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test_on_demand_feature_view.py
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# Copyright 2022 The Feast Authors
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import datetime
from typing import Any, Dict, List
import pandas as pd
import pytest
from feast.feature_view import FeatureView
from feast.field import Field
from feast.infra.offline_stores.file_source import FileSource
from feast.on_demand_feature_view import (
OnDemandFeatureView,
PandasTransformation,
PythonTransformation,
)
from feast.types import Float32
def udf1(features_df: pd.DataFrame) -> pd.DataFrame:
df = pd.DataFrame()
df["output1"] = features_df["feature1"]
df["output2"] = features_df["feature2"]
return df
def udf2(features_df: pd.DataFrame) -> pd.DataFrame:
df = pd.DataFrame()
df["output1"] = features_df["feature1"] + 100
df["output2"] = features_df["feature2"] + 101
return df
def python_native_udf(features_dict: Dict[str, Any]) -> Dict[str, Any]:
output_dict: Dict[str, List[Any]] = {
"output1": features_dict["feature1"] + 100,
"output2": features_dict["feature2"] + 101,
}
return output_dict
def python_writes_test_udf(features_dict: Dict[str, Any]) -> Dict[str, Any]:
output_dict: Dict[str, List[Any]] = {
"output1": features_dict["feature1"] + 100,
"output2": features_dict["feature2"] + 101,
"output3": datetime.datetime.now(),
}
return output_dict
@pytest.mark.filterwarnings("ignore:udf and udf_string parameters are deprecated")
def test_hash():
file_source = FileSource(name="my-file-source", path="test.parquet")
feature_view = FeatureView(
name="my-feature-view",
entities=[],
schema=[
Field(name="feature1", dtype=Float32),
Field(name="feature2", dtype=Float32),
],
source=file_source,
)
sources = [feature_view]
on_demand_feature_view_1 = OnDemandFeatureView(
name="my-on-demand-feature-view",
sources=sources,
schema=[
Field(name="output1", dtype=Float32),
Field(name="output2", dtype=Float32),
],
feature_transformation=PandasTransformation(
udf=udf1, udf_string="udf1 source code"
),
)
on_demand_feature_view_2 = OnDemandFeatureView(
name="my-on-demand-feature-view",
sources=sources,
schema=[
Field(name="output1", dtype=Float32),
Field(name="output2", dtype=Float32),
],
feature_transformation=PandasTransformation(
udf=udf1, udf_string="udf1 source code"
),
)
on_demand_feature_view_3 = OnDemandFeatureView(
name="my-on-demand-feature-view",
sources=sources,
schema=[
Field(name="output1", dtype=Float32),
Field(name="output2", dtype=Float32),
],
feature_transformation=PandasTransformation(
udf=udf2, udf_string="udf2 source code"
),
)
on_demand_feature_view_4 = OnDemandFeatureView(
name="my-on-demand-feature-view",
sources=sources,
schema=[
Field(name="output1", dtype=Float32),
Field(name="output2", dtype=Float32),
],
feature_transformation=PandasTransformation(
udf=udf2, udf_string="udf2 source code"
),
description="test",
)
on_demand_feature_view_5 = OnDemandFeatureView(
name="my-on-demand-feature-view",
sources=sources,
schema=[
Field(name="output1", dtype=Float32),
Field(name="output2", dtype=Float32),
],
feature_transformation=PandasTransformation(
udf=udf2, udf_string="udf2 source code"
),
description="test",
)
s1 = {on_demand_feature_view_1, on_demand_feature_view_2}
assert len(s1) == 1
s2 = {on_demand_feature_view_1, on_demand_feature_view_3}
assert len(s2) == 2
s3 = {on_demand_feature_view_3, on_demand_feature_view_4}
assert len(s3) == 2
s4 = {
on_demand_feature_view_1,
on_demand_feature_view_2,
on_demand_feature_view_3,
on_demand_feature_view_4,
}
assert len(s4) == 3
assert on_demand_feature_view_5.feature_transformation == PandasTransformation(
udf2, "udf2 source code"
)
def test_python_native_transformation_mode():
file_source = FileSource(name="my-file-source", path="test.parquet")
feature_view = FeatureView(
name="my-feature-view",
entities=[],
schema=[
Field(name="feature1", dtype=Float32),
Field(name="feature2", dtype=Float32),
],
source=file_source,
)
sources = [feature_view]
on_demand_feature_view_python_native = OnDemandFeatureView(
name="my-on-demand-feature-view",
sources=sources,
schema=[
Field(name="output1", dtype=Float32),
Field(name="output2", dtype=Float32),
],
feature_transformation=PythonTransformation(
udf=python_native_udf, udf_string="python native udf source code"
),
description="test",
mode="python",
)
on_demand_feature_view_python_native_err = OnDemandFeatureView(
name="my-on-demand-feature-view",
sources=sources,
schema=[
Field(name="output1", dtype=Float32),
Field(name="output2", dtype=Float32),
],
feature_transformation=PandasTransformation(
udf=python_native_udf, udf_string="python native udf source code"
),
description="test",
mode="python",
)
assert (
