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uxlfoundation · Sep 2, 2024 · f1d5691 · f1d5691
1 parent da6a3e7
commit f1d5691
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Showing 2 changed files with 15 additions and 15 deletions.
diff --git a/sklearnex/linear_model/tests/test_incremental_linear.py b/sklearnex/linear_model/tests/test_incremental_linear.py
@@ -46,13 +46,13 @@ def test_sklearnex_fit_on_gold_data(dataframe, queue, fit_intercept, macro_block
     inclin.fit(X_df, y_df)
 
     y_pred = inclin.predict(X_df)
-    y_pred_as_numpy = _as_numpy(y_pred)
+    np_y_pred = _as_numpy(y_pred)
 
-    tol = 2e-6 if y_pred_as_numpy.dtype == np.float32 else 1e-7
+    tol = 2e-6 if dtype == np.float32 else 1e-7
     assert_allclose(inclin.coef_, [1], atol=tol)
     if fit_intercept:
         assert_allclose(inclin.intercept_, [0], atol=tol)
-    assert_allclose(y_pred_as_numpy, y, atol=tol)
+    assert_allclose(np_y_pred, y, atol=tol)
 
 
 @pytest.mark.parametrize("dataframe,queue", get_dataframes_and_queues())
@@ -85,15 +85,15 @@ def test_sklearnex_partial_fit_on_gold_data(
 
     X_df = _convert_to_dataframe(X, sycl_queue=queue, target_df=dataframe)
     y_pred = inclin.predict(X_df)
-    y_pred_as_numpy = _as_numpy(y_pred)
+    np_y_pred = _as_numpy(y_pred)
 
     assert inclin.n_features_in_ == 1
-    tol = 2e-6 if y_pred_as_numpy.dtype == np.float32 else 1e-7
+    tol = 2e-6 if dtype == np.float32 else 1e-7
     assert_allclose(inclin.coef_, [[1]], atol=tol)
     if fit_intercept:
         assert_allclose(inclin.intercept_, 3, atol=tol)
 
-    assert_allclose(y_pred_as_numpy, y, atol=tol)
+    assert_allclose(np_y_pred, y, atol=tol)
 
 
 @pytest.mark.parametrize("dataframe,queue", get_dataframes_and_queues())
@@ -126,15 +126,15 @@ def test_sklearnex_partial_fit_multitarget_on_gold_data(
 
     X_df = _convert_to_dataframe(X, sycl_queue=queue, target_df=dataframe)
     y_pred = inclin.predict(X_df)
-    y_pred_as_numpy = _as_numpy(y_pred)
+    np_y_pred = _as_numpy(y_pred)
 
     assert inclin.n_features_in_ == 2
-    tol = 7e-6 if y_pred_as_numpy.dtype == np.float32 else 1e-7
+    tol = 7e-6 if dtype == np.float32 else 1e-7
     assert_allclose(inclin.coef_, [1.0, 2.0], atol=tol)
     if fit_intercept:
         assert_allclose(inclin.intercept_, 3.0, atol=tol)
 
-    assert_allclose(y_pred_as_numpy, y, atol=tol)
+    assert_allclose(np_y_pred, y, atol=tol)
 
 
 @pytest.mark.parametrize("dataframe,queue", get_dataframes_and_queues())

diff --git a/sklearnex/preview/decomposition/tests/test_incremental_pca.py b/sklearnex/preview/decomposition/tests/test_incremental_pca.py
@@ -74,7 +74,7 @@ def check_pca_on_gold_data(incpca, dtype, whiten, transformed_data):
     )
 
     tol = 1e-7
-    if transformed_data.dtype == np.float32:
+    if dtype == np.float32:
         tol = 7e-6 if whiten else 1e-6
 
     assert incpca.n_samples_seen_ == expected_n_samples_seen_
@@ -112,7 +112,7 @@ def check_pca_on_gold_data(incpca, dtype, whiten, transformed_data):
 
 
 def check_pca(incpca, dtype, whiten, data, transformed_data):
-    tol = 3e-3 if transformed_data.dtype == np.float32 else 2e-6
+    tol = 3e-3 if dtype == np.float32 else 2e-6
 
     n_components = incpca.n_components_
 
@@ -200,7 +200,7 @@ def test_sklearnex_partial_fit_on_gold_data(dataframe, queue, whiten, num_blocks
 
     X_df = _convert_to_dataframe(X, sycl_queue=queue, target_df=dataframe)
     transformed_data = incpca.transform(X_df)
-    check_pca_on_gold_data(incpca, dtype, whiten, _as_numpy(transformed_data))
+    check_pca_on_gold_data(incpca, dtype, whiten, transformed_data)
 
 
 @pytest.mark.parametrize("dataframe,queue", get_dataframes_and_queues())
@@ -217,7 +217,7 @@ def test_sklearnex_fit_on_gold_data(dataframe, queue, whiten, num_blocks, dtype)
     incpca.fit(X_df)
     transformed_data = incpca.transform(X_df)
 
-    check_pca_on_gold_data(incpca, dtype, whiten, _as_numpy(transformed_data))
+    check_pca_on_gold_data(incpca, dtype, whiten, transformed_data)
 
 
 @pytest.mark.parametrize("dataframe,queue", get_dataframes_and_queues())
@@ -235,7 +235,7 @@ def test_sklearnex_fit_transform_on_gold_data(
     X_df = _convert_to_dataframe(X, sycl_queue=queue, target_df=dataframe)
     transformed_data = incpca.fit_transform(X_df)
 
-    check_pca_on_gold_data(incpca, dtype, whiten, _as_numpy(transformed_data))
+    check_pca_on_gold_data(incpca, dtype, whiten, transformed_data)
 
 
 @pytest.mark.parametrize("dataframe,queue", get_dataframes_and_queues())
@@ -263,4 +263,4 @@ def test_sklearnex_partial_fit_on_random_data(
 
     X_df = _convert_to_dataframe(X, sycl_queue=queue, target_df=dataframe)
     transformed_data = incpca.transform(X_df)
-    check_pca(incpca, dtype, whiten, X, _as_numpy(transformed_data))
+    check_pca(incpca, dtype, whiten, X, transformed_data)