replace warnings.warning

Signed-off-by: xadupre <[email protected]>

benchmarks/bench_plot_onnxruntime_hgb.py

@@ -141,7 +141,7 @@ def bench(
                             try:
                                 assert_almost_equal(p1.ravel(), p2.ravel(), decimal=5)
                             except AssertionError as e:
-                                warnings.warning(str(e))
+                                warning.warn(str(e))
     return res
 
 

benchmarks/bench_plot_onnxruntime_linreg.py

@@ -134,7 +134,7 @@ def bench(n_obs, n_features, fit_intercepts, methods, repeat=10, verbose=False):
                         try:
                             assert_almost_equal(p1.ravel(), p2.ravel(), decimal=5)
                         except AssertionError as e:
-                            warnings.warning(str(e))
+                            warning.warn(str(e))
     return res
 
 

benchmarks/bench_plot_onnxruntime_random_forest_reg.py

@@ -192,7 +192,7 @@ def bench(
                                         p1.ravel(), p2.ravel(), decimal=5
                                     )
                                 except AssertionError as e:
-                                    warnings.warning(str(e))
+                                    warning.warn(str(e))
     return res
 
 

benchmarks/bench_plot_onnxruntime_svm_reg.py

@@ -127,7 +127,7 @@ def bench(n_obs, n_features, kernels, methods, repeat=10, verbose=False):
                         try:
                             assert_almost_equal(p1.ravel(), p2.ravel(), decimal=3)
                         except AssertionError as e:
-                            warnings.warning(str(e))
+                            warning.warn(str(e))
     return res
 
 

docs/tutorial/plot_gexternal_lightgbm_reg.py

@@ -82,7 +82,7 @@ def skl2onnx_convert_lightgbm(scope, operator, container):
     options = scope.get_options(operator.raw_operator)
     if "split" in options:
         if pv.Version(oml_version) < pv.Version("1.9.2"):
-            warnings.warning(
+            warning.warn(
                 "Option split was released in version 1.9.2 but %s is "
                 "installed. It will be ignored." % oml_version
             )

skl2onnx/_parse.py

@@ -134,7 +134,7 @@ def _parse_sklearn_simple_model(scope, model, inputs, custom_parsers=None, alias
             try:
                 names = parser_names(scope=scope, inputs=inputs)
             except TypeError as e:
-                warnings.warning(
+                warning.warn(
                     "Calling parser %r for model type %r failed due to %r. "
                     "This warnings will become an exception in version 1.11. "
                     "The parser signature should parser(scope=None, "
@@ -560,7 +560,7 @@ def _parse_sklearn_classifier(scope, model, inputs, custom_parsers=None):
 def _parse_sklearn_multi_output_classifier(scope, model, inputs, custom_parsers=None):
     options = scope.get_options(model, dict(zipmap=True))
     if options["zipmap"]:
-        warnings.warning(
+        warning.warn(
             "Option zipmap is ignored for model %r. "
             "Set option zipmap to False to "
             "remove this message." % type(model),

skl2onnx/_supported_operators.py

@@ -568,7 +568,7 @@ def update_registered_converter(
         and model in sklearn_operator_name_map
         and alias != sklearn_operator_name_map[model]
     ):
-        warnings.warning(
+        warning.warn(
             "Model '{0}' was already registered under alias "
             "'{1}'.".format(model, sklearn_operator_name_map[model])
         )

skl2onnx/algebra/onnx_operator.py

@@ -1332,7 +1332,7 @@ class OnnxSubOperator(OnnxSubEstimator):
 
     def __init__(self, *args, **kwargs):
         OnnxSubEstimator.__init__(self, *args, **kwargs)
-        warnings.warning(
+        warning.warn(
             (
                 "Class OnnxSubOperator will be removed in 1.10. "
                 "It should be replaced by OnnxSubEstimator."

skl2onnx/algebra/onnx_operator_mixin.py

@@ -212,7 +212,7 @@ def converter(
                         target_opset=op_version, outputs=outputs, options=options
                     )
             except TypeError:
-                warnings.warning(
+                warning.warn(
                     "Signature should be to_onnx_operator(self, inputs=None, "
                     "outputs=None, target_opset=None, **kwargs). "
                     "This will be the case in version 1.11, class=%r."

