MLflow Schema enforcement should not cast object to pandas String (#5134

)

* remove cast

Signed-off-by: Steven Chen <s.chen@databricks.com>

* tests

Signed-off-by: Steven Chen <s.chen@databricks.com>

mlflow/pyfunc/__init__.py

@@ -326,18 +326,9 @@ def _enforce_mlflow_datatype(name, values: pandas.Series, t: DataType):
         values = values.infer_objects()
 
     if t == DataType.string and values.dtype == np.object:
-        #  NB: strings are by default parsed and inferred as objects, but it is
-        # recommended to use StringDtype extension type if available. See
-        #
-        # `https://pandas.pydata.org/pandas-docs/stable/user_guide/text.html`
-        #
-        # for more detail.
-        try:
-            return values.astype(t.to_pandas(), errors="raise")
-        except ValueError:
-            raise MlflowException(
-                "Failed to convert column {0} from type {1} to {2}.".format(name, values.dtype, t)
-            )
+        # NB: the object can contain any type and we currently cannot cast to pandas Strings
+        # due to how None is cast
+        return values
 
     # NB: Comparison of pandas and numpy data type fails when numpy data type is on the left hand
     # side of the comparison operator. It works, however, if pandas type is on the left hand side.

tests/pyfunc/test_model_export_with_loader_module_and_data_path.py

@@ -179,6 +179,9 @@ def test_column_schema_enforcement():
     expected_types = dict(zip(input_schema.input_names(), input_schema.pandas_types()))
     # MLflow datetime type in input_schema does not encode precision, so add it for assertions
     expected_types["h"] = np.dtype("datetime64[ns]")
+    # np.object cannot be converted to pandas Strings at the moment
+    expected_types["f"] = np.object
+    expected_types["g"] = np.object
     actual_types = res.dtypes.to_dict()
     assert expected_types == actual_types
 

tests/pyfunc/test_scoring_server.py

@@ -542,15 +542,18 @@ def predict(self, context, model_input):
             extra_args=["--no-conda"],
         )
         response_json = json.loads(response.content)
-        assert response_json == [[k, str(v)] for k, v in pandas_df_with_all_types.dtypes.items()]
+
+        # np.objects are not converted to pandas Strings at the moment
+        expected_types = {**pandas_df_with_all_types.dtypes, "string": np.dtype(object)}
+        assert response_json == [[k, str(v)] for k, v in expected_types.items()]
         response = pyfunc_serve_and_score_model(
             model_uri="runs:/{}/model".format(run.info.run_id),
             data=json.dumps(pandas_df_with_all_types.to_dict(orient="records"), cls=NumpyEncoder),
             content_type=pyfunc_scoring_server.CONTENT_TYPE_JSON_RECORDS_ORIENTED,
             extra_args=["--no-conda"],
         )
         response_json = json.loads(response.content)
-        assert response_json == [[k, str(v)] for k, v in pandas_df_with_all_types.dtypes.items()]
+        assert response_json == [[k, str(v)] for k, v in expected_types.items()]
 
 
 @pytest.mark.large