Fix issue with numpy datetimes in feast_value_type_to_pandas_type

sdk/python/feast/on_demand_feature_view.py

@@ -259,7 +259,9 @@ def get_requested_odfvs(feature_refs, project, registry):
         return requested_on_demand_feature_views
 
 
-def on_demand_feature_view(features: List[Feature], inputs: Dict[str, FeatureView]):
+def on_demand_feature_view(
+    features: List[Feature], inputs: Dict[str, Union[FeatureView, RequestDataSource]]
+):
     """
     Declare an on-demand feature view
 

sdk/python/feast/type_map.py

@@ -97,7 +97,7 @@ def feast_value_type_to_pandas_type(value_type: ValueType) -> Any:
         ValueType.DOUBLE: "float",
         ValueType.BYTES: "bytes",
         ValueType.BOOL: "bool",
-        ValueType.UNIX_TIMESTAMP: "datetime",
+        ValueType.UNIX_TIMESTAMP: "datetime64[ns]",
     }
     if value_type.name.endswith("_LIST"):
         return "object"

sdk/python/tests/integration/registration/test_inference.py

@@ -1,12 +1,15 @@
+import pandas as pd
 import pytest
 
-from feast import Entity, RepoConfig, ValueType
+from feast import Entity, Feature, RepoConfig, ValueType
+from feast.data_source import RequestDataSource
 from feast.errors import RegistryInferenceFailure
 from feast.feature_view import FeatureView
 from feast.inference import (
     update_data_sources_with_inferred_event_timestamp_col,
     update_entities_with_inferred_types_from_feature_views,
 )
+from feast.on_demand_feature_view import on_demand_feature_view
 from tests.utils.data_source_utils import (
     prep_file_source,
     simple_bq_source_using_query_arg,
@@ -81,3 +84,21 @@ def test_update_data_sources_with_inferred_event_timestamp_col(simple_dataset_1)
             update_data_sources_with_inferred_event_timestamp_col(
                 [file_source], RepoConfig(provider="local", project="test")
             )
+
+
+def test_modify_feature_views_success():
+    # Create Feature Views
+    date_request = RequestDataSource(
+        name="date_request", schema={"some_date": ValueType.UNIX_TIMESTAMP}
+    )
+
+    @on_demand_feature_view(
+        inputs={"date_request": date_request},
+        features=[Feature("output", ValueType.UNIX_TIMESTAMP)],
+    )
+    def test_view(features_df: pd.DataFrame) -> pd.DataFrame:
+        data = pd.DataFrame()
+        data["output"] = features_df["some_date"]
+        return data
+
+    test_view.infer_features()