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Merged
franciscojavierarceo merged 9 commits into from
Apr 4, 2024
Merged

chore: Adding more tests for On Demand Feature Views #4069

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066880f
checking in progress...trying to fix tests
franciscojavierarceo Apr 3, 2024
79e6edc
testing more...
franciscojavierarceo Apr 3, 2024
262c586
fixed
franciscojavierarceo Apr 3, 2024
0ebfe7d
fixed some tests
franciscojavierarceo Apr 3, 2024
270f6c0
fixed test and serialization
franciscojavierarceo Apr 4, 2024
3574bcc
removed commented out code
franciscojavierarceo Apr 4, 2024
1d3745d
lint
franciscojavierarceo Apr 4, 2024
255ba05
added a test to make it explicit that feature calculation must happen…
franciscojavierarceo Apr 4, 2024
924ebd5
linter
franciscojavierarceo Apr 4, 2024
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13 changes: 13 additions & 0 deletions sdk/python/feast/on_demand_feature_view.py
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Original file line number Diff line number Diff line change
Expand Up @@ -300,10 +300,23 @@ def from_proto(
== "user_defined_function"
and on_demand_feature_view_proto.spec.feature_transformation.user_defined_function.body_text
!= ""
and on_demand_feature_view_proto.spec.mode == "pandas"
):
transformation = PandasTransformation.from_proto(
on_demand_feature_view_proto.spec.feature_transformation.user_defined_function
)
elif (
on_demand_feature_view_proto.spec.feature_transformation.WhichOneof(
"transformation"
)
== "user_defined_function"
and on_demand_feature_view_proto.spec.feature_transformation.user_defined_function.body_text
!= ""
and on_demand_feature_view_proto.spec.mode == "python"
):
transformation = PythonTransformation.from_proto(
on_demand_feature_view_proto.spec.feature_transformation.user_defined_function
)
elif (
on_demand_feature_view_proto.spec.feature_transformation.WhichOneof(
"transformation"
Expand Down
14 changes: 14 additions & 0 deletions sdk/python/tests/example_repos/example_feature_repo_1.py
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Original file line number Diff line number Diff line change
@@ -1,6 +1,9 @@
from datetime import timedelta

import pandas as pd

from feast import Entity, FeatureService, FeatureView, Field, FileSource, PushSource
from feast.on_demand_feature_view import on_demand_feature_view
from feast.types import Float32, Int64, String

# Note that file source paths are not validated, so there doesn't actually need to be any data
Expand Down Expand Up @@ -99,6 +102,17 @@
)


@on_demand_feature_view(
sources=[customer_profile],
schema=[Field(name="on_demand_age", dtype=Int64)],
mode="pandas",
)
def customer_profile_pandas_odfv(inputs: pd.DataFrame) -> pd.DataFrame:
outputs = pd.DataFrame()
outputs["on_demand_age"] = inputs["age"] + 1
return outputs


all_drivers_feature_service = FeatureService(
name="driver_locations_service",
features=[driver_locations],
Expand Down
11 changes: 11 additions & 0 deletions sdk/python/tests/unit/online_store/test_online_retrieval.py
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Original file line number Diff line number Diff line change
Expand Up @@ -124,6 +124,17 @@ def test_online() -> None:

assert "trips" in result

result = store.get_online_features(
features=["customer_profile_pandas_odfv:on_demand_age"],
entity_rows=[{"driver_id": 1, "customer_id": "5"}],
full_feature_names=False,
).to_dict()

assert "on_demand_age" in result
assert result["driver_id"] == [1]
assert result["customer_id"] == ["5"]
assert result["on_demand_age"] == [4]

# invalid table reference
with pytest.raises(FeatureViewNotFoundException):
store.get_online_features(
Expand Down
93 changes: 93 additions & 0 deletions sdk/python/tests/unit/test_on_demand_pandas_transformation.py
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Original file line number Diff line number Diff line change
@@ -0,0 +1,93 @@
import os
import tempfile
from datetime import datetime, timedelta

import pandas as pd

from feast import Entity, FeatureStore, FeatureView, FileSource, RepoConfig
from feast.driver_test_data import create_driver_hourly_stats_df
from feast.field import Field
from feast.infra.online_stores.sqlite import SqliteOnlineStoreConfig
from feast.on_demand_feature_view import on_demand_feature_view
from feast.types import Float32, Float64, Int64


def test_pandas_transformation():
with tempfile.TemporaryDirectory() as data_dir:
store = FeatureStore(
config=RepoConfig(
project="test_on_demand_python_transformation",
registry=os.path.join(data_dir, "registry.db"),
provider="local",
entity_key_serialization_version=2,
online_store=SqliteOnlineStoreConfig(
path=os.path.join(data_dir, "online.db")
),
)
)

