960 lines
35 KiB
Python
960 lines
35 KiB
Python
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# -*- coding: utf-8 -*-
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import re
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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.exceptions import NotFittedError
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from sklearn.utils._testing import assert_array_equal
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from sklearn.utils._testing import assert_allclose
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from sklearn.utils._testing import _convert_container
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from sklearn.utils import is_scalar_nan
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from sklearn.preprocessing import OneHotEncoder
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from sklearn.preprocessing import OrdinalEncoder
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def test_one_hot_encoder_sparse_dense():
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# check that sparse and dense will give the same results
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X = np.array([[3, 2, 1], [0, 1, 1]])
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enc_sparse = OneHotEncoder()
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enc_dense = OneHotEncoder(sparse=False)
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X_trans_sparse = enc_sparse.fit_transform(X)
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X_trans_dense = enc_dense.fit_transform(X)
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assert X_trans_sparse.shape == (2, 5)
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assert X_trans_dense.shape == (2, 5)
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assert sparse.issparse(X_trans_sparse)
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assert not sparse.issparse(X_trans_dense)
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# check outcome
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assert_array_equal(X_trans_sparse.toarray(), [[0., 1., 0., 1., 1.],
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[1., 0., 1., 0., 1.]])
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assert_array_equal(X_trans_sparse.toarray(), X_trans_dense)
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def test_one_hot_encoder_diff_n_features():
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X = np.array([[0, 2, 1], [1, 0, 3], [1, 0, 2]])
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X2 = np.array([[1, 0]])
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enc = OneHotEncoder()
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enc.fit(X)
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err_msg = ("The number of features in X is different to the number of "
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"features of the fitted data.")
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with pytest.raises(ValueError, match=err_msg):
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enc.transform(X2)
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def test_one_hot_encoder_handle_unknown():
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X = np.array([[0, 2, 1], [1, 0, 3], [1, 0, 2]])
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X2 = np.array([[4, 1, 1]])
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# Test that one hot encoder raises error for unknown features
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# present during transform.
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oh = OneHotEncoder(handle_unknown='error')
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oh.fit(X)
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with pytest.raises(ValueError, match='Found unknown categories'):
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oh.transform(X2)
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# Test the ignore option, ignores unknown features (giving all 0's)
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oh = OneHotEncoder(handle_unknown='ignore')
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oh.fit(X)
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X2_passed = X2.copy()
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assert_array_equal(
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oh.transform(X2_passed).toarray(),
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np.array([[0., 0., 0., 0., 1., 0., 0.]]))
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# ensure transformed data was not modified in place
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assert_allclose(X2, X2_passed)
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# Raise error if handle_unknown is neither ignore or error.
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oh = OneHotEncoder(handle_unknown='42')
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with pytest.raises(ValueError, match='handle_unknown should be either'):
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oh.fit(X)
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def test_one_hot_encoder_not_fitted():
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X = np.array([['a'], ['b']])
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enc = OneHotEncoder(categories=['a', 'b'])
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msg = ("This OneHotEncoder instance is not fitted yet. "
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"Call 'fit' with appropriate arguments before using this "
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"estimator.")
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with pytest.raises(NotFittedError, match=msg):
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enc.transform(X)
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def test_one_hot_encoder_handle_unknown_strings():
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X = np.array(['11111111', '22', '333', '4444']).reshape((-1, 1))
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X2 = np.array(['55555', '22']).reshape((-1, 1))
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# Non Regression test for the issue #12470
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# Test the ignore option, when categories are numpy string dtype
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# particularly when the known category strings are larger
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# than the unknown category strings
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oh = OneHotEncoder(handle_unknown='ignore')
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oh.fit(X)
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X2_passed = X2.copy()
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assert_array_equal(
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oh.transform(X2_passed).toarray(),
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np.array([[0., 0., 0., 0.], [0., 1., 0., 0.]]))
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# ensure transformed data was not modified in place
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assert_array_equal(X2, X2_passed)
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@pytest.mark.parametrize("output_dtype", [np.int32, np.float32, np.float64])
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@pytest.mark.parametrize("input_dtype", [np.int32, np.float32, np.float64])
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def test_one_hot_encoder_dtype(input_dtype, output_dtype):
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X = np.asarray([[0, 1]], dtype=input_dtype).T
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X_expected = np.asarray([[1, 0], [0, 1]], dtype=output_dtype)
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oh = OneHotEncoder(categories='auto', dtype=output_dtype)
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assert_array_equal(oh.fit_transform(X).toarray(), X_expected)
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assert_array_equal(oh.fit(X).transform(X).toarray(), X_expected)
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oh = OneHotEncoder(categories='auto', dtype=output_dtype, sparse=False)
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assert_array_equal(oh.fit_transform(X), X_expected)
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assert_array_equal(oh.fit(X).transform(X), X_expected)
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@pytest.mark.parametrize("output_dtype", [np.int32, np.float32, np.float64])
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def test_one_hot_encoder_dtype_pandas(output_dtype):
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pd = pytest.importorskip('pandas')
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X_df = pd.DataFrame({'A': ['a', 'b'], 'B': [1, 2]})
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X_expected = np.array([[1, 0, 1, 0], [0, 1, 0, 1]], dtype=output_dtype)
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oh = OneHotEncoder(dtype=output_dtype)
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assert_array_equal(oh.fit_transform(X_df).toarray(), X_expected)
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assert_array_equal(oh.fit(X_df).transform(X_df).toarray(), X_expected)
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oh = OneHotEncoder(dtype=output_dtype, sparse=False)
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assert_array_equal(oh.fit_transform(X_df), X_expected)
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assert_array_equal(oh.fit(X_df).transform(X_df), X_expected)
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def test_one_hot_encoder_feature_names():
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enc = OneHotEncoder()
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X = [['Male', 1, 'girl', 2, 3],
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['Female', 41, 'girl', 1, 10],
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['Male', 51, 'boy', 12, 3],
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['Male', 91, 'girl', 21, 30]]
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enc.fit(X)
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feature_names = enc.get_feature_names()
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assert isinstance(feature_names, np.ndarray)
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assert_array_equal(['x0_Female', 'x0_Male',
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'x1_1', 'x1_41', 'x1_51', 'x1_91',
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'x2_boy', 'x2_girl',
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'x3_1', 'x3_2', 'x3_12', 'x3_21',
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'x4_3',
