TST Extend tests for scipy.sparse.*array in sklearn/preprocessing/tests/test_common.py (scikit-learn#27164)

Author: work-mohit

sklearn/preprocessing/tests/test_common.py

@@ -2,7 +2,6 @@
 
 import numpy as np
 import pytest
-from scipy import sparse
 
 from sklearn.base import clone
 from sklearn.datasets import load_iris
@@ -22,6 +21,15 @@
     scale,
 )
 from sklearn.utils._testing import assert_allclose, assert_array_equal
+from sklearn.utils.fixes import (
+    BSR_CONTAINERS,
+    COO_CONTAINERS,
+    CSC_CONTAINERS,
+    CSR_CONTAINERS,
+    DIA_CONTAINERS,
+    DOK_CONTAINERS,
+    LIL_CONTAINERS,
+)
 
 iris = load_iris()
 
@@ -113,19 +121,19 @@ def test_missing_value_handling(
             Xt_dense = est_dense.fit(X_train).transform(X_test)
             Xt_inv_dense = est_dense.inverse_transform(Xt_dense)
 
-        for sparse_constructor in (
-            sparse.csr_matrix,
-            sparse.csc_matrix,
-            sparse.bsr_matrix,
-            sparse.coo_matrix,
-            sparse.dia_matrix,
-            sparse.dok_matrix,
-            sparse.lil_matrix,
+        for sparse_container in (
+            BSR_CONTAINERS
+            + COO_CONTAINERS
+            + CSC_CONTAINERS
+            + CSR_CONTAINERS
+            + DIA_CONTAINERS
+            + DOK_CONTAINERS
+            + LIL_CONTAINERS
         ):
             # check that the dense and sparse inputs lead to the same results
             # precompute the matrix to avoid catching side warnings
-            X_train_sp = sparse_constructor(X_train)
-            X_test_sp = sparse_constructor(X_test)
+            X_train_sp = sparse_container(X_train)
+            X_test_sp = sparse_container(X_test)
             with warnings.catch_warnings():
                 warnings.simplefilter("ignore", PendingDeprecationWarning)
                 warnings.simplefilter("error", RuntimeWarning)