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chore: Updated tests to confirm behavior of inconsistent offline/onli…
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…ne return values (feast-dev#4615)
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@franciscojavierarceo
franciscojavierarceo authored Oct 15, 2024
1 parent 9e13636 commit 95fe8c2
Showing 1 changed file with 152 additions and 1 deletion.
153 changes: 152 additions & 1 deletion sdk/python/tests/unit/test_on_demand_python_transformation.py
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Original file line number Diff line number Diff line change
Expand Up @@ -425,6 +425,13 @@ def setUp(self):
Field(name="avg_daily_trip_rank_names", dtype=Array(String)),
],
)
input_request = RequestSource(
name="vals_to_add",
schema=[
Field(name="val_to_add", dtype=Int64),
Field(name="val_to_add_2", dtype=Int64),
],
)

@on_demand_feature_view(
sources=[request_source, driver_stats_fv],
Expand Down Expand Up @@ -476,8 +483,37 @@ def python_view(inputs: dict[str, Any]) -> dict[str, Any]:
output["achieved_ranks"] = ranks
return output

@on_demand_feature_view(
sources=[
driver_stats_fv,
input_request,
],
schema=[
Field(name="conv_rate_plus_val1", dtype=Float64),
Field(name="conv_rate_plus_val2", dtype=Float64),
],
mode="pandas",
)
def pandas_view(features_df: pd.DataFrame) -> pd.DataFrame:
df = pd.DataFrame()
df["conv_rate_plus_val1"] = (
features_df["conv_rate"] + features_df["val_to_add"]
)
df["conv_rate_plus_val2"] = (
features_df["conv_rate"] + features_df["val_to_add_2"]
)
return df

self.store.apply(
[driver, driver_stats_source, driver_stats_fv, python_view]
[
driver,
driver_stats_source,
driver_stats_fv,
python_view,
pandas_view,
input_request,
request_source,
]
)
fv_applied = self.store.get_feature_view("driver_hourly_stats")
assert fv_applied.entities == [driver.name]
Expand All @@ -488,6 +524,121 @@ def python_view(inputs: dict[str, Any]) -> dict[str, Any]:
feature_view_name="driver_hourly_stats", df=driver_df
)

batch_sample = pd.DataFrame(driver_entities, columns=["driver_id"])
batch_sample["val_to_add"] = 0
batch_sample["val_to_add_2"] = 1
batch_sample["event_timestamp"] = start_date
batch_sample["created"] = start_date
fv_only_cols = ["driver_id", "event_timestamp", "created"]

resp_base_fv = self.store.get_historical_features(
entity_df=batch_sample[fv_only_cols],
features=[
"driver_hourly_stats:conv_rate",
"driver_hourly_stats:acc_rate",
"driver_hourly_stats:avg_daily_trips",
],
).to_df()
assert resp_base_fv is not None
assert sorted(resp_base_fv.columns) == [
"acc_rate",
"avg_daily_trips",
"conv_rate",
"created__",
"driver_id",
"event_timestamp",
]
resp = self.store.get_historical_features(
entity_df=batch_sample,
features=[
"driver_hourly_stats:conv_rate",
"driver_hourly_stats:acc_rate",
"driver_hourly_stats:avg_daily_trips",
"pandas_view:conv_rate_plus_val1",
"pandas_view:conv_rate_plus_val2",
],
).to_df()
assert resp is not None
assert resp["conv_rate_plus_val1"].isnull().sum() == 0

# Now testing feature retrieval for driver ids not in the dataset
missing_batch_sample = pd.DataFrame([1234567890], columns=["driver_id"])
missing_batch_sample["val_to_add"] = 0
missing_batch_sample["val_to_add_2"] = 1
missing_batch_sample["event_timestamp"] = start_date
missing_batch_sample["created"] = start_date
resp_offline = self.store.get_historical_features(
entity_df=missing_batch_sample,
features=[
"driver_hourly_stats:conv_rate",
"driver_hourly_stats:acc_rate",
"driver_hourly_stats:avg_daily_trips",
"pandas_view:conv_rate_plus_val1",
"pandas_view:conv_rate_plus_val2",
],
).to_df()
assert resp_offline is not None
assert resp_offline["conv_rate_plus_val1"].isnull().sum() == 1
assert sorted(resp_offline.columns) == [
"acc_rate",
"avg_daily_trips",
"conv_rate",
"conv_rate_plus_val1",
"conv_rate_plus_val2",
"created__",
"driver_id",
"event_timestamp",
"val_to_add",
"val_to_add_2",
]
with pytest.raises(TypeError):
_ = self.store.get_online_features(
entity_rows=[
{"driver_id": 1234567890, "val_to_add": 0, "val_to_add_2": 1}
],
features=[
"driver_hourly_stats:conv_rate",
"driver_hourly_stats:acc_rate",
"driver_hourly_stats:avg_daily_trips",
"pandas_view:conv_rate_plus_val1",
"pandas_view:conv_rate_plus_val2",
],
)
resp_online = self.store.get_online_features(
entity_rows=[{"driver_id": 1001, "val_to_add": 0, "val_to_add_2": 1}],
features=[
"driver_hourly_stats:conv_rate",
"driver_hourly_stats:acc_rate",
"driver_hourly_stats:avg_daily_trips",
"pandas_view:conv_rate_plus_val1",
"pandas_view:conv_rate_plus_val2",
],
).to_df()
assert resp_online is not None
assert sorted(resp_online.columns) == [
"acc_rate",
"avg_daily_trips",
"conv_rate",
"conv_rate_plus_val1",
"conv_rate_plus_val2",
"driver_id",
# It does not have the items below
# "created__",
# "event_timestamp",
# "val_to_add",
# "val_to_add_2",
]
# Note online and offline columns will not match because:
# you want to be space efficient online when considering the impact of network latency so you want to send
# and receive the minimally required set of data, which means after transformation you only need to send the
# output in the response.
# Offline, you will probably prioritize reproducibility and being able to iterate, which means you will want
# the underlying inputs into your transformation, so the extra data is tolerable.
assert sorted(resp_online.columns) != sorted(resp_offline.columns)

def test_setup(self):
pass

def test_python_transformation_returning_all_data_types(self):
entity_rows = [
{
Expand Down

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