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'x4_10', 'x4_30'], feature_names)
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feature_names2 = enc.get_feature_names(['one', 'two',
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'three', 'four', 'five'])
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assert_array_equal(['one_Female', 'one_Male',
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'two_1', 'two_41', 'two_51', 'two_91',
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'three_boy', 'three_girl',
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'four_1', 'four_2', 'four_12', 'four_21',
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'five_3', 'five_10', 'five_30'], feature_names2)
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with pytest.raises(ValueError, match="input_features should have length"):
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enc.get_feature_names(['one', 'two'])
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def test_one_hot_encoder_feature_names_unicode():
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enc = OneHotEncoder()
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X = np.array([['c❤t1', 'dat2']], dtype=object).T
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enc.fit(X)
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feature_names = enc.get_feature_names()
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assert_array_equal(['x0_c❤t1', 'x0_dat2'], feature_names)
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feature_names = enc.get_feature_names(input_features=['n👍me'])
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assert_array_equal(['n👍me_c❤t1', 'n👍me_dat2'], feature_names)
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def test_one_hot_encoder_set_params():
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X = np.array([[1, 2]]).T
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oh = OneHotEncoder()
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# set params on not yet fitted object
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oh.set_params(categories=[[0, 1, 2, 3]])
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assert oh.get_params()['categories'] == [[0, 1, 2, 3]]
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assert oh.fit_transform(X).toarray().shape == (2, 4)
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# set params on already fitted object
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oh.set_params(categories=[[0, 1, 2, 3, 4]])
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assert oh.fit_transform(X).toarray().shape == (2, 5)
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def check_categorical_onehot(X):
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enc = OneHotEncoder(categories='auto')
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Xtr1 = enc.fit_transform(X)
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enc = OneHotEncoder(categories='auto', sparse=False)
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Xtr2 = enc.fit_transform(X)
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assert_allclose(Xtr1.toarray(), Xtr2)
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assert sparse.isspmatrix_csr(Xtr1)
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return Xtr1.toarray()
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@pytest.mark.parametrize("X", [
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[['def', 1, 55], ['abc', 2, 55]],
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np.array([[10, 1, 55], [5, 2, 55]]),
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np.array([['b', 'A', 'cat'], ['a', 'B', 'cat']], dtype=object),
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np.array([['b', 1, 'cat'], ['a', np.nan, 'cat']], dtype=object),
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np.array([['b', 1, 'cat'], ['a', float('nan'), 'cat']], dtype=object),
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np.array([[None, 1, 'cat'], ['a', 2, 'cat']], dtype=object),
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np.array([[None, 1, None], ['a', np.nan, None]], dtype=object),
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np.array([[None, 1, None], ['a', float('nan'), None]], dtype=object),
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], ids=['mixed', 'numeric', 'object', 'mixed-nan', 'mixed-float-nan',
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'mixed-None', 'mixed-None-nan', 'mixed-None-float-nan'])
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def test_one_hot_encoder(X):
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Xtr = check_categorical_onehot(np.array(X)[:, [0]])
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assert_allclose(Xtr, [[0, 1], [1, 0]])
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Xtr = check_categorical_onehot(np.array(X)[:, [0, 1]])
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assert_allclose(Xtr, [[0, 1, 1, 0], [1, 0, 0, 1]])
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Xtr = OneHotEncoder(categories='auto').fit_transform(X)
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assert_allclose(Xtr.toarray(), [[0, 1, 1, 0, 1], [1, 0, 0, 1, 1]])
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@pytest.mark.parametrize('sparse_', [False, True])
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@pytest.mark.parametrize('drop', [None, 'first'])
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def test_one_hot_encoder_inverse(sparse_, drop):
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X = [['abc', 2, 55], ['def', 1, 55], ['abc', 3, 55]]
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enc = OneHotEncoder(sparse=sparse_, drop=drop)
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X_tr = enc.fit_transform(X)
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exp = np.array(X, dtype=object)
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assert_array_equal(enc.inverse_transform(X_tr), exp)
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X = [[2, 55], [1, 55], [3, 55]]
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enc = OneHotEncoder(sparse=sparse_, categories='auto',
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drop=drop)
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X_tr = enc.fit_transform(X)
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exp = np.array(X)
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assert_array_equal(enc.inverse_transform(X_tr), exp)
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if drop is None:
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# with unknown categories
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# drop is incompatible with handle_unknown=ignore
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X = [['abc', 2, 55], ['def', 1, 55], ['abc', 3, 55]]
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enc = OneHotEncoder(sparse=sparse_, handle_unknown='ignore',
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categories=[['abc', 'def'], [1, 2],
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[54, 55, 56]])
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X_tr = enc.fit_transform(X)
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exp = np.array(X, dtype=object)
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exp[2, 1] = None
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assert_array_equal(enc.inverse_transform(X_tr), exp)
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# with an otherwise numerical output, still object if unknown
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X = [[2, 55], [1, 55], [3, 55]]
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enc = OneHotEncoder(sparse=sparse_, categories=[[1, 2], [54, 56]],
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handle_unknown='ignore')
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X_tr = enc.fit_transform(X)
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exp = np.array(X, dtype=object)
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exp[2, 0] = None
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exp[:, 1] = None
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assert_array_equal(enc.inverse_transform(X_tr), exp)
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# incorrect shape raises
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X_tr = np.array([[0, 1, 1], [1, 0, 1]])
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msg = re.escape('Shape of the passed X data is not correct')
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with pytest.raises(ValueError, match=msg):
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enc.inverse_transform(X_tr)
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@pytest.mark.parametrize('sparse_', [False, True])
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@pytest.mark.parametrize(
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"X, X_trans",
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[
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([[2, 55], [1, 55], [2, 55]], [[0, 1, 1], [0, 0, 0], [0, 1, 1]]),
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([['one', 'a'], ['two', 'a'], ['three', 'b'], ['two', 'a']],
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[[0, 0, 0, 0, 0], [0, 0, 0, 0, 1], [0, 1, 0, 0, 0]]),
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]
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)
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def test_one_hot_encoder_inverse_transform_raise_error_with_unknown(
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X, X_trans, sparse_
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):
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"""Check that `inverse_transform` raise an error with unknown samples, no
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dropped feature, and `handle_unknow="error`.
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Non-regression test for:
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https://github.com/scikit-learn/scikit-learn/issues/14934
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"""
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enc = OneHotEncoder(sparse=sparse_).fit(X)
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msg = (
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r"Samples \[(\d )*\d\] can not be inverted when drop=None and "
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r"handle_unknown='error' because they contain all zeros"
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)
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if sparse_:
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# emulate sparse data transform by a one-hot encoder sparse.