skl2onnx/common/_container.py

@@ -767,7 +767,7 @@ def _get_op_version(self, domain, op_type):
                 # avoid a not necessarily necessary warning
                 vers = 1
             else:
-                warnings.warning(
+                warning.warn(
                     "Unable to find operator '{}' in domain '{}' in ONNX, "
                     "op_version is forced to 1.".format(op_type, domain)
                 )

skl2onnx/common/_topology.py

@@ -1507,7 +1507,7 @@ def convert_topology(
             ".".format(onnx_target_opset, found)
         )
     if onnx_target_opset > get_latest_tested_opset_version():
-        warnings.warning(
+        warning.warn(
             "Parameter target_opset {} > {} is higher than the "
             "the latest tested version"
             ".".format(onnx_target_opset, get_latest_tested_opset_version())

skl2onnx/convert.py

@@ -178,7 +178,7 @@ def convert_sklearn(
     if name is None:
         name = str(uuid4().hex)
     if dtype is not None:
-        warnings.warning(
+        warning.warn(
             "Parameter dtype is no longer supported. It will be removed in 1.9.0.",
             DeprecationWarning,
         )

skl2onnx/helpers/investigate.py

@@ -207,7 +207,7 @@ def decision_function(self, X, *args, **kwargs):
                 try:
                     setattr(model, k, MethodType(new_methods[k], model))
                 except AttributeError:
-                    warnings.warning(
+                    warning.warn(
                         "Unable to overwrite method '{}' for class "
                         "{}.".format(k, type(model))
                     )
@@ -217,7 +217,7 @@ def decision_function(self, X, *args, **kwargs):
             try:
                 setattr(skl_model, k, MethodType(new_methods[k], skl_model))
             except AttributeError:
-                warnings.warning(
+                warning.warn(
                     "Unable to overwrite method '{}' for class "
                     "{}.".format(k, type(skl_model))
                 )

skl2onnx/operator_converters/local_outlier_factor.py

@@ -53,7 +53,7 @@ def convert_sklearn_local_outlier_factor(
     kwargs = {}
     if op.p != 2:
         if options["optim"] == "cdist":
-            warnings.warning(
+            warning.warn(
                 "Option p=%r may not be compatible with the runtime. "
                 "See https://github.com/microsoft/onnxruntime/blob/main/"
                 "docs/ContribOperators.md#com.microsoft.CDist."

skl2onnx/operator_converters/text_vectoriser.py

@@ -200,7 +200,7 @@ def convert_sklearn_text_vectorizer(
             "https://github.com/onnx/sklearn-onnx/issues."
         )
     if op.analyzer == "char":
-        warnings.warning(
+        warning.warn(
             "The conversion of CountVectorizer may not work. "
             "only tokenizer='word' is fully supported. "
             "You may raise an issue at "

skl2onnx/sklapi/woe_transformer_onnx.py

@@ -267,7 +267,7 @@ def onnx_attributes(self):
             )
         )
         if len(atts["target_weights"]) != len(set(atts["target_weights"])):
-            warnings.warning(
+            warning.warn(
                 "All targets should be unique %r." % atts["target_weights"],
                 stacklevel=0,
             )

tests/benchmark.py

@@ -37,7 +37,7 @@ def run_all_tests(folder=None, verbose=True):
     try:
         import onnxmltools  # noqa: F401
     except ImportError:
-        warnings.warning("Cannot import onnxmltools. Some tests won't work.")
+        warning.warn("Cannot import onnxmltools. Some tests won't work.")
 
     this = os.path.abspath(os.path.dirname(__file__))
     subs = [this]

tests/test_algebra_onnx_operators.py

@@ -488,7 +488,7 @@ def test_onnxt_runtime_pad(self):
         try:
             assert_almost_equal(exp, got[0])
         except AssertionError as e:
-            warnings.warning(e)
+            warning.warn(e)
 
         data = np.array([[1.0, 1.2], [2.3, 3.4], [4.5, 5.7]], dtype=np.float32)
         pads = np.array([0, 2, 0, 0], dtype=np.int64)

tests/test_algebra_onnx_operators_scan.py

@@ -228,7 +228,7 @@ def test_onnx_example_pdist(self):
             onnx.checker.check_model(model_def)
         except ValidationError as e:
             if pv.Version(onnx__version__) <= pv.Version("1.5.0"):
-                warnings.warning(e)
+                warning.warn(e)
             else:
                 raise e
 

