fix testing issues

uxlfoundation · Nov 21, 2024 · b5b8442 · b5b8442
1 parent 5355039
commit b5b8442
Showing 1 changed file with 8 additions and 9 deletions.
diff --git a/sklearnex/utils/tests/test_finite.py b/sklearnex/utils/tests/test_finite.py
@@ -44,7 +44,7 @@
 @pytest.mark.parametrize("ensure_all_finite", ["allow-nan", True])
 def test_sum_infinite_actually_finite(dtype, shape, ensure_all_finite):
     est = DummyEstimator()
-    X = np.array(shape, dtype=dtype)
+    X = np.empty(shape, dtype=dtype)
     X.fill(np.finfo(dtype).max)
     X = np.atleast_2d(X)
     X_array = validate_data(est, X, ensure_all_finite=ensure_all_finite)
@@ -120,7 +120,7 @@ def test_validate_data_random_shape_and_location(
 
     allow_nan = ensure_all_finite == "allow-nan"
     if check is None or (allow_nan and check == "NaN"):
-        validate_data(est, X)
+        validate_data(est, X, ensure_all_finite=ensure_all_finite)
     else:
         type_err = "infinity" if allow_nan else "NaN, infinity"
         msg_err = f"Input X contains {type_err}."
@@ -129,26 +129,25 @@ def test_validate_data_random_shape_and_location(
 
 
 @pytest.mark.parametrize("dtype", [np.float32, np.float64])
-@pytest.mark.parametrize("array_api_dispatch", [True, False])
+@pytest.mark.parametrize("array_api_dispatch", [True, False] if sklearn_check_version("1.2") else [False])
 @pytest.mark.parametrize("dataframe, queue", get_dataframes_and_queues())
 def test_validate_data_output(array_api_dispatch, dtype, dataframe, queue):
     est = DummyEstimator()
     X, y = gen_dataset(est, queue=queue, target_df=dataframe, dtype=dtype)[0]
 
     dispatch = {}
-    if sklearn_check_version("1.2"):
+    if array_api_dispatch:
+        pytest.skip(dataframe == "pandas", "pandas inputs do not work with sklearn's array_api_dispatch")
         dispatch["array_api_dispatch"] = array_api_dispatch
 
     with config_context(**dispatch):
-        validate_data(est, X, y)
-        est.fit(X, y)
+        X_out, y_out = validate_data(est, X, y)
+        # check sklearn validate_data operations work underneath
         X_array = validate_data(est, X, reset=False)
-        X_out = est.predict(X)
 
     if dataframe == "pandas" or (
         dataframe == "array_api"
-        and not (sklearn_check_version("1.2") and array_api_dispatch)
-    ):
+        and not array_api_dispatch):
         # array_api_strict from sklearn < 1.2 and pandas will convert to numpy arrays
         assert isinstance(X_array, np.ndarray)
         assert isinstance(X_out, np.ndarray)