scikit-learn
diff --git a/‎sklearn/feature_selection/tests/test_variance_threshold.py
Lines changed: 30 additions & 19 deletions b/‎sklearn/feature_selection/tests/test_variance_threshold.py
Lines changed: 30 additions & 19 deletions
@@ -1,33 +1,39 @@
 import numpy as np
 import pytest
-from scipy.sparse import bsr_matrix, csc_matrix, csr_matrix
 
 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
 
 
-def test_zero_variance():
+@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))
 
-    for X in [data, csr_matrix(data), csc_matrix(data), bsr_matrix(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]])
 
 
-def test_variance_threshold():
+@pytest.mark.parametrize("sparse_container", [None] + CSR_CONTAINERS)
+def test_variance_threshold(sparse_container):
     # Test VarianceThreshold with custom variance.
-    for X in [data, csr_matrix(data)]:
-        X = VarianceThreshold(threshold=0.4).fit_transform(X)
-        assert (len(data), 1) == X.shape
+    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(
@@ -37,25 +43,30 @@ def test_variance_threshold():
         "as it relies on numerical instabilities."
     ),
 )
-def test_zero_variance_floating_point_error():
+@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)
 
-    for X in [data2, csr_matrix(data2), csc_matrix(data2), bsr_matrix(data2)]:
-        msg = "No feature in X meets the variance threshold 0.00000"
-        with pytest.raises(ValueError, match=msg):
-            VarianceThreshold().fit(X)
 
-
-def test_variance_nan():
+@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
 
-    for X in [arr, csr_matrix(arr), csc_matrix(arr), bsr_matrix(arr)]:
-        sel = VarianceThreshold().fit(X)
-        assert_array_equal([0, 3, 4], sel.get_support(indices=True))
+    X = arr if sparse_container is None else sparse_container(arr)
+    sel = VarianceThreshold().fit(X)
+    assert_array
33F8
_equal([0, 3, 4], sel.get_support(indices=True))