def test_request_data_source_deprecation():
with pytest.warns(DeprecationWarning):
request_data_source = RequestDataSource(
name="vals_to_add",
schema={"val_to_add": ValueType.INT64, "val_to_add_2": ValueType.INT64},
)
request_data_source_proto = request_data_source.to_proto()
returned_request_source = RequestSource.from_proto(request_data_source_proto)
assert returned_request_source == request_data_source
def create_driver_age_request_feature_view():
return RequestFeatureView(
name="driver_age",
request_data_source=RequestDataSource(
name="driver_age_source", schema={"driver_age": ValueType.INT32}
),
)
def from_proto(cls, request_feature_view_proto: RequestFeatureViewProto):
"""
Creates a request feature view from a protobuf representation.
Args:
request_feature_view_proto: A protobuf representation of an request feature view.
Returns:
A RequestFeatureView object based on the request feature view protobuf.
"""
request_feature_view_obj = cls(
name=request_feature_view_proto.spec.name,
request_data_source=RequestDataSource.from_proto(
request_feature_view_proto.spec.request_data_source
),
)
# FeatureViewProjections are not saved in the RequestFeatureView proto.
# Create the default projection.
request_feature_view_obj.projection = FeatureViewProjection.from_definition(
request_feature_view_obj
)
return request_feature_view_obj
def create_similarity_request_data_source():
return RequestDataSource(
name="similarity_input",
schema={
"vector_double": ValueType.DOUBLE_LIST,
"vector_float": ValueType.FLOAT_LIST,
},
)
def from_proto(cls,
on_demand_feature_view_proto: OnDemandFeatureViewProto):
"""
Creates an on demand feature view from a protobuf representation.
Args:
on_demand_feature_view_proto: A protobuf representation of an on-demand feature view.
Returns:
A OnDemandFeatureView object based on the on-demand feature view protobuf.
"""
inputs = {}
for (
input_name,
on_demand_input,
) in on_demand_feature_view_proto.spec.inputs.items():
if on_demand_input.WhichOneof("input") == "feature_view":
inputs[input_name] = FeatureView.from_proto(
on_demand_input.feature_view).projection
elif on_demand_input.WhichOneof(
"input") == "feature_view_projection":
inputs[input_name] = FeatureViewProjection.from_proto(
on_demand_input.feature_view_projection)
else:
inputs[input_name] = RequestDataSource.from_proto(
on_demand_input.request_data_source)
on_demand_feature_view_obj = cls(
name=on_demand_feature_view_proto.spec.name,
features=[
Feature(
name=feature.name,
dtype=ValueType(feature.value_type),
labels=dict(feature.labels),
) for feature in on_demand_feature_view_proto.spec.features
],
inputs=inputs,
udf=dill.loads(
on_demand_feature_view_proto.spec.user_defined_function.body),
)
# FeatureViewProjections are not saved in the OnDemandFeatureView proto.
# Create the default projection.
on_demand_feature_view_obj.projection = FeatureViewProjection.from_definition(
on_demand_feature_view_obj)
if on_demand_feature_view_proto.meta.HasField("created_timestamp"):
on_demand_feature_view_obj.created_timestamp = (
on_demand_feature_view_proto.meta.created_timestamp.ToDatetime(
))
return on_demand_feature_view_obj
def test_modify_feature_views_success():
# Create Feature Views
date_request = RequestDataSource(
name="date_request", schema={"some_date": ValueType.UNIX_TIMESTAMP})
@on_demand_feature_view(
inputs={"date_request": date_request},
features=[Feature("output", ValueType.UNIX_TIMESTAMP)],
)
def test_view(features_df: pd.DataFrame) -> pd.DataFrame:
data = pd.DataFrame()
data["output"] = features_df["some_date"]
return data
test_view.infer_features()
def from_proto(cls,
on_demand_feature_view_proto: OnDemandFeatureViewProto):
"""
Creates an on demand feature view from a protobuf representation.
Args:
on_demand_feature_view_proto: A protobuf representation of an on-demand feature view.
Returns:
A OnDemandFeatureView object based on the on-demand feature view protobuf.
"""
inputs = {}
for (
input_name,
on_demand_input,
) in on_demand_feature_view_proto.spec.inputs.items():
if on_demand_input.WhichOneof("input") == "feature_view":
inputs[input_name] = FeatureView.from_proto(
on_demand_input.feature_view)
else:
inputs[input_name] = RequestDataSource.from_proto(
on_demand_input.request_data_source)
on_demand_feature_view_obj = cls(
name=on_demand_feature_view_proto.spec.name,
features=[
Feature(
name=feature.name,
dtype=ValueType(feature.value_type),
labels=dict(feature.labels),
) for feature in on_demand_feature_view_proto.spec.features
],
inputs=inputs,
udf=dill.loads(
on_demand_feature_view_proto.spec.user_defined_function.body),
)
return on_demand_feature_view_obj
def create_conv_rate_request_data_source():
return RequestDataSource(
name="conv_rate_input", schema={"val_to_add": ValueType.INT32}
)
),
Feature(
name="string_feature",
dtype=ValueType.STRING,
labels=dict(description="String feature"),
),
],
online=True,
batch_source=driver_hourly_stats_source,
tags={},
)
input_request = RequestDataSource(
name="vals_to_add",
schema={
"val_to_add": ValueType.INT64,
"val_to_add_2": ValueType.INT64
},
)
@on_demand_feature_view( # type: ignore
inputs={
"driver_hourly_stats": driver_hourly_stats_view,
"vals_to_add": input_request,
},
features=[
Feature(name="conv_rate_plus_val1", dtype=ValueType.DOUBLE),
Feature(name="conv_rate_plus_val2", dtype=ValueType.DOUBLE),
],
)