Avoid implicit type downgrading for pandas Series or DataFrame when using replace()

- An explicit call to `infer_objects()` forces the inference to the actual dtype

Author: Thierry RAMORASOAVINA

tests/test_sklearn.py

@@ -1135,7 +1135,9 @@ def _create_train_test_multitable_dataframe(cls, transform_for_regression=False)
             dataset_name, "SpliceJunction", "SpliceJunctionDNA"
         )
         if transform_for_regression:
-            root_table_data.replace({"Class": {"EI": 1, "IE": 2, "N": 3}}, inplace=True)
+            root_table_data = root_table_data.replace(
+                {"Class": {"EI": 1, "IE": 2, "N": 3}}
+            ).infer_objects()  # this call forces the inference to the actual dtype
         root_train_data, root_test_data = KhiopsTestHelper.prepare_data(
             root_table_data, "Class"
         )

tests/test_sklearn_output_types.py

@@ -84,7 +84,8 @@ def test_classifier_output_types(self):
                 "int": y,
                 "int binary": y_bin,
                 "float": y.astype(float),
-                "bool": y.replace({0: True, 1: True, 2: False}),
+                # the call to `infer_objects()` forces the inference to the actual dtype
+                "bool": y.replace({0: True, 1: True, 2: False}).infer_objects(),
                 "string": self._replace(y, {0: "se", 1: "vi", 2: "ve"}),
                 "string binary": self._replace(y_bin, {0: "vi_or_se", 1: "ve"}),
                 "int as string": self._replace(y, {0: "8", 1: "9", 2: "10"}),