REDVM
diff --git a/‎sklearn/datasets/tests/test_svmlight_format.py
Lines changed: 19 additions & 13 deletions b/‎sklearn/datasets/tests/test_svmlight_format.py
Lines changed: 19 additions & 13 deletions
@@ -17,7 +17,7 @@
     assert_array_equal,
     fails_if_pypy,
 )
-from sklearn.utils.fixes import _open_binary, _path
+from sklearn.utils.fixes import CSR_CONTAINERS, _open_binary, _path
 
 TEST_DATA_MODULE = "sklearn.datasets.tests.data"
 datafile = "svmlight_classification.txt"
@@ -254,10 +254,11 @@ def test_invalid_filename():
         load_svmlight_file("trou pic nic douille")
 
 
-def test_dump():
+@pytest.mark.parametrize("csr_container", CSR_CONTAINERS)
+def test_dump(csr_container):
     X_sparse, y_dense = _load_svmlight_local_test_file(datafile)
     X_dense = X_sparse.toarray()
-    y_sparse = sp.csr_matrix(y_dense)
+    y_sparse = csr_container(y_dense)
 
     # slicing a csr_matrix can unsort its .indices, so test that we sort
     # those correctly
@@ -323,10 +324,11 @@ def test_dump():
                         )
 
 
-def test_dump_multilabel():
+@pytest.mark.parametrize("csr_container", CSR_CONTAINERS)
+def test_dump_multilabel(csr_container):
     X = [[1, 0, 3, 0, 5], [0, 0, 0, 0, 0], [0, 5, 0, 1, 0]]
     y_dense = [[0, 1, 0], [1, 0, 1], [1, 1, 0]]
-    y_sparse = sp.csr_matrix(y_dense)
+    y_sparse = csr_container(y_dense)
     for y in [y_dense, y_sparse]:
         f = BytesIO()
         dump_svmlight_file(X, y, f, multilabel=True)
@@ -465,9 +467,10 @@ def test_load_with_long_qid():
     assert_array_equal(X.toarray(), true_X)
 
 
-def test_load_zeros():
+@pytest.mark.parametrize("csr_container", CSR_CONTAINERS)
+def test_load_zeros(csr_container):
     f = BytesIO()
-    true_X = sp.csr_matrix(np.zeros(shape=(3, 4)))
+    true_X = csr_container(np.zeros(shape=(3, 4)))
     true_y = np.array([0, 1, 0])
     dump_svmlight_file(true_X, true_y, f)
 
@@ -481,12 +484,13 @@ def test_load_zeros():
 @pytest.mark.parametrize("sparsity", [0, 0.1, 0.5, 0.99, 1])
 @pytest.mark.parametrize("n_samples", [13, 101])
 @pytest.mark.parametrize("n_features", [2, 7, 41])
-def test_load_with_offsets(sparsity, n_samples, n_features):
+@pytest.mark.parametrize("csr_container", CSR_CONTAINERS)
+def test_load_with_offsets(sparsity, n_samples, n_features, csr_container):
     rng = np.random.RandomState(0)
     X = rng.uniform(low=0.0, high=1.0, size=(n_samples, n_features))
     if sparsity:
         X[X < sparsity] = 0.0
-    X = sp.csr_matrix(X)
+    X = csr_container(X)
     y = rng.randint(low=0, high=2, size=n_samples)
 
     f = BytesIO()
@@ -517,7 +521,8 @@ def test_load_with_offsets(sparsity, n_samples, n_features):
     assert_array_almost_equal(X.toarray(), X_concat.toarray())
 
 
-def test_load_offset_exhaustive_splits():
+@pytest.mark.parametrize("csr_container", CSR_CONTAINERS)
+def test_load_offset_exhaustive_splits(csr_container):
     rng = np.random.RandomState(0)
     X = np.array(
         [
@@ -530,7 +535,7 @@ def test_load_offset_exhaustive_splits():
             [1, 0, 0, 0, 0, 0],
         ]
     )
-    X = sp.csr_matrix(X)
+    X = csr_container(X)
     n_samples, n_features = X.shape
     y = rng.randint(low=0, high=2, size=n_samples)
     query_id = np.arange(n_samples) // 2
@@ -564,7 +569,8 @@ def test_load_with_offsets_error():
         _load_svmlight_local_test_file(datafile, offset=3, length=3)
 
 
-def test_multilabel_y_explicit_zeros(tmp_path):
+@pytest.mark.parametrize("csr_container", CSR_CONTAINERS)
+def test_multilabel_y_explicit_zeros(tmp_path, csr_container):
     """
     Ensure that if y contains explicit zeros (i.e. elements of y.data equal to
     0) then those explicit zeros are not encoded.
@@ -576,7 +582,7 @@ def test_multilabel_y_explicit_zeros(tmp_path):
     indices = np.array([0, 2, 2, 0, 1, 2])
     # The first and last element are explicit zeros.
     data = np.array([0, 1, 1, 1, 1, 0])
-    y = sp.csr_matrix((data, indices, indptr), shape=(3, 3))
+    y = csr_container((data, indices, indptr), shape=(3, 3))
     # y as a dense array would look like
     # [[0, 0, 1],
     #  [0, 0, 1],