Address comments

sklearnex/spmd/basic_statistics/tests/test_incremental_basic_statistics_spmd.py

@@ -70,6 +70,7 @@ def test_incremental_basic_statistics_fit_spmd_gold(dataframe, queue, weighted,
     )
 
     if weighted:
+        # Create weights array containing the weight for each sample in the data
         weights = np.array([0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0], dtype=dtype)
         dpt_weights = _convert_to_dataframe(
             weights, sycl_queue=queue, target_df=dataframe
@@ -88,7 +89,11 @@ def test_incremental_basic_statistics_fit_spmd_gold(dataframe, queue, weighted,
     )
 
     for option, _, _ in options_and_tests:
-        assert_allclose(getattr(incbs_spmd, option), getattr(incbs, option))
+        assert_allclose(
+            getattr(incbs_spmd, option),
+            getattr(incbs, option),
+            err_msg=f"Result for {option} is incorrect",
+        )
 
 
 @pytest.mark.skipif(
@@ -131,6 +136,7 @@ def test_incremental_basic_statistics_partial_fit_spmd_gold(
     split_local_data = np.array_split(local_data, num_blocks)
 
     if weighted:
+        # Create weights array containing the weight for each sample in the data
         weights = np.array([0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0], dtype=dtype)
         dpt_weights = _convert_to_dataframe(
             weights, sycl_queue=queue, target_df=dataframe
@@ -156,7 +162,11 @@ def test_incremental_basic_statistics_partial_fit_spmd_gold(
     incbs.fit(dpt_data, sample_weight=dpt_weights if weighted else None)
 
     for option, _, _ in options_and_tests:
-        assert_allclose(getattr(incbs_spmd, option), getattr(incbs, option))
+        assert_allclose(
+            getattr(incbs_spmd, option),
+            getattr(incbs, option),
+            err_msg=f"Result for {option} is incorrect",
+        )
 
 
 @pytest.mark.skipif(
@@ -200,6 +210,7 @@ def test_incremental_basic_statistics_single_option_partial_fit_spmd_gold(
     split_local_data = np.array_split(local_data, num_blocks)
 
     if weighted:
+        # Create weights array containing the weight for each sample in the data
         weights = np.array([0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0], dtype=dtype)
         dpt_weights = _convert_to_dataframe(
             weights, sycl_queue=queue, target_df=dataframe
@@ -260,6 +271,7 @@ def test_incremental_basic_statistics_partial_fit_spmd_synthetic(
     split_data = np.array_split(data, num_blocks)
 
     if weighted:
+        # Create weights array containing the weight for each sample in the data
         weights = _generate_weights(n_samples, dtype=dtype)
         local_weights = _get_local_tensor(weights)
         split_local_weights = np.array_split(local_weights, num_blocks)
@@ -288,4 +300,9 @@ def test_incremental_basic_statistics_partial_fit_spmd_synthetic(
         incbs.partial_fit(dpt_data, sample_weight=dpt_weights if weighted else None)
 
     for option, _, _ in options_and_tests:
-        assert_allclose(getattr(incbs_spmd, option), getattr(incbs, option), atol=tol)
+        assert_allclose(
+            getattr(incbs_spmd, option),
+            getattr(incbs, option),
+            atol=tol,
+            err_msg=f"Result for {option} is incorrect",
+        )