41 lines
1.3 KiB
Python
41 lines
1.3 KiB
Python
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import numpy as np
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import pytest
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from sklearn.utils._testing import assert_allclose
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from sklearn.utils.arrayfuncs import _all_with_any_reduction_axis_1, min_pos
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def test_min_pos():
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# Check that min_pos returns a positive value and that it's consistent
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# between float and double
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X = np.random.RandomState(0).randn(100)
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min_double = min_pos(X)
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min_float = min_pos(X.astype(np.float32))
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assert_allclose(min_double, min_float)
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assert min_double >= 0
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@pytest.mark.parametrize("dtype", [np.float32, np.float64])
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def test_min_pos_no_positive(dtype):
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# Check that the return value of min_pos is the maximum representable
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# value of the input dtype when all input elements are <= 0 (#19328)
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X = np.full(100, -1.0).astype(dtype, copy=False)
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assert min_pos(X) == np.finfo(dtype).max
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@pytest.mark.parametrize(
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"dtype", [np.int16, np.int32, np.int64, np.float32, np.float64]
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)
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@pytest.mark.parametrize("value", [0, 1.5, -1])
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def test_all_with_any_reduction_axis_1(dtype, value):
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# Check that return value is False when there is no row equal to `value`
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X = np.arange(12, dtype=dtype).reshape(3, 4)
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assert not _all_with_any_reduction_axis_1(X, value=value)
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# Make a row equal to `value`
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X[1, :] = value
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assert _all_with_any_reduction_axis_1(X, value=value)
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