Add test for list lookup using repeat

fennel/client_tests/test_featureset.py

@@ -20,7 +20,7 @@
     expectations,
     expect_column_values_to_be_between,
 )
-from fennel.expr import col, lit, when
+from fennel.expr import col, lit, when, repeat
 from fennel.testing import mock, log
 
 ################################################################################
@@ -1326,11 +1326,19 @@ class UserInfoListLookup:
         country: List[Optional[str]] = F(UserInfoDataset.country)
         name: List[str] = F(UserInfoDataset.name, default="Unknown")
 
+    @featureset
+    class UserInfoListLookup2:
+        user_id_request: int = F(Request.user_id)
+        user_id: List[int] = F(repeat(col("user_id_request"), 5))
+        country: List[Optional[str]] = F(UserInfoDataset.country)
+        name: List[str] = F(UserInfoDataset.name, default="Unknown")
+
     client.commit(
         datasets=[UserInfoDataset],
-        featuresets=[UserInfoListLookup],
+        featuresets=[UserInfoListLookup, UserInfoListLookup2, Request],
         message="Initial commit",
     )
+    client.sleep()
 
     now = datetime.now(timezone.utc)
     data = [
@@ -1370,6 +1378,20 @@ class UserInfoListLookup:
         ["Monica", "Rahul", "Unknown"],
     ]
 
+    feature_df2 = client.query(
+        outputs=[UserInfoListLookup2],
+        inputs=[Request.user_id],
+        input_dataframe=pd.DataFrame(
+            {"Request.user_id": [18232, 12345]}
+        ),
+    )
+
+    assert feature_df2.shape == (2, 4)
+    assert feature_df2["UserInfoListLookup2.country"].tolist() == [
+        ["USA", "USA", "USA", "USA", "USA"],
+        [pd.NA, pd.NA, pd.NA, pd.NA, pd.NA],
+    ]
+
 
 @pytest.mark.integration
 @mock
@@ -1465,7 +1487,7 @@ class KeyedFeatureset:
     client.commit(
         datasets=[KeyedDataset], featuresets=[KeyedFeatureset], message="msg"
     )
-
+    client.sleep()
     # Log data
     now = datetime.now(timezone.utc)
     data = [

fennel/testing/mock_client.py

@@ -527,6 +527,7 @@ def _transform_input_dataframe_from_inputs(
                     input_dataframe[input_col], col_type
                 )
             except Exception as e:
+                print(input_dataframe)
                 raise Exception(
                     f"Error casting input dataframe column `{input_col}` for feature `{feature.fqn_}`, "
                     f"dtype: `{feature.dtype}`: {e}"