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X_trans = _convert_container(X_trans, "sparse")
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with pytest.raises(ValueError, match=msg):
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enc.inverse_transform(X_trans)
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def test_one_hot_encoder_inverse_if_binary():
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X = np.array([['Male', 1],
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['Female', 3],
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['Female', 2]], dtype=object)
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ohe = OneHotEncoder(drop='if_binary', sparse=False)
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X_tr = ohe.fit_transform(X)
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assert_array_equal(ohe.inverse_transform(X_tr), X)
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# check that resetting drop option without refitting does not throw an error
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@pytest.mark.parametrize('drop', ['if_binary', 'first', None])
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@pytest.mark.parametrize('reset_drop', ['if_binary', 'first', None])
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def test_one_hot_encoder_drop_reset(drop, reset_drop):
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X = np.array([['Male', 1],
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['Female', 3],
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['Female', 2]], dtype=object)
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ohe = OneHotEncoder(drop=drop, sparse=False)
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ohe.fit(X)
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X_tr = ohe.transform(X)
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feature_names = ohe.get_feature_names()
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ohe.set_params(drop=reset_drop)
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assert_array_equal(ohe.inverse_transform(X_tr), X)
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|
assert_allclose(ohe.transform(X), X_tr)
|
||
|
|
assert_array_equal(ohe.get_feature_names(), feature_names)
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize("method", ['fit', 'fit_transform'])
|
||
|
|
@pytest.mark.parametrize("X", [
|
||
|
|
[1, 2],
|
||
|
|
np.array([3., 4.])
|
||
|
|
])
|
||
|
|
def test_X_is_not_1D(X, method):
|
||
|
|
oh = OneHotEncoder()
|
||
|
|
|
||
|
|
msg = ("Expected 2D array, got 1D array instead")
|
||
|
|
with pytest.raises(ValueError, match=msg):
|
||
|
|
getattr(oh, method)(X)
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize("method", ['fit', 'fit_transform'])
|
||
|
|
def test_X_is_not_1D_pandas(method):
|
||
|
|
pd = pytest.importorskip('pandas')
|
||
|
|
X = pd.Series([6, 3, 4, 6])
|
||
|
|
oh = OneHotEncoder()
|
||
|
|
|
||
|
|
msg = ("Expected 2D array, got 1D array instead")
|
||
|
|
with pytest.raises(ValueError, match=msg):
|
||
|
|
getattr(oh, method)(X)
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize("X, cat_exp, cat_dtype", [
|
||
|
|
([['abc', 55], ['def', 55]], [['abc', 'def'], [55]], np.object_),
|
||
|
|
(np.array([[1, 2], [3, 2]]), [[1, 3], [2]], np.integer),
|
||
|
|
(np.array([['A', 'cat'], ['B', 'cat']], dtype=object),
|
||
|
|
[['A', 'B'], ['cat']], np.object_),
|
||
|
|
(np.array([['A', 'cat'], ['B', 'cat']]),
|
||
|
|
[['A', 'B'], ['cat']], np.str_),
|
||
|
|
(np.array([[1, 2], [np.nan, 2]]), [[1, np.nan], [2]], np.float_),
|
||
|
|
(np.array([['A', np.nan], [None, np.nan]], dtype=object),
|
||
|
|
[['A', None], [np.nan]], np.object_),
|
||
|
|
(np.array([['A', float('nan')], [None, float('nan')]], dtype=object),
|
||
|
|
[['A', None], [float('nan')]], np.object_),
|
||
|
|
], ids=['mixed', 'numeric', 'object', 'string', 'missing-float',
|
||
|
|
'missing-np.nan-object', 'missing-float-nan-object'])
|
||
|
|
def test_one_hot_encoder_categories(X, cat_exp, cat_dtype):
|
||
|
|
# order of categories should not depend on order of samples
|
||
|
|
for Xi in [X, X[::-1]]:
|
||
|
|
enc = OneHotEncoder(categories='auto')
|
||
|
|
enc.fit(Xi)
|
||
|
|
# assert enc.categories == 'auto'
|
||
|
|
assert isinstance(enc.categories_, list)
|
||
|
|
for res, exp in zip(enc.categories_, cat_exp):
|
||
|
|
res_list = res.tolist()
|
||
|
|
if is_scalar_nan(exp[-1]):
|
||
|
|
assert is_scalar_nan(res_list[-1])
|
||
|
|
assert res_list[:-1] == exp[:-1]
|
||
|
|
else:
|
||
|
|
assert res.tolist() == exp
|
||
|
|
assert np.issubdtype(res.dtype, cat_dtype)
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize("X, X2, cats, cat_dtype", [
|
||
|
|
(np.array([['a', 'b']], dtype=object).T,
|
||
|
|
np.array([['a', 'd']], dtype=object).T,
|
||
|
|
[['a', 'b', 'c']], np.object_),
|
||
|
|
(np.array([[1, 2]], dtype='int64').T,
|
||
|
|
np.array([[1, 4]], dtype='int64').T,
|
||
|
|
[[1, 2, 3]], np.int64),
|
||
|
|
(np.array([['a', 'b']], dtype=object).T,
|
||
|
|
np.array([['a', 'd']], dtype=object).T,
|
||
|
|
[np.array(['a', 'b', 'c'])], np.object_),
|
||
|
|
(np.array([[None, 'a']], dtype=object).T,
|
||
|
|
np.array([[None, 'b']], dtype=object).T,
|
||
|
|
[[None, 'a', 'z']], object),
|
||
|
|
(np.array([['a', 'b']], dtype=object).T,
|
||
|
|
np.array([['a', np.nan]], dtype=object).T,
|
||
|
|
[['a', 'b', 'z']], object),
|
||
|
|
(np.array([['a', None]], dtype=object).T,
|
||
|
|
np.array([['a', np.nan]], dtype=object).T,
|
||
|
|
[['a', None, 'z']], object),
|
||
|
|
(np.array([['a', np.nan]], dtype=object).T,
|
||
|
|
np.array([['a', None]], dtype=object).T,
|
||
|
|
[['a', np.nan, 'z']], object),
|
||
|
|
], ids=['object', 'numeric', 'object-string',
|
||
|
|
'object-string-none', 'object-string-nan',
|
||
|
|
'object-None-and-nan', 'object-nan-and-None'])
|
||
|
|
def test_one_hot_encoder_specified_categories(X, X2, cats, cat_dtype):
|
||
|
|
enc = OneHotEncoder(categories=cats)
|
||
|
|
exp = np.array([[1., 0., 0.],
|
||
|
|
[0., 1., 0.]])