on_demand_feature_view_python_native.feature_transformation
== PythonTransformation(python_native_udf, "python native udf source code")
)
with pytest.raises(TypeError):
assert (
on_demand_feature_view_python_native_err.feature_transformation
== PythonTransformation(python_native_udf, "python native udf source code")
)
assert on_demand_feature_view_python_native.transform_dict(
{
"feature1": 0,
"feature2": 1,
}
) == {"feature1": 0, "feature2": 1, "output1": 100, "output2": 102}
@pytest.mark.filterwarnings("ignore:udf and udf_string parameters are deprecated")
def test_from_proto_backwards_compatible_udf():
file_source = FileSource(name="my-file-source", path="test.parquet")
feature_view = FeatureView(
name="my-feature-view",
entities=[],
schema=[
Field(name="feature1", dtype=Float32),
Field(name="feature2", dtype=Float32),
],
source=file_source,
)
sources = [feature_view]
on_demand_feature_view = OnDemandFeatureView(
name="my-on-demand-feature-view",
sources=sources,
schema=[
Field(name="output1", dtype=Float32),
Field(name="output2", dtype=Float32),
],
feature_transformation=PandasTransformation(
udf=udf1, udf_string="udf1 source code"
),
)
# We need a proto with the "udf1 source code" in the user_defined_function.body_text
# and to populate it in feature_transformation
proto = on_demand_feature_view.to_proto()
assert (
on_demand_feature_view.feature_transformation.udf_string
== proto.spec.feature_transformation.user_defined_function.body_text
)
# Because of the current set of code this is just confirming it is empty
assert proto.spec.user_defined_function.body_text == ""
assert proto.spec.user_defined_function.body == b""
assert proto.spec.user_defined_function.name == ""
# Assuming we pull it from the registry we set it to the feature_transformation proto values
proto.spec.user_defined_function.name = (
proto.spec.feature_transformation.user_defined_function.name
)
proto.spec.user_defined_function.body = (
proto.spec.feature_transformation.user_defined_function.body
)
proto.spec.user_defined_function.body_text = (
proto.spec.feature_transformation.user_defined_function.body_text
)
# For objects that are already registered, feature_transformation and mode is not set
proto.spec.feature_transformation.Clear()
proto.spec.ClearField("mode")
# And now we expect the to get the same object back under feature_transformation
reserialized_proto = OnDemandFeatureView.from_proto(proto)
assert (
reserialized_proto.feature_transformation.udf_string
== on_demand_feature_view.feature_transformation.udf_string
)
def test_on_demand_feature_view_writes_protos():
file_source = FileSource(name="my-file-source", path="test.parquet")
feature_view = FeatureView(
name="my-feature-view",
entities=[],
schema=[
Field(name="feature1", dtype=Float32),
Field(name="feature2", dtype=Float32),
],
source=file_source,
)
sources = [feature_view]
on_demand_feature_view = OnDemandFeatureView(
name="my-on-demand-feature-view",
sources=sources,
schema=[
Field(name="output1", dtype=Float32),
Field(name="output2", dtype=Float32),
],
feature_transformation=PandasTransformation(
udf=udf1, udf_string="udf1 source code"
),
write_to_online_store=True,
)
proto = on_demand_feature_view.to_proto()
reserialized_proto = OnDemandFeatureView.from_proto(proto)
assert on_demand_feature_view.write_to_online_store
assert proto.spec.write_to_online_store
assert reserialized_proto.write_to_online_store
proto.spec.write_to_online_store = False
reserialized_proto = OnDemandFeatureView.from_proto(proto)
assert not reserialized_proto.write_to_online_store
def test_on_demand_feature_view_stored_writes():
file_source = FileSource(name="my-file-source", path="test.parquet")
feature_view = FeatureView(
name="my-feature-view",
entities=[],
schema=[
Field(name="feature1", dtype=Float32),
Field(name="feature2", dtype=Float32),
],
source=file_source,
)
sources = [feature_view]
on_demand_feature_view = OnDemandFeatureView(
name="my-on-demand-feature-view",
sources=sources,
schema=[
Field(name="output1", dtype=Float32),
Field(name="output2", dtype=Float32),
],
feature_transformation=PythonTransformation(
udf=python_writes_test_udf, udf_string="python native udf source code"
),
description="testing on demand feature view stored writes",
mode="python",
write_to_online_store=True,
)
transformed_output = on_demand_feature_view.transform_dict(
{
"feature1": 0,
"feature2": 1,
}
)
expected_output = {"feature1": 0, "feature2": 1, "output1": 100, "output2": 102}
keys_to_validate = [
"feature1",
"feature2",
"output1",
"output2",
]
for k in keys_to_validate:
assert transformed_output[k] == expected_output[k]
assert transformed_output["output3"] is not None and isinstance(
transformed_output["output3"], datetime.datetime
)