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import numpy as np
import pytest
from scipy import sparse
from sklearn.utils.testing import assert_array_equal
from sklearn.utils.testing import assert_raise_message
from sklearn.preprocessing._encoders import _transform_selected
from sklearn.preprocessing.data import Binarizer
def toarray(a):
if hasattr(a, "toarray"):
a = a.toarray()
return a
def _check_transform_selected(X, X_expected, dtype, sel):
for M in (X, sparse.csr_matrix(X)):
Xtr = _transform_selected(M, Binarizer().transform, dtype, sel)
assert_array_equal(toarray(Xtr), X_expected)
@pytest.mark.parametrize("output_dtype", [np.int32, np.float32, np.float64])
@pytest.mark.parametrize("input_dtype", [np.int32, np.float32, np.float64])
def test_transform_selected(output_dtype, input_dtype):
X = np.asarray([[3, 2, 1], [0, 1, 1]], dtype=input_dtype)
X_expected = np.asarray([[1, 2, 1], [0, 1, 1]], dtype=output_dtype)
_check_transform_selected(X, X_expected, output_dtype, [0])
_check_transform_selected(X, X_expected, output_dtype,
[True, False, False])
X_expected = np.asarray([[1, 1, 1], [0, 1, 1]], dtype=output_dtype)
_check_transform_selected(X, X_expected, output_dtype, [0, 1, 2])
_check_transform_selected(X, X_expected, output_dtype, [True, True, True])
_check_transform_selected(X, X_expected, output_dtype, "all")
_check_transform_selected(X, X, output_dtype, [])
_check_transform_selected(X, X, output_dtype, [False, False, False])
@pytest.mark.parametrize("output_dtype", [np.int32, np.float32, np.float64])
@pytest.mark.parametrize("input_dtype", [np.int32, np.float32, np.float64])
def test_transform_selected_copy_arg(output_dtype, input_dtype):
# transformer that alters X
def _mutating_transformer(X):
X[0, 0] = X[0, 0] + 1
return X
original_X = np.asarray([[1, 2], [3, 4]], dtype=input_dtype)
expected_Xtr = np.asarray([[2, 2], [3, 4]], dtype=output_dtype)
X = original_X.copy()
Xtr = _transform_selected(X, _mutating_transformer, output_dtype,
copy=True, selected='all')
assert_array_equal(toarray(X), toarray(original_X))
assert_array_equal(toarray(Xtr), expected_Xtr)
def test_transform_selected_retain_order():
X = [[-1, 1], [2, -2]]
assert_raise_message(ValueError,
"The retain_order option can only be set to True "
"for dense matrices.",
_transform_selected, sparse.csr_matrix(X),
Binarizer().transform, dtype=np.int, selected=[0],
retain_order=True)
def transform(X):
return np.hstack((X, [[0], [0]]))
assert_raise_message(ValueError,
"The retain_order option can only be set to True "
"if the dimensions of the input array match the "
"dimensions of the transformed array.",
_transform_selected, X, transform, dtype=np.int,
selected=[0], retain_order=True)
X_expected = [[-1, 1], [2, 0]]
Xtr = _transform_selected(X, Binarizer().transform, dtype=np.int,
selected=[1], retain_order=True)
assert_array_equal(toarray(Xtr), X_expected)
X_expected = [[0, 1], [1, -2]]
Xtr = _transform_selected(X, Binarizer().transform, dtype=np.int,
selected=[0], retain_order=True)
assert_array_equal(toarray(Xtr), X_expected)
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