|
||
|
|
assert_array_equal(enc.fit_transform(X).toarray(), exp)
|
||
|
|
assert list(enc.categories[0]) == list(cats[0])
|
||
|
|
assert enc.categories_[0].tolist() == list(cats[0])
|
||
|
|
# manually specified categories should have same dtype as
|
||
|
|
# the data when coerced from lists
|
||
|
|
assert enc.categories_[0].dtype == cat_dtype
|
||
|
|
|
||
|
|
# when specifying categories manually, unknown categories should already
|
||
|
|
# raise when fitting
|
||
|
|
enc = OneHotEncoder(categories=cats)
|
||
|
|
with pytest.raises(ValueError, match="Found unknown categories"):
|
||
|
|
enc.fit(X2)
|
||
|
|
enc = OneHotEncoder(categories=cats, handle_unknown='ignore')
|
||
|
|
exp = np.array([[1., 0., 0.], [0., 0., 0.]])
|
||
|
|
assert_array_equal(enc.fit(X2).transform(X2).toarray(), exp)
|
||
|
|
|
||
|
|
|
||
|
|
def test_one_hot_encoder_unsorted_categories():
|
||
|
|
X = np.array([['a', 'b']], dtype=object).T
|
||
|
|
|
||
|
|
enc = OneHotEncoder(categories=[['b', 'a', 'c']])
|
||
|
|
exp = np.array([[0., 1., 0.],
|
||
|
|
[1., 0., 0.]])
|
||
|
|
assert_array_equal(enc.fit(X).transform(X).toarray(), exp)
|
||
|
|
assert_array_equal(enc.fit_transform(X).toarray(), exp)
|
||
|
|
assert enc.categories_[0].tolist() == ['b', 'a', 'c']
|
||
|
|
assert np.issubdtype(enc.categories_[0].dtype, np.object_)
|
||
|
|
|
||
|
|
# unsorted passed categories still raise for numerical values
|
||
|
|
X = np.array([[1, 2]]).T
|
||
|
|
enc = OneHotEncoder(categories=[[2, 1, 3]])
|
||
|
|
msg = 'Unsorted categories are not supported'
|
||
|
|
with pytest.raises(ValueError, match=msg):
|
||
|
|
enc.fit_transform(X)
|
||
|
|
|
||
|
|
# np.nan must be the last category in categories[0] to be considered sorted
|
||
|
|
X = np.array([[1, 2, np.nan]]).T
|
||
|
|
enc = OneHotEncoder(categories=[[1, np.nan, 2]])
|
||
|
|
with pytest.raises(ValueError, match=msg):
|
||
|
|
enc.fit_transform(X)
|
||
|
|
|
||
|
|
|
||
|
|
def test_one_hot_encoder_specified_categories_mixed_columns():
|
||
|
|
# multiple columns
|
||
|
|
X = np.array([['a', 'b'], [0, 2]], dtype=object).T
|
||
|
|
enc = OneHotEncoder(categories=[['a', 'b', 'c'], [0, 1, 2]])
|
||
|
|
exp = np.array([[1., 0., 0., 1., 0., 0.],
|
||
|
|
[0., 1., 0., 0., 0., 1.]])
|
||
|
|
assert_array_equal(enc.fit_transform(X).toarray(), exp)
|
||
|
|
assert enc.categories_[0].tolist() == ['a', 'b', 'c']
|
||
|
|
assert np.issubdtype(enc.categories_[0].dtype, np.object_)
|
||
|
|
assert enc.categories_[1].tolist() == [0, 1, 2]
|
||
|
|
# integer categories but from object dtype data
|
||
|
|
assert np.issubdtype(enc.categories_[1].dtype, np.object_)
|
||
|
|
|
||
|
|
|
||
|
|
def test_one_hot_encoder_pandas():
|
||
|
|
pd = pytest.importorskip('pandas')
|
||
|
|
|
||
|
|
X_df = pd.DataFrame({'A': ['a', 'b'], 'B': [1, 2]})
|
||
|
|
|
||
|
|
Xtr = check_categorical_onehot(X_df)
|
||
|
|
assert_allclose(Xtr, [[1, 0, 1, 0], [0, 1, 0, 1]])
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize("drop, expected_names",
|
||
|
|
[('first', ['x0_c', 'x2_b']),
|
||
|
|
('if_binary', ['x0_c', 'x1_2', 'x2_b']),
|
||
|
|
(['c', 2, 'b'], ['x0_b', 'x2_a'])],
|
||
|
|
ids=['first', 'binary', 'manual'])
|
||
|
|
def test_one_hot_encoder_feature_names_drop(drop, expected_names):
|
||
|
|
X = [['c', 2, 'a'],
|
||
|
|
['b', 2, 'b']]
|
||
|
|
|
||
|
|
ohe = OneHotEncoder(drop=drop)
|
||
|
|
ohe.fit(X)
|
||
|
|
feature_names = ohe.get_feature_names()
|
||
|
|
assert isinstance(feature_names, np.ndarray)
|
||
|
|
assert_array_equal(expected_names, feature_names)
|
||
|
|
|
||
|
|
|
||
|
|
def test_one_hot_encoder_drop_equals_if_binary():
|
||
|
|
# Canonical case
|
||
|
|
X = [[10, 'yes'],
|
||
|
|
[20, 'no'],
|
||
|
|
[30, 'yes']]
|
||
|
|
expected = np.array([[1., 0., 0., 1.],
|
||
|
|
[0., 1., 0., 0.],
|
||
|
|
[0., 0., 1., 1.]])
|
||
|
|
expected_drop_idx = np.array([None, 0])
|
||
|
|
|
||
|
|
ohe = OneHotEncoder(drop='if_binary', sparse=False)
|
||
|
|
result = ohe.fit_transform(X)
|
||
|
|
assert_array_equal(ohe.drop_idx_, expected_drop_idx)
|
||
|
|
assert_allclose(result, expected)
|
||
|
|
|
||
|
|
# with only one cat, the behaviour is equivalent to drop=None
|
||
|
|
X = [['true', 'a'],
|
||
|
|
['false', 'a'],
|
||
|
|
['false', 'a']]
|
||
|
|
expected = np.array([[1., 1.],
|
||
|
|
[0., 1.],
|
||
|
|
[0., 1.]])
