse(res)
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assert not col_trans.sparse_output_
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# if nothing is sparse -> no sparse
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for thres in [0.33, 0, 1]:
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col_trans = ColumnTransformer(
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[('trans1', OneHotEncoder(sparse=False), [0]),
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('trans2', OneHotEncoder(sparse=False), [1])],
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sparse_threshold=thres)
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res = col_trans.fit_transform(X_array)
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assert not sparse.issparse(res)
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assert not col_trans.sparse_output_
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def test_column_transformer_error_msg_1D():
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X_array = np.array([[0., 1., 2.], [2., 4., 6.]]).T
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col_trans = ColumnTransformer([('trans', StandardScaler(), 0)])
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assert_raise_message(ValueError, "1D data passed to a transformer",
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col_trans.fit, X_array)
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assert_raise_message(ValueError, "1D data passed to a transformer",
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col_trans.fit_transform, X_array)
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col_trans = ColumnTransformer([('trans', TransRaise(), 0)])
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for func in [col_trans.fit, col_trans.fit_transform]:
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assert_raise_message(ValueError, "specific message", func, X_array)
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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 not 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 not 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 not 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 not hasattr(ct.transformers[0][1], 'mean_')
|
|
assert hasattr(ct.transformers_[0][1], 'mean_')
|
|
|
|
ct = ColumnTransformer([('trans', StandardScaler(), [0])])
|
|
ct.fit_transform(X_array)
|
|
assert not 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, str) 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)
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assert_array_equal(ct.fit(X_array).transform(X_array), X_res_both)
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assert len(ct.transformers_) == 2
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assert ct.transformers_[-1][0] == 'remainder'
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assert isinstance(ct.transformers_[-1][1], DoubleTrans)
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assert_array_equal(ct.transformers_[-1][2], [1, 2])
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|
|
|
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def test_column_transformer_sparse_remainder_transformer():
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X_array = np.array([[0, 1, 2],
|
|
[2, 4, 6],
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|
[8, 6, 4]]).T
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|
|
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ct = ColumnTransformer([('trans1', Trans(), [0])],
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|
remainder=SparseMatrixTrans(),
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|
sparse_threshold=0.8)
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|
|
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X_trans = ct.fit_transform(X_array)
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|
assert sparse.issparse(X_trans)
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|
# 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)))
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|
assert_array_equal(X_trans.toarray(), exp_array)
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|
assert len(ct.transformers_) == 2
|
|
assert ct.transformers_[-1][0] == 'remainder'
|
|
assert isinstance(ct.transformers_[-1][1], SparseMatrixTrans)
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|
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']
|
|
|
|
|
|
def test_column_transformer_negative_column_indexes():
|
|
X = np.random.randn(2, 2)
|
|
X_categories = np.array([[1], [2]])
|
|
X = np.concatenate([X, X_categories], axis=1)
|
|
|
|
ohe = OneHotEncoder(categories='auto')
|
|
|
|
tf_1 = ColumnTransformer([('ohe', ohe, [-1])], remainder='passthrough')
|
|
tf_2 = ColumnTransformer([('ohe', ohe, [2])], remainder='passthrough')
|
|
assert_array_equal(tf_1.fit_transform(X), tf_2.fit_transform(X))
|