73 lines
2.6 KiB
Python
73 lines
2.6 KiB
Python
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import numpy as np
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import pytest
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from sklearn.feature_selection import VarianceThreshold
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from sklearn.utils._testing import assert_array_equal
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from sklearn.utils.fixes import BSR_CONTAINERS, CSC_CONTAINERS, CSR_CONTAINERS
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data = [[0, 1, 2, 3, 4], [0, 2, 2, 3, 5], [1, 1, 2, 4, 0]]
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data2 = [[-0.13725701]] * 10
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@pytest.mark.parametrize(
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"sparse_container", [None] + BSR_CONTAINERS + CSC_CONTAINERS + CSR_CONTAINERS
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)
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def test_zero_variance(sparse_container):
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# Test VarianceThreshold with default setting, zero variance.
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X = data if sparse_container is None else sparse_container(data)
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sel = VarianceThreshold().fit(X)
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assert_array_equal([0, 1, 3, 4], sel.get_support(indices=True))
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def test_zero_variance_value_error():
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# Test VarianceThreshold with default setting, zero variance, error cases.
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with pytest.raises(ValueError):
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VarianceThreshold().fit([[0, 1, 2, 3]])
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with pytest.raises(ValueError):
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VarianceThreshold().fit([[0, 1], [0, 1]])
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@pytest.mark.parametrize("sparse_container", [None] + CSR_CONTAINERS)
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def test_variance_threshold(sparse_container):
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# Test VarianceThreshold with custom variance.
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X = data if sparse_container is None else sparse_container(data)
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X = VarianceThreshold(threshold=0.4).fit_transform(X)
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assert (len(data), 1) == X.shape
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@pytest.mark.skipif(
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np.var(data2) == 0,
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reason=(
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"This test is not valid for this platform, "
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"as it relies on numerical instabilities."
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),
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)
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@pytest.mark.parametrize(
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"sparse_container", [None] + BSR_CONTAINERS + CSC_CONTAINERS + CSR_CONTAINERS
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)
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def test_zero_variance_floating_point_error(sparse_container):
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# Test that VarianceThreshold(0.0).fit eliminates features that have
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# the same value in every sample, even when floating point errors
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# cause np.var not to be 0 for the feature.
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# See #13691
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X = data2 if sparse_container is None else sparse_container(data2)
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msg = "No feature in X meets the variance threshold 0.00000"
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with pytest.raises(ValueError, match=msg):
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VarianceThreshold().fit(X)
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@pytest.mark.parametrize(
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"sparse_container", [None] + BSR_CONTAINERS + CSC_CONTAINERS + CSR_CONTAINERS
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)
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def test_variance_nan(sparse_container):
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arr = np.array(data, dtype=np.float64)
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# add single NaN and feature should still be included
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arr[0, 0] = np.nan
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# make all values in feature NaN and feature should be rejected
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arr[:, 1] = np.nan
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X = arr if sparse_container is None else sparse_container(arr)
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sel = VarianceThreshold().fit(X)
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assert_array_equal([0, 3, 4], sel.get_support(indices=True))
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