|
||
|
|
expected_drop_idx = np.array([0, None])
|
||
|
|
|
||
|
|
ohe = OneHotEncoder(drop='if_binary', sparse=False)
|
||
|
|
result = ohe.fit_transform(X)
|
||
|
|
assert_array_equal(ohe.drop_idx_, expected_drop_idx)
|
||
|
|
assert_allclose(result, expected)
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize("X", [
|
||
|
|
[['abc', 2, 55], ['def', 1, 55]],
|
||
|
|
np.array([[10, 2, 55], [20, 1, 55]]),
|
||
|
|
np.array([['a', 'B', 'cat'], ['b', 'A', 'cat']], dtype=object)
|
||
|
|
], ids=['mixed', 'numeric', 'object'])
|
||
|
|
def test_ordinal_encoder(X):
|
||
|
|
enc = OrdinalEncoder()
|
||
|
|
exp = np.array([[0, 1, 0],
|
||
|
|
[1, 0, 0]], dtype='int64')
|
||
|
|
assert_array_equal(enc.fit_transform(X), exp.astype('float64'))
|
||
|
|
enc = OrdinalEncoder(dtype='int64')
|
||
|
|
assert_array_equal(enc.fit_transform(X), exp)
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize("X, X2, cats, cat_dtype", [
|
||
|
|
(np.array([['a', 'b']], dtype=object).T,
|
||
|
|
np.array([['a', 'd']], dtype=object).T,
|
||
|
|
[['a', 'b', 'c']], np.object_),
|
||
|
|
(np.array([[1, 2]], dtype='int64').T,
|
||
|
|
np.array([[1, 4]], dtype='int64').T,
|
||
|
|
[[1, 2, 3]], np.int64),
|
||
|
|
(np.array([['a', 'b']], dtype=object).T,
|
||
|
|
np.array([['a', 'd']], dtype=object).T,
|
||
|
|
[np.array(['a', 'b', 'c'])], np.object_),
|
||
|
|
], ids=['object', 'numeric', 'object-string-cat'])
|
||
|
|
def test_ordinal_encoder_specified_categories(X, X2, cats, cat_dtype):
|
||
|
|
enc = OrdinalEncoder(categories=cats)
|
||
|
|
exp = np.array([[0.], [1.]])
|
||
|
|
assert_array_equal(enc.fit_transform(X), exp)
|
||
|
|
assert list(enc.categories[0]) == list(cats[0])
|
||
|
|
assert enc.categories_[0].tolist() == list(cats[0])
|
||
|
|
# manually specified categories should have same dtype as
|
||
|
|
# the data when coerced from lists
|
||
|
|
assert enc.categories_[0].dtype == cat_dtype
|
||
|
|
|
||
|
|
# when specifying categories manually, unknown categories should already
|
||
|
|
# raise when fitting
|
||
|
|
enc = OrdinalEncoder(categories=cats)
|
||
|
|
with pytest.raises(ValueError, match="Found unknown categories"):
|
||
|
|
enc.fit(X2)
|
||
|
|
|
||
|
|
|
||
|
|
def test_ordinal_encoder_inverse():
|
||
|
|
X = [['abc', 2, 55], ['def', 1, 55]]
|
||
|
|
enc = OrdinalEncoder()
|
||
|
|
X_tr = enc.fit_transform(X)
|
||
|
|
exp = np.array(X, dtype=object)
|
||
|
|
assert_array_equal(enc.inverse_transform(X_tr), exp)
|
||
|
|
|
||
|
|
# incorrect shape raises
|
||
|
|
X_tr = np.array([[0, 1, 1, 2], [1, 0, 1, 0]])
|
||
|
|
msg = re.escape('Shape of the passed X data is not correct')
|
||
|
|
with pytest.raises(ValueError, match=msg):
|
||
|
|
enc.inverse_transform(X_tr)
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize("X", [np.array([[1, np.nan]]).T,
|
||
|
|
np.array([['a', np.nan]], dtype=object).T],
|
||
|
|
ids=['numeric', 'object'])
|
||
|
|
def test_ordinal_encoder_raise_missing(X):
|
||
|
|
ohe = OrdinalEncoder()
|
||
|
|
|
||
|
|
with pytest.raises(ValueError, match="Input contains NaN"):
|
||
|
|
ohe.fit(X)
|
||
|
|
|
||
|
|
with pytest.raises(ValueError, match="Input contains NaN"):
|
||
|
|
ohe.fit_transform(X)
|
||
|
|
|
||
|
|
ohe.fit(X[:1, :])
|
||
|
|
|
||
|
|
with pytest.raises(ValueError, match="Input contains NaN"):
|
||
|
|
ohe.transform(X)
|
||
|
|
|
||
|
|
|
||
|
|
def test_ordinal_encoder_handle_unknowns_string():
|
||
|
|
enc = OrdinalEncoder(handle_unknown='use_encoded_value', unknown_value=-2)
|
||
|
|
X_fit = np.array([['a', 'x'], ['b', 'y'], ['c', 'z']], dtype=object)
|
||
|
|
X_trans = np.array([['c', 'xy'], ['bla', 'y'], ['a', 'x']], dtype=object)
|
||
|
|
enc.fit(X_fit)
|
||
|
|
|
||
|
|
X_trans_enc = enc.transform(X_trans)
|
||
|
|
exp = np.array([[2, -2], [-2, 1], [0, 0]], dtype='int64')
|
||
|
|
assert_array_equal(X_trans_enc, exp)
|
||
|
|
|
||
|
|
X_trans_inv = enc.inverse_transform(X_trans_enc)
|
||
|
|
inv_exp = np.array([['c', None], [None, 'y'], ['a', 'x']], dtype=object)
|
||
|
|
assert_array_equal(X_trans_inv, inv_exp)
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize('dtype', [float, int])
|
||
|
|
def test_ordinal_encoder_handle_unknowns_numeric(dtype):
|
||
|
|
enc = OrdinalEncoder(handle_unknown='use_encoded_value',
|
||
|
|
unknown_value=-999)
|
||
|
|
X_fit = np.array([[1, 7], [2, 8], [3, 9]], dtype=dtype)
|
||
|
|
X_trans = np.array([[3, 12], [23, 8], [1, 7]], dtype=dtype)
|
||
|
|
enc.fit(X_fit)
|
||
|
|
|
||
|
|
X_trans_enc = enc.transform(X_trans)
|
||
|
|
exp = np.array([[2, -999], [-999, 1], [0, 0]], dtype='int64')
|
||
|
|
assert_array_equal(X_trans_enc, exp)
|
||
|
|
|
||
|
|
X_trans_inv = enc.inverse_transform(X_trans_enc)
|
||
|
|
inv_exp = np.array([[3, None], [None, 8], [1, 7]], dtype=object)
|
||
|
|
assert_array_equal(X_trans_inv, inv_exp)
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize(
|
||
|
|
"params, err_type, err_msg",
|
||
|
|
[
|
||
|
|
(
|
||
|
|
{"handle_unknown": "use_encoded_value"},
|
||
|
|
TypeError,
|
||
|
|
"unknown_value should be an integer or np.nan when handle_unknown "
|
||
|
|
"is 'use_encoded_value', got None.",
|
||
|
|
),
|
||
|
|
(
|
||
|
|
{"unknown_value": -2},
|
||
|
|
TypeError,
|
||
|
|
"unknown_value should only be set when handle_unknown is "
|
||
|
|
"'use_encoded_value', got -2.",
|
||
|
|
),
|
||
|
|
(
|
||
|
|
{"handle_unknown": "use_encoded_value", "unknown_value": "bla"},
|
||
|
|
TypeError,
|
||
|
|
"unknown_value should be an integer or np.nan when handle_unknown "
|
||
|
|
"is 'use_encoded_value', got bla.",
|
||
|
|
),
|
||
|
|
(
|
||
|
|
{"handle_unknown": "use_encoded_value", "unknown_value": 1},
|
||
|
|
ValueError,
|
||
|
|
"The used value for unknown_value (1) is one of the values "
|
||
|
|
"already used for encoding the seen categories.",
|
||
|
|
),
|
||
|
|
(
|
||
|
|
{"handle_unknown": "ignore"},
|
||
|
|
ValueError,
|
||
|
|
"handle_unknown should be either 'error' or 'use_encoded_value', "
|
||
|
|
"got ignore.",
|
||
|
|
),
|
||
|
|
],
|
||
|
|
)
|
||
|
|
def test_ordinal_encoder_handle_unknowns_raise(params, err_type, err_msg):
|
||
|
|
# Check error message when validating input parameters
|
||
|
|
X = np.array([['a', 'x'], ['b', 'y']], dtype=object)
|
||
|
|
|
||
|
|
encoder = OrdinalEncoder(**params)
|
||
|
|
with pytest.raises(err_type, match=err_msg):
|
||
|
|
encoder.fit(X)
|
||
|
|
|
||
|
|
|
||
|
|
def test_ordinal_encoder_handle_unknowns_nan():
|
||
|
|
# Make sure unknown_value=np.nan properly works
|
||
|
|
|
||
|
|
enc = OrdinalEncoder(handle_unknown='use_encoded_value',
|
||
|
|
unknown_value=np.nan)
|
||
|
|
|
||
|
|
X_fit = np.array([[1], [2], [3]])
|
||
|
|
enc.fit(X_fit)
|
||
|
|
X_trans = enc.transform([[1], [2], [4]])
|
||
|
|
assert_array_equal(X_trans, [[0], [1], [np.nan]])
|
||
|
|
|
||
|
|
|
||
|
|
def test_ordinal_encoder_handle_unknowns_nan_non_float_dtype():
|
||
|
|
# Make sure an error is raised when unknown_value=np.nan and the dtype
|
||
|
|
# isn't a float dtype
|
||
|
|
enc = OrdinalEncoder(handle_unknown='use_encoded_value',
|
||
|
|
unknown_value=np.nan, dtype=int)
|
||
|
|
|
||
|
|
X_fit = np.array([[1], [2], [3]])
|
||
|
|
with pytest.raises(ValueError,
|
||
|
|
match="dtype parameter should be a float dtype"):
|
||
|
|
enc.fit(X_fit)
|
||
|
|
|
||
|
|
|
||
|
|
def test_ordinal_encoder_raise_categories_shape():
|
||
|
|
|
||
|
|
X = np.array([['Low', 'Medium', 'High', 'Medium', 'Low']], dtype=object).T
|
||
|
|
cats = ['Low', 'Medium', 'High']
|
||
|
|
enc = OrdinalEncoder(categories=cats)
|
||
|
|
msg = ("Shape mismatch: if categories is an array,")
|
||
|
|
|
||
|
|
with pytest.raises(ValueError, match=msg):
|
||
|
|
enc.fit(X)
|
||
|
|
|
||
|
|
|
||
|
|
def test_encoder_dtypes():
|
||
|
|
# check that dtypes are preserved when determining categories
|
||
|
|
enc = OneHotEncoder(categories='auto')
|
||
|
|
exp = np.array([[1., 0., 1., 0.], [0., 1., 0., 1.]], dtype='float64')
|
||
|
|
|
||
|
|
for X in [np.array([[1, 2], [3, 4]], dtype='int64'),
|
||
|
|
np.array([[1, 2], [3, 4]], dtype='float64'),
|
||
|
|
np.array([['a', 'b'], ['c', 'd']]), # unicode dtype
|
||
|
|
np.array([[b'a', b'b'], [b'c', b'd']]), # string dtype
|
||
|
|
np.array([[1, 'a'], [3, 'b']], dtype='object')]:
|
||
|
|
enc.fit(X)
|
||
|
|
assert all([enc.categories_[i].dtype == X.dtype for i in range(2)])
|
||
|
|
assert_array_equal(enc.transform(X).toarray(), exp)
|
||
|
|
|
||
|
|
X = [[1, 2], [3, 4]]
|
||
|
|
enc.fit(X)
|
||
|
|
assert all([np.issubdtype(enc.categories_[i].dtype, np.integer)
|
||
|
|
for i in range(2)])
|
||
|
|
assert_array_equal(enc.transform(X).toarray(), exp)
|
||
|
|
|
||
|
|
X = [[1, 'a'], [3, 'b']]
|
||
|
|
enc.fit(X)
|
||
|
|
assert all([enc.categories_[i].dtype == 'object' for i in range(2)])
|
||
|
|
assert_array_equal(enc.transform(X).toarray(), exp)
|
||
|
|
|
||
|
|
|
||
|
|
def test_encoder_dtypes_pandas():
|
||
|
|
# check dtype (similar to test_categorical_encoder_dtypes for dataframes)
|
||
|
|
pd = pytest.importorskip('pandas')
|
||
|
|
|
||
|
|
enc = OneHotEncoder(categories='auto')
|
||
|
|
exp = np.array([[1., 0., 1., 0., 1., 0.],
|
||
|
|
[0., 1., 0., 1., 0., 1.]], dtype='float64')
|
||
|
|
|
||
|
|
X = pd.DataFrame({'A': [1, 2], 'B': [3, 4], 'C': [5, 6]}, dtype='int64')
|
||
|
|
enc.fit(X)
|
||
|
|
assert all([enc.categories_[i].dtype == 'int64' for i in range(2)])
|
||
|
|
assert_array_equal(enc.transform(X).toarray(), exp)
|
||
|
|
|
||
|
|
X = pd.DataFrame({'A': [1, 2], 'B': ['a', 'b'], 'C': [3., 4.]})
|
||
|
|
X_type = [X['A'].dtype, X['B'].dtype, X['C'].dtype]
|
||
|
|
enc.fit(X)
|
||
|
|
assert all([enc.categories_[i].dtype == X_type[i] for i in range(3)])
|
||
|
|
assert_array_equal(enc.transform(X).toarray(), exp)
|
||
|
|
|
||
|
|
|
||
|
|
def test_one_hot_encoder_warning():
|
||
|
|
enc = OneHotEncoder()
|
||
|
|
X = [['Male', 1], ['Female', 3]]
|
||
|
|
np.testing.assert_no_warnings(enc.fit_transform, X)
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize("missing_value", [np.nan, None, float('nan')])
|
||
|
|
def test_one_hot_encoder_drop_manual(missing_value):
|
||
|
|
cats_to_drop = ['def', 12, 3, 56, missing_value]
|
||
|
|
enc = OneHotEncoder(drop=cats_to_drop)
|
||
|
|
X = [['abc', 12, 2, 55, 'a'],
|
||
|
|
['def', 12, 1, 55, 'a'],
|
||
|
|
['def', 12, 3, 56, missing_value]]
|
||
|
|
trans = enc.fit_transform(X).toarray()
|
||
|
|
exp = [[1, 0, 1, 1, 1],
|
||
|
|
[0, 1, 0, 1, 1],
|
||
|
|
[0, 0, 0, 0, 0]]
|
||
|
|
assert_array_equal(trans, exp)
|
||
|
|
assert enc.drop is cats_to_drop
|
||
|
|
|
||
|
|
dropped_cats = [cat[feature]
|
||
|
|
for cat, feature in zip(enc.categories_,
|
||
|
|
enc.drop_idx_)]
|
||
|
|
X_inv_trans = enc.inverse_transform(trans)
|
||
|
|
X_array = np.array(X, dtype=object)
|
||
|
|
|
||
|
|
# last value is np.nan
|
||
|
|
if is_scalar_nan(cats_to_drop[-1]):
|
||
|
|
assert_array_equal(dropped_cats[:-1], cats_to_drop[:-1])
|
||
|
|
assert is_scalar_nan(dropped_cats[-1])
|
||
|
|
assert is_scalar_nan(cats_to_drop[-1])
|
||
|
|
# do not include the last column which includes missing values
|
||
|
|
assert_array_equal(X_array[:, :-1], X_inv_trans[:, :-1])
|
||
|
|
|
||
|
|
# check last column is the missing value
|
||
|
|
assert_array_equal(X_array[-1, :-1], X_inv_trans[-1, :-1])
|
||
|
|
assert is_scalar_nan(X_array[-1, -1])
|
||
|
|
assert is_scalar_nan(X_inv_trans[-1, -1])
|
||
|
|
else:
|
||
|
|
assert_array_equal(dropped_cats, cats_to_drop)
|
||
|
|
assert_array_equal(X_array, X_inv_trans)
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize(
|
||
|
|
"X_fit, params, err_msg",
|
||
|
|
[([["Male"], ["Female"]], {'drop': 'second'},
|
||
|
|
"Wrong input for parameter `drop`"),
|
||
|
|
([["Male"], ["Female"]], {'drop': 'first', 'handle_unknown': 'ignore'},
|
||
|
|
"`handle_unknown` must be 'error'"),
|
||
|
|
([['abc', 2, 55], ['def', 1, 55], ['def', 3, 59]],
|
||
|
|
{'drop': np.asarray('b', dtype=object)},
|
||
|
|
"Wrong input for parameter `drop`"),
|
||
|
|
([['abc', 2, 55], ['def', 1, 55], ['def', 3, 59]],
|
||
|
|
{'drop': ['ghi', 3, 59]},
|
||
|
|
"The following categories were supposed")]
|
||
|
|
)
|
||
|
|
def test_one_hot_encoder_invalid_params(X_fit, params, err_msg):
|
||
|
|
enc = OneHotEncoder(**params)
|
||
|
|
with pytest.raises(ValueError, match=err_msg):
|
||
|
|
enc.fit(X_fit)
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize('drop', [['abc', 3], ['abc', 3, 41, 'a']])
|
||
|
|
def test_invalid_drop_length(drop):
|
||
|
|
enc = OneHotEncoder(drop=drop)
|
||
|
|
err_msg = "`drop` should have length equal to the number"
|
||
|
|
with pytest.raises(ValueError, match=err_msg):
|
||
|
|
enc.fit([['abc', 2, 55], ['def', 1, 55], ['def', 3, 59]])
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize("density", [True, False],
|
||
|
|
ids=['sparse', 'dense'])
|
||
|
|
@pytest.mark.parametrize("drop", ['first',
|
||
|
|
['a', 2, 'b']],
|
||
|
|
ids=['first', 'manual'])
|
||
|
|
def test_categories(density, drop):
|
||
|
|
ohe_base = OneHotEncoder(sparse=density)
|
||
|
|
ohe_test = OneHotEncoder(sparse=density, drop=drop)
|
||
|
|
X = [['c', 1, 'a'],
|
||
|
|
['a', 2, 'b']]
|
||
|
|
ohe_base.fit(X)
|
||
|
|
ohe_test.fit(X)
|
||
|
|
assert_array_equal(ohe_base.categories_, ohe_test.categories_)
|
||
|
|
if drop == 'first':
|
||
|
|
assert_array_equal(ohe_test.drop_idx_, 0)
|
||
|
|
else:
|
||
|
|
for drop_cat, drop_idx, cat_list in zip(drop,
|
||
|
|
ohe_test.drop_idx_,
|
||
|
|
ohe_test.categories_):
|
||
|
|
assert cat_list[int(drop_idx)] == drop_cat
|
||
|
|
assert isinstance(ohe_test.drop_idx_, np.ndarray)
|
||
|
|
assert ohe_test.drop_idx_.dtype == object
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize('Encoder', [OneHotEncoder, OrdinalEncoder])
|
||
|
|
def test_encoders_has_categorical_tags(Encoder):
|
||
|
|
assert 'categorical' in Encoder()._get_tags()['X_types']
|
||
|
|
|
||
|
|
|
||
|
|
# deliberately omit 'OS' as an invalid combo
|
||
|
|
@pytest.mark.parametrize('input_dtype, category_dtype', ['OO', 'OU',
|
||
|
|
'UO', 'UU', 'US',
|
||
|
|
'SO', 'SU', 'SS'])
|
||
|
|
@pytest.mark.parametrize('array_type', ['list', 'array', 'dataframe'])
|
||
|
|
def test_encoders_string_categories(input_dtype, category_dtype, array_type):
|
||
|
|
"""Check that encoding work with object, unicode, and byte string dtypes.
|
||
|
|
Non-regression test for:
|
||
|
|
https://github.com/scikit-learn/scikit-learn/issues/15616
|
||
|
|
https://github.com/scikit-learn/scikit-learn/issues/15726
|
||
|
|
https://github.com/scikit-learn/scikit-learn/issues/19677
|
||
|
|
"""
|
||
|
|
|
||
|
|
X = np.array([['b'], ['a']], dtype=input_dtype)
|
||
|
|
categories = [np.array(['b', 'a'], dtype=category_dtype)]
|
||
|
|
ohe = OneHotEncoder(categories=categories, sparse=False).fit(X)
|
||
|
|
|
||
|
|
X_test = _convert_container([['a'], ['a'], ['b'], ['a']], array_type,
|
||
|
|
dtype=input_dtype)
|
||
|
|
X_trans = ohe.transform(X_test)
|
||
|
|
|
||
|
|
expected = np.array([[0, 1], [0, 1], [1, 0], [0, 1]])
|
||
|
|
assert_allclose(X_trans, expected)
|
||
|
|
|
||
|
|
oe = OrdinalEncoder(categories=categories).fit(X)
|
||
|
|
X_trans = oe.transform(X_test)
|
||
|
|
|
||
|
|
expected = np.array([[1], [1], [0], [1]])
|
||
|
|
assert_array_equal(X_trans, expected)
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize("missing_value", [np.nan, None])
|
||
|
|
def test_ohe_missing_values_get_feature_names(missing_value):
|
||
|
|
# encoder with missing values with object dtypes
|
||
|
|
X = np.array([['a', 'b', missing_value, 'a', missing_value]],
|
||
|
|
dtype=object).T
|
||
|
|
ohe = OneHotEncoder(sparse=False, handle_unknown='ignore').fit(X)
|
||
|
|
names = ohe.get_feature_names()
|
||
|
|
assert_array_equal(names, ['x0_a', 'x0_b', f'x0_{missing_value}'])
|
||
|
|
|
||
|
|
|
||
|
|
def test_ohe_missing_value_support_pandas():
|
||
|
|
# check support for pandas with mixed dtypes and missing values
|
||
|
|
pd = pytest.importorskip('pandas')
|
||
|
|
df = pd.DataFrame({
|
||
|
|
'col1': ['dog', 'cat', None, 'cat'],
|
||
|
|
'col2': np.array([3, 0, 4, np.nan], dtype=float)
|
||
|
|
}, columns=['col1', 'col2'])
|
||
|
|
expected_df_trans = np.array([
|
||
|
|
[0, 1, 0, 0, 1, 0, 0],
|
||
|
|
[1, 0, 0, 1, 0, 0, 0],
|
||
|
|
[0, 0, 1, 0, 0, 1, 0],
|
||
|
|
[1, 0, 0, 0, 0, 0, 1],
|
||
|
|
])
|
||
|
|
|
||
|
|
Xtr = check_categorical_onehot(df)
|
||
|
|
assert_allclose(Xtr, expected_df_trans)
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize('pd_nan_type', ['pd.NA', 'np.nan'])
|
||
|
|
def test_ohe_missing_value_support_pandas_categorical(pd_nan_type):
|
||
|
|
# checks pandas dataframe with categorical features
|
||
|
|
if pd_nan_type == 'pd.NA':
|
||
|
|
# pd.NA is in pandas 1.0
|
||
|
|
pd = pytest.importorskip('pandas', minversion="1.0")
|
||
|
|
pd_missing_value = pd.NA
|
||
|
|
else: # np.nan
|
||
|
|
pd = pytest.importorskip('pandas')
|
||
|
|
pd_missing_value = np.nan
|
||
|
|
|
||
|
|
df = pd.DataFrame({
|
||
|
|
'col1': pd.Series(['c', 'a', pd_missing_value, 'b', 'a'],
|
||
|
|
dtype='category'),
|
||
|
|
})
|
||
|
|
expected_df_trans = np.array([
|
||
|
|
[0, 0, 1, 0],
|
||
|
|
[1, 0, 0, 0],
|
||
|
|
[0, 0, 0, 1],
|
||
|
|
[0, 1, 0, 0],
|
||
|
|
[1, 0, 0, 0],
|
||
|
|
])
|
||
|
|
|
||
|
|
ohe = OneHotEncoder(sparse=False, handle_unknown='ignore')
|
||
|
|
df_trans = ohe.fit_transform(df)
|
||
|
|
assert_allclose(expected_df_trans, df_trans)
|
||
|
|
|
||
|
|
assert len(ohe.categories_) == 1
|
||
|
|
assert_array_equal(ohe.categories_[0][:-1], ['a', 'b', 'c'])
|
||
|
|
assert np.isnan(ohe.categories_[0][-1])
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize("X_train", [
|
||
|
|
[['AA', 'B']],
|
||
|
|
np.array([['AA', 'B']], dtype='O'),
|
||
|
|
np.array([['AA', 'B']], dtype='U'),
|
||
|
|
])
|
||
|
|
@pytest.mark.parametrize("X_test", [
|
||
|
|
[['A', 'B']],
|
||
|
|
np.array([['A', 'B']], dtype='O'),
|
||
|
|
np.array([['A', 'B']], dtype='U'),
|
||
|
|
])
|
||
|
|
def test_ordinal_encoder_handle_unknown_string_dtypes(X_train, X_test):
|
||
|
|
"""Checks that ordinal encoder transforms string dtypes. Non-regression
|
||
|
|
test for #19872."""
|
||
|
|
enc = OrdinalEncoder(handle_unknown='use_encoded_value', unknown_value=-9)
|
||
|
|
enc.fit(X_train)
|
||
|
|
|
||
|
|
X_trans = enc.transform(X_test)
|
||
|
|
assert_allclose(X_trans, [[-9, 0]])
|