tests/test_algebra_onnx_operators_sparse.py

@@ -74,7 +74,7 @@ def test_onnx_init_sparse_coo(self):
             res = sess.run(None, {"X": X.todense()})[0]
         except RuntimeError as e:
             # Sparse tensor is not supported for constant.
-            warnings.warning(
+            warning.warn(
                 "Unable to run with %r\n---\n%s\n%s" % ({"X": X}, model_def, e)
             )
             return

tests/test_algebra_symbolic.py

@@ -17,7 +17,7 @@
     from skl2onnx.algebra.onnx_ops import OnnxAbs, OnnxNormalizer, OnnxArgMin
     from skl2onnx.algebra.onnx_ops import OnnxSplitApi18, OnnxScaler
 except ImportError:
-    warnings.warning("Unable to test OnnxAbs, OnnxNormalizer, OnnxArgMin, OnnxSplit.")
+    warning.warn("Unable to test OnnxAbs, OnnxNormalizer, OnnxArgMin, OnnxSplit.")
     OnnxAbs = None
 from test_utils import TARGET_OPSET, InferenceSessionEx as InferenceSession
 

tests/test_sklearn_documentation.py

@@ -87,7 +87,7 @@ def test_pipeline_tfidf(self):
                 random_state=1, subset="test", categories=categories
             )
         except urllib.error.URLError:
-            warnings.warning("Unit test may fail due to connectivity issue.")
+            warning.warn("Unit test may fail due to connectivity issue.")
             return
         train_data = SubjectBodyExtractor().fit_transform(train.data)
         tfi = TfidfVectorizer(min_df=30)
@@ -130,7 +130,7 @@ def test_pipeline_tfidf_pipeline_minmax(self):
                 random_state=1, subset="train", categories=categories
             )
         except urllib.error.URLError:
-            warnings.warning("Unit test may fail due to connectivity issue.")
+            warning.warn("Unit test may fail due to connectivity issue.")
             return
         train_data = SubjectBodyExtractor().fit_transform(train.data)
         pipeline = Pipeline(

tests/test_sklearn_gaussian_process_regressor.py

@@ -1042,7 +1042,7 @@ def test_gpr_rbf_fitted_return_std_exp_sine_squared_double_true(self):
             gp.fit(Xtrain_, Ytrain_)
         except (AttributeError, TypeError) as e:
             # unstable issue fixed with scikit-learn>=0.24
-            warnings.warning(
+            warning.warn(
                 "Training did not converge but fails at raising a warning: %r." % e
             )
             return

tests/test_sklearn_nearest_neighbour_converter.py

@@ -343,7 +343,7 @@ def test_model_knn_regressor2_1_radius(self):
                 # One broadcasted multiplication unexpectedly produces nan.
                 whole = "\n".join(rows)
                 if "[        nan" in whole:
-                    warnings.warning(whole)
+                    warning.warn(whole)
                     return
                 raise AssertionError(whole)
             if ort_version.startswith("1.3.") and sys.platform == "win32":

tests/test_sklearn_pipeline.py

@@ -342,7 +342,7 @@ def test_pipeline_column_transformer_titanic(self):
             data = pandas.read_csv(titanic_url)
         except url_error.URLError:
             # Do not fail the test if the data cannot be fetched.
-            warnings.warning("Unable to fetch titanic data.")
+            warning.warn("Unable to fetch titanic data.")
             return
         X = data.drop("survived", axis=1)
         y = data["survived"]

tests/test_utils/tests_helper.py

@@ -515,15 +515,15 @@ def lambda_original():
                 except OnnxRuntimeMissingNewOnnxOperatorException as e:
                     if fail_evenif_notimplemented:
                         raise e
-                    warnings.warning(str(e))
+                    warning.warn(str(e))
                     continue
                 except AssertionError as e:
                     if dump_error_log:
                         with open(error_dump, "w", encoding="utf-8") as f:
                             f.write(str(e) + "\n--------------\n")
                             traceback.print_exc(file=f)
                     if isinstance(allow, bool) and allow:
-                        warnings.warning(
+                        warning.warn(
                             "Issue with '{0}' due to {1}".format(
                                 basename, str(e).replace("\n", " -- ")
                             )

tests/test_utils/utils_backend_onnxruntime.py

@@ -125,7 +125,7 @@ def compare_runtime(
     try:
         import onnxruntime
     except ImportError:
-        warnings.warning("Unable to import onnxruntime.")
+        warning.warn("Unable to import onnxruntime.")
         return None
 
     if verbose: