isolate ODFV list type feature test to smaller code changes

Signed-off-by: Jeff <jeffxl@apple.com>

sdk/python/feast/driver_test_data.py

@@ -124,9 +124,6 @@ def create_driver_hourly_stats_df(drivers, start_date, end_date) -> pd.DataFrame
     df_all_drivers["avg_daily_trips"] = np.random.randint(0, 1000, size=rows).astype(
         np.int32
     )
-    dummy_vector = [1.0, 0.0]
-    df_all_drivers["embedding_double"] = [dummy_vector] * rows
-    df_all_drivers["embedding_float"] = df_all_drivers["embedding_double"]
     df_all_drivers["created"] = pd.to_datetime(pd.Timestamp.now(tz=None).round("ms"))
 
     # Create duplicate rows that should be filtered by created timestamp

sdk/python/tests/integration/feature_repos/universal/entities.py

@@ -16,3 +16,7 @@ def customer():
 
 def location():
     return Entity(name="location_id", value_type=ValueType.INT64)
+
+
+def item():
+    return Entity(name="item_id", value_type=ValueType.INT64)

sdk/python/tests/integration/feature_repos/universal/feature_views.py

@@ -128,6 +128,22 @@ def create_similarity_request_data_source():
     )
 
 
+def create_item_embeddings_feature_view(source, infer_features: bool = False):
+    item_embeddings_feature_view = FeatureView(
+        name="item_embeddings",
+        entities=["item"],
+        features=None
+        if infer_features
+        else [
+            Feature(name="embedding_double", dtype=ValueType.DOUBLE_LIST),
+            Feature(name="embedding_float", dtype=ValueType.FLOAT_LIST),
+        ],
+        batch_source=source,
+        ttl=timedelta(hours=2),
+    )
+    return item_embeddings_feature_view
+
+
 def create_driver_hourly_stats_feature_view(source, infer_features: bool = False):
     driver_stats_feature_view = FeatureView(
         name="driver_stats",
@@ -138,8 +154,6 @@ def create_driver_hourly_stats_feature_view(source, infer_features: bool = False
             Feature(name="conv_rate", dtype=ValueType.FLOAT),
             Feature(name="acc_rate", dtype=ValueType.FLOAT),
             Feature(name="avg_daily_trips", dtype=ValueType.INT32),
-            Feature(name="embedding_double", dtype=ValueType.DOUBLE_LIST),
-            Feature(name="embedding_float", dtype=ValueType.FLOAT_LIST),
         ],
         batch_source=source,
         ttl=timedelta(hours=2),

sdk/python/tests/integration/online_store/test_universal_online.py

@@ -143,14 +143,11 @@ def test_write_to_online_store(environment, universal_data_sources):
     fs.apply([driver_hourly_stats, driver_entity])
 
     # fake data to ingest into Online Store
-    dummy_vector = [1.0, 0.0]
     data = {
         "driver_id": [123],
         "conv_rate": [0.85],
         "acc_rate": [0.91],
         "avg_daily_trips": [14],
-        "embedding_double": [dummy_vector],
-        "embedding_float": [dummy_vector],
         "event_timestamp": [pd.Timestamp(datetime.datetime.utcnow()).round("ms")],
         "created": [pd.Timestamp(datetime.datetime.utcnow()).round("ms")],
     }

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

@@ -1,12 +1,17 @@
+from datetime import datetime
+
+import pandas as pd
 import pytest
 
 from feast import Feature, ValueType
 from feast.errors import SpecifiedFeaturesNotPresentError
-from tests.integration.feature_repos.universal.entities import customer, driver
+from feast.infra.offline_stores.file_source import FileSource
+from tests.integration.feature_repos.universal.entities import customer, driver, item
 from tests.integration.feature_repos.universal.feature_views import (
     conv_rate_plus_100_feature_view,
     create_conv_rate_request_data_source,
     create_driver_hourly_stats_feature_view,
+    create_item_embeddings_feature_view,
     create_similarity_request_data_source,
     similarity_feature_view,
 )
@@ -29,19 +34,39 @@ def test_infer_odfv_features(environment, universal_data_sources, infer_features
         infer_features=infer_features,
     )
 
-    sim_odfv = similarity_feature_view(
-        {
-            "driver": driver_hourly_stats,
-            "input_request": create_similarity_request_data_source(),
-        },
-        infer_features=infer_features,
-    )
-
-    feast_objects = [driver_hourly_stats, driver_odfv, sim_odfv, driver(), customer()]
+    feast_objects = [driver_hourly_stats, driver_odfv, driver(), customer()]
     store.apply(feast_objects)
     odfv = store.get_on_demand_feature_view("conv_rate_plus_100")
     assert len(odfv.features) == 3
 
+
+@pytest.mark.integration
+@pytest.mark.parametrize("infer_features", [True, False], ids=lambda v: str(v))
+def test_infer_odfv_list_features(environment, infer_features, tmp_path):
+    fake_embedding = [1.0, 1.0]
+    items_df = pd.DataFrame(
+        data={
+            "item_id": [0],
+            "embedding_float": [fake_embedding],
+            "embedding_double": [fake_embedding],
+            "event_timestamp": [pd.Timestamp(datetime.utcnow())],
+            "created": [pd.Timestamp(datetime.utcnow())],
+        }
+    )
+    output_path = f"{tmp_path}/items.parquet"
+    items_df.to_parquet(output_path)
+    fake_items_src = FileSource(
+        path=output_path,
+        event_timestamp_column="event_timestamp",
+        created_timestamp_column="created",
+    )
+    items = create_item_embeddings_feature_view(fake_items_src)
+    sim_odfv = similarity_feature_view(
+        {"items": items, "input_request": create_similarity_request_data_source()},
+        infer_features=infer_features,
+    )
+    store = environment.feature_store
+    store.apply([item(), items, sim_odfv])
     odfv = store.get_on_demand_feature_view("similarity")
     assert len(odfv.features) == 2