TST Extend tests for scipy.sparse.*array in sklearn/utils/tests/test_class_weight.py (scikit-learn#27188)

Author: yuanx749

sklearn/utils/class_weight.py

@@ -157,10 +157,11 @@ def compute_sample_weight(class_weight, y, *, indices=None):
 
     expanded_class_weight = []
     for k in range(n_outputs):
-        y_full = y[:, k]
-        if sparse.issparse(y_full):
+        if sparse.issparse(y):
             # Ok to densify a single column at a time
-            y_full = y_full.toarray().flatten()
+            y_full = y[:, [k]].toarray().flatten()
+        else:
+            y_full = y[:, k]
         classes_full = np.unique(y_full)
         classes_missing = None
 

sklearn/utils/tests/test_class_weight.py

@@ -1,13 +1,13 @@
 import numpy as np
 import pytest
 from numpy.testing import assert_allclose
-from scipy import sparse
 
 from sklearn.datasets import make_blobs
 from sklearn.linear_model import LogisticRegression
 from sklearn.tree import DecisionTreeClassifier
 from sklearn.utils._testing import assert_almost_equal, assert_array_almost_equal
 from sklearn.utils.class_weight import compute_class_weight, compute_sample_weight
+from sklearn.utils.fixes import CSC_CONTAINERS
 
 
 def test_compute_class_weight():
@@ -308,8 +308,9 @@ def test_class_weight_does_not_contains_more_classes():
     tree.fit([[0, 0, 1], [1, 0, 1], [1, 2, 0]], [0, 0, 1])
 
 
-def test_compute_sample_weight_sparse():
+@pytest.mark.parametrize("csc_container", CSC_CONTAINERS)
+def test_compute_sample_weight_sparse(csc_container):
     """Check that we can compute weight for sparse `y`."""
-    y = sparse.csc_matrix(np.asarray([0, 1, 1])).T
+    y = csc_container(np.asarray([0, 1, 1])).T
     sample_weight = compute_sample_weight("balanced", y)
     assert_allclose(sample_weight, [1.5, 0.75, 0.75])