# Generate test data.
end_date = datetime.now().replace(microsecond=0, second=0, minute=0)
start_date = end_date - timedelta(days=15)

driver_entities = [1001, 1002, 1003, 1004, 1005]
driver_df = create_driver_hourly_stats_df(driver_entities, start_date, end_date)
driver_stats_path = os.path.join(data_dir, "driver_stats.parquet")
driver_df.to_parquet(path=driver_stats_path, allow_truncated_timestamps=True)

driver = Entity(name="driver", join_keys=["driver_id"])

driver_stats_source = FileSource(
name="driver_hourly_stats_source",
path=driver_stats_path,
timestamp_field="event_timestamp",
created_timestamp_column="created",
)

driver_stats_fv = FeatureView(
name="driver_hourly_stats",
entities=[driver],
ttl=timedelta(days=0),
schema=[
Field(name="conv_rate", dtype=Float32),
Field(name="acc_rate", dtype=Float32),
Field(name="avg_daily_trips", dtype=Int64),
],
online=True,
source=driver_stats_source,
)

@on_demand_feature_view(
sources=[driver_stats_fv],
schema=[Field(name="conv_rate_plus_acc", dtype=Float64)],
mode="pandas",
)
def pandas_view(inputs: pd.DataFrame) -> pd.DataFrame:
df = pd.DataFrame()
df["conv_rate_plus_acc"] = inputs["conv_rate"] + inputs["acc_rate"]
return df

store.apply([driver, driver_stats_source, driver_stats_fv, pandas_view])

entity_rows = [
{
"driver_id": 1001,
}
]
store.write_to_online_store(
feature_view_name="driver_hourly_stats", df=driver_df
)

online_response = store.get_online_features(
entity_rows=entity_rows,
features=[
"driver_hourly_stats:conv_rate",
"driver_hourly_stats:acc_rate",
"driver_hourly_stats:avg_daily_trips",
"pandas_view:conv_rate_plus_acc",
],
).to_df()

assert online_response["conv_rate_plus_acc"].equals(
online_response["conv_rate"] + online_response["acc_rate"]
)
172 changes: 172 additions & 0 deletions sdk/python/tests/unit/test_on_demand_python_transformation.py
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Original file line number Diff line number Diff line change
@@ -0,0 +1,172 @@
import os
import tempfile
import unittest
from datetime import datetime, timedelta
from typing import Any, Dict

import pandas as pd
import pytest

from feast import Entity, FeatureStore, FeatureView, FileSource, RepoConfig
from feast.driver_test_data import create_driver_hourly_stats_df
from feast.field import Field
from feast.infra.online_stores.sqlite import SqliteOnlineStoreConfig
from feast.on_demand_feature_view import on_demand_feature_view
from feast.types import Float32, Float64, Int64


class TestOnDemandPythonTransformation(unittest.TestCase):
def setUp(self):
with tempfile.TemporaryDirectory() as data_dir:
self.store = FeatureStore(
config=RepoConfig(
project="test_on_demand_python_transformation",
registry=os.path.join(data_dir, "registry.db"),
provider="local",
entity_key_serialization_version=2,
online_store=SqliteOnlineStoreConfig(
path=os.path.join(data_dir, "online.db")
),
)
)

# Generate test data.
end_date = datetime.now().replace(microsecond=0, second=0, minute=0)
start_date = end_date - timedelta(days=15)

driver_entities = [1001, 1002, 1003, 1004, 1005]
driver_df = create_driver_hourly_stats_df(
driver_entities, start_date, end_date
)
driver_stats_path = os.path.join(data_dir, "driver_stats.parquet")
driver_df.to_parquet(
path=driver_stats_path, allow_truncated_timestamps=True
)

driver = Entity(name="driver", join_keys=["driver_id"])

driver_stats_source = FileSource(
name="driver_hourly_stats_source",
path=driver_stats_path,
timestamp_field="event_timestamp",
created_timestamp_column="created",
)

driver_stats_fv = FeatureView(
name="driver_hourly_stats",
entities=[driver],
ttl=timedelta(days=0),
schema=[
Field(name="conv_rate", dtype=Float32),
Field(name="acc_rate", dtype=Float32),
Field(name="avg_daily_trips", dtype=Int64),
],
online=True,
source=driver_stats_source,
)

