8000 MAINT validate parameter in VarianceThreshold (#23581) · scikit-learn/scikit-learn@b60832d · GitHub
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MAINT validate parameter in VarianceThreshold (#23581)
Co-authored-by: Sangam Swadi K <sangamswadik@users.noreply.github.com> Co-authored-by: Jérémie du Boisberranger <34657725+jeremiedbb@users.noreply.github.com>
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‎sklearn/feature_selection/_variance_threshold.py

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@@ -1,11 +1,13 @@
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# Author: Lars Buitinck
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# License: 3-clause BSD
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from numbers import Real
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import numpy as np
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from ..base import BaseEstimator
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from ._base import SelectorMixin
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from ..utils.sparsefuncs import mean_variance_axis, min_max_axis
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from ..utils.validation import check_is_fitted
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from ..utils._param_validation import Interval
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class VarianceThreshold(SelectorMixin, BaseEstimator):
@@ -67,6 +69,8 @@ class VarianceThreshold(SelectorMixin, BaseEstimator):
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[1, 1]])
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"""
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_parameter_constraints = {"threshold": [Interval(Real, 0, None, closed="left")]}
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def __init__(self, threshold=0.0):
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self.threshold = threshold
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@@ -88,6 +92,7 @@ def fit(self, X, y=None):
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self : object
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Returns the instance itself.
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"""
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self._validate_params()
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X = self._validate_data(
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X,
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accept_sparse=("csr", "csc"),
@@ -110,8 +115,6 @@ def fit(self, X, y=None):
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# for constant features
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compare_arr = np.array([self.variances_, peak_to_peaks])
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self.variances_ = np.nanmin(compare_arr, axis=0)
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elif self.threshold < 0.0:
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raise ValueError(f"Threshold must be non-negative. Got: {self.threshold}")
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if np.all(~np.isfinite(self.variances_) | (self.variances_ <= self.threshold)):
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msg = "No feature in X meets the variance threshold {0:.5f}"

‎sklearn/feature_selection/tests/test_variance_threshold.py

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Original file line numberDiff line numberDiff line change
@@ -32,15 +32,6 @@ def test_variance_threshold():
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assert (len(data), 1) == X.shape
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@pytest.mark.parametrize("X", [data, csr_matrix(data)])
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def test_variance_negative(X):
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"""Test VarianceThreshold with negative variance."""
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var_threshold = VarianceThreshold(threshold=-1.0)
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msg = r"^Threshold must be non-negative. Got: -1.0$"
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with pytest.raises(ValueError, match=msg):
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var_threshold.fit(X)
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@pytest.mark.skipif(
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np.var(data2) == 0,
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reason=(

‎sklearn/tests/test_common.py

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@@ -609,7 +609,6 @@ def test_estimators_do_not_raise_errors_in_init_or_set_params(Estimator):
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"TransformedTargetRegressor",
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"TruncatedSVD",
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"TweedieRegressor",
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"VarianceThreshold",
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"VotingClassifier",
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"VotingRegressor",
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]

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