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