@on_demand_feature_view(
sources=[driver_stats_fv],
schema=[Field(name="conv_rate_plus_acc_pandas", dtype=Float64)],
mode="pandas",
)
def pandas_view(inputs: pd.DataFrame) -> pd.DataFrame:
df = pd.DataFrame()
df["conv_rate_plus_acc_pandas"] = (
inputs["conv_rate"] + inputs["acc_rate"]
)
return df

@on_demand_feature_view(
sources=[driver_stats_fv[["conv_rate", "acc_rate"]]],
schema=[Field(name="conv_rate_plus_acc_python", dtype=Float64)],
mode="python",
)
def python_view(inputs: Dict[str, Any]) -> Dict[str, Any]:
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@tokoko tokoko Apr 4, 2024

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I'm still a little confused about the required signature here. Are these functions supposed to accept a dict of lists (looks like that in this test) and apply the udf for all entities at once? I thought from the previous PR that the goal was to have a udf that would be applied to individual entities...

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@tokoko tokoko Apr 4, 2024

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Can you also alter the tests so that more than one entity is passed? this will probably fail in such a case as only first entity is processed. If we are sticking with this signature, udf should look something like this:

return {
                'conv_rate_plus_acc_python': [
                    conv_rate + acc_rate
                    for conv_rate, acc_rate in zip(inputs['conv_rate'], inputs['acc_rate'])
                ]
            }

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@franciscojavierarceo franciscojavierarceo Apr 4, 2024

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If you look at _infer_features_dict you'll see it expects a dict of lists. I added an explicit test that shows this will result in a type failure when running the apply operations. We can add singleton execution as a follow up but this is sufficient to highlight the currently supported behavior and then we can cut a release.

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@tokoko tokoko Apr 4, 2024

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@franciscojavierarceo got it, good... that's probably more efficient anyway. no rush, but in that case it will probably be a good idea to change type annotations for relevant functions to Dict[str, List[Any]].

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@franciscojavierarceo franciscojavierarceo Apr 4, 2024

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I actually originally had that setup but I received a ton of type failures from that which is why I did it this way.

Let me address both of those as folllowups. I want to merge this and cut a release.

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@franciscojavierarceo franciscojavierarceo Apr 4, 2024

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I made an issue here #4075, will close it later.

output: Dict[str, Any] = {
"conv_rate_plus_acc_python": [
conv_rate + acc_rate
for conv_rate, acc_rate in zip(
inputs["conv_rate"], inputs["acc_rate"]
)
]
}
return output

@on_demand_feature_view(
sources=[driver_stats_fv[["conv_rate", "acc_rate"]]],
schema=[
Field(name="conv_rate_plus_acc_python_singleton", dtype=Float64)
],
mode="python",
)
def python_singleton_view(inputs: Dict[str, Any]) -> Dict[str, Any]:
output: Dict[str, Any] = dict(conv_rate_plus_acc_python=float("-inf"))
output["conv_rate_plus_acc_python_singleton"] = (
inputs["conv_rate"] + inputs["acc_rate"]
)
return output

with pytest.raises(TypeError):
# Note the singleton view will fail as the type is
# expected to be a List which can be confirmed in _infer_features_dict
self.store.apply(
[
driver,
driver_stats_source,
driver_stats_fv,
pandas_view,
python_view,
python_singleton_view,
]
)

self.store.apply(
[driver, driver_stats_source, driver_stats_fv, pandas_view, python_view]
)
self.store.write_to_online_store(
feature_view_name="driver_hourly_stats", df=driver_df
)

def test_python_pandas_parity(self):
entity_rows = [
{
"driver_id": 1001,
}
]

online_python_response = self.store.get_online_features(
entity_rows=entity_rows,
features=[
"driver_hourly_stats:conv_rate",
"driver_hourly_stats:acc_rate",
"python_view:conv_rate_plus_acc_python",
],
).to_dict()

online_pandas_response = self.store.get_online_features(
entity_rows=entity_rows,
features=[
"driver_hourly_stats:conv_rate",
"driver_hourly_stats:acc_rate",
"pandas_view:conv_rate_plus_acc_pandas",
],
).to_df()

assert len(online_python_response) == 4
assert all(
key in online_python_response.keys()
for key in [
"driver_id",
"acc_rate",
"conv_rate",
"conv_rate_plus_acc_python",
]
)
assert len(online_python_response["conv_rate_plus_acc_python"]) == 1
assert (
online_python_response["conv_rate_plus_acc_python"][0]
== online_pandas_response["conv_rate_plus_acc_pandas"][0]
== online_python_response["conv_rate"][0]
+ online_python_response["acc_rate"][0]
)