fix many tiny bugs

Signed-off-by: Xavier Dupre <[email protected]>

docs/tests/test_documentation_examples.py

@@ -64,8 +64,8 @@ def run_test(self, fold: str, name: str, verbose=0) -> int:
     def add_test_methods(cls):
         this = os.path.abspath(os.path.dirname(__file__))
         folds = [
-            os.path.normpath(os.path.join(this, "..", "docs", "examples")),
-            os.path.normpath(os.path.join(this, "..", "docs", "tutorial")),
+            os.path.normpath(os.path.join(this, "..", "examples")),
+            os.path.normpath(os.path.join(this, "..", "tutorial")),
         ]
         for fold in folds:
             found = os.listdir(fold)

skl2onnx/_parse.py

@@ -436,14 +436,18 @@ def _parse_sklearn_grid_search_cv(scope, model, inputs, custom_parsers=None):
 
 
 def _parse_sklearn_random_trees_embedding(scope, model, inputs, custom_parsers=None):
-    res = parse_sklearn(
-        scope, model.base_estimator_, inputs, custom_parsers=custom_parsers
-    )
+    if hasattr(model, "estimator_"):
+        est = model.estimator_
+    elif hasattr(model, "base_estimator_"):
+        est = model.base_estimator_
+    else:
+        raise RuntimeError(
+            f"Model {model} was not trained (unable to find the estimator {dir(model)})."
+        )
+    res = parse_sklearn(scope, est, inputs, custom_parsers=custom_parsers)
     if len(res) != 1:
         raise RuntimeError("A regressor only produces one output not %r." % res)
-    scope.replace_raw_operator(
-        model.base_estimator_, model, "SklearnRandomTreesEmbedding"
-    )
+    scope.replace_raw_operator(est, model, "SklearnRandomTreesEmbedding")
     return res
 
 

tests/test_issues_2024.py

@@ -1,5 +1,7 @@
 # SPDX-License-Identifier: Apache-2.0
 import unittest
+import packaging.version as pv
+from onnxruntime import __version__ as ort_version
 
 
 class TestInvestigate(unittest.TestCase):
@@ -39,6 +41,10 @@ def test_issue_1053(self):
                 ]  # Select a single sample.
                 self.assertEqual(len(pred_onx.tolist()), 1)
 
+    @unittest.skipIf(
+        pv.Version(ort_version) < pv.Version("1.16.0"),
+        reason="opset 19 not implemented",
+    )
     def test_issue_1055(self):
         import numpy as np
         from numpy.testing import assert_almost_equal

tests/test_sklearn_double_tensor_type_cls.py

@@ -409,7 +409,7 @@ def test_calibration_sigmoid_64(self):
         self._common_classifier(
             [
                 lambda: CalibratedClassifierCV(
-                    base_estimator=LogisticRegression(), method="sigmoid"
+                    estimator=LogisticRegression(), method="sigmoid"
                 )
             ],
             "CalibratedClassifierCV",

tests/test_sklearn_glm_classifier_converter.py

@@ -396,7 +396,7 @@ def test_model_logistic_regression_multi_class_no_intercept(self):
 
     @ignore_warnings(category=(DeprecationWarning, ConvergenceWarning))
     def test_model_logistic_regression_multi_class_lbfgs(self):
-        penalty = "l2" if _sklearn_version() < pv.Version("0.21.0") else "none"
+        penalty = "l2"
         model, X = fit_classification_model(
             linear_model.LogisticRegression(
                 solver="lbfgs", penalty=penalty, max_iter=10000

tests_onnxmltools/test_xgboost_converters.py

@@ -178,7 +178,7 @@ def test_xgb_classifier_reglog(self):
             conv_model.SerializeToString(), providers=["CPUExecutionProvider"]
         )
         res = sess.run(None, {"input": X.astype(np.float32)})
-        assert_almost_equal(xgb.predict_proba(X), res[1])
+        assert_almost_equal(xgb.predict_proba(X), res[1], decimal=4)
         assert_almost_equal(xgb.predict(X), res[0])
 
     @unittest.skipIf(StackingClassifier is None, reason="new in 0.22")