90 lines
3.5 KiB
Python
90 lines
3.5 KiB
Python
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import numpy as np
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import pytest
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from scipy import sparse
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from sklearn.utils.testing import assert_array_equal
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from sklearn.utils.testing import assert_raise_message
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from sklearn.preprocessing._encoders import _transform_selected
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from sklearn.preprocessing.data import Binarizer
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def toarray(a):
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if hasattr(a, "toarray"):
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a = a.toarray()
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return a
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def _check_transform_selected(X, X_expected, dtype, sel):
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for M in (X, sparse.csr_matrix(X)):
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Xtr = _transform_selected(M, Binarizer().transform, dtype, sel)
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assert_array_equal(toarray(Xtr), X_expected)
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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_transform_selected(output_dtype, input_dtype):
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X = np.asarray([[3, 2, 1], [0, 1, 1]], dtype=input_dtype)
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X_expected = np.asarray([[1, 2, 1], [0, 1, 1]], dtype=output_dtype)
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_check_transform_selected(X, X_expected, output_dtype, [0])
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_check_transform_selected(X, X_expected, output_dtype,
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[True, False, False])
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X_expected = np.asarray([[1, 1, 1], [0, 1, 1]], dtype=output_dtype)
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_check_transform_selected(X, X_expected, output_dtype, [0, 1, 2])
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_check_transform_selected(X, X_expected, output_dtype, [True, True, True])
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_check_transform_selected(X, X_expected, output_dtype, "all")
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_check_transform_selected(X, X, output_dtype, [])
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_check_transform_selected(X, X, output_dtype, [False, False, False])
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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_transform_selected_copy_arg(output_dtype, input_dtype):
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# transformer that alters X
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def _mutating_transformer(X):
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X[0, 0] = X[0, 0] + 1
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return X
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original_X = np.asarray([[1, 2], [3, 4]], dtype=input_dtype)
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expected_Xtr = np.asarray([[2, 2], [3, 4]], dtype=output_dtype)
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X = original_X.copy()
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Xtr = _transform_selected(X, _mutating_transformer, output_dtype,
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copy=True, selected='all')
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assert_array_equal(toarray(X), toarray(original_X))
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assert_array_equal(toarray(Xtr), expected_Xtr)
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def test_transform_selected_retain_order():
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X = [[-1, 1], [2, -2]]
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assert_raise_message(ValueError,
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"The retain_order option can only be set to True "
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"for dense matrices.",
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_transform_selected, sparse.csr_matrix(X),
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Binarizer().transform, dtype=np.int, selected=[0],
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retain_order=True)
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def transform(X):
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return np.hstack((X, [[0], [0]]))
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assert_raise_message(ValueError,
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"The retain_order option can only be set to True "
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"if the dimensions of the input array match the "
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"dimensions of the transformed array.",
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_transform_selected, X, transform, dtype=np.int,
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selected=[0], retain_order=True)
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X_expected = [[-1, 1], [2, 0]]
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Xtr = _transform_selected(X, Binarizer().transform, dtype=np.int,
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selected=[1], retain_order=True)
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assert_array_equal(toarray(Xtr), X_expected)
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X_expected = [[0, 1], [1, -2]]
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Xtr = _transform_selected(X, Binarizer().transform, dtype=np.int,
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selected=[0], retain_order=True)
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assert_array_equal(toarray(Xtr), X_expected)
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