import copy
import functools
import warnings
from datetime import datetime
from types import FunctionType
from typing import Any, Dict, List, Optional, Type, Union
import dill
import pandas as pd
from typeguard import typechecked
from feast.base_feature_view import BaseFeatureView
from feast.batch_feature_view import BatchFeatureView
from feast.data_source import RequestSource
from feast.errors import RegistryInferenceFailure, SpecifiedFeaturesNotPresentError
from feast.feature_view import FeatureView
from feast.feature_view_projection import FeatureViewProjection
from feast.field import Field, from_value_type
from feast.protos.feast.core.OnDemandFeatureView_pb2 import (
OnDemandFeatureView as OnDemandFeatureViewProto,
)
from feast.protos.feast.core.OnDemandFeatureView_pb2 import (
OnDemandFeatureViewMeta,
OnDemandFeatureViewSpec,
OnDemandSource,
)
from feast.protos.feast.core.OnDemandFeatureView_pb2 import (
UserDefinedFunction as UserDefinedFunctionProto,
)
from feast.type_map import (
feast_value_type_to_pandas_type,
python_type_to_feast_value_type,
)
from feast.usage import log_exceptions
from feast.value_type import ValueType
warnings.simplefilter("once", DeprecationWarning)
[docs]@typechecked
class OnDemandFeatureView(BaseFeatureView):
"""
[Experimental] An OnDemandFeatureView defines a logical group of features that are
generated by applying a transformation on a set of input sources, such as feature
views and request data sources.
Attributes:
name: The unique name of the on demand feature view.
features: The list of features in the output of the on demand feature view.
source_feature_view_projections: A map from input source names to actual input
sources with type FeatureViewProjection.
source_request_sources: A map from input source names to the actual input
sources with type RequestSource.
udf: The user defined transformation function, which must take pandas dataframes
as inputs.
description: A human-readable description.
tags: A dictionary of key-value pairs to store arbitrary metadata.
owner: The owner of the on demand feature view, typically the email of the primary
maintainer.
"""
name: str
features: List[Field]
source_feature_view_projections: Dict[str, FeatureViewProjection]
source_request_sources: Dict[str, RequestSource]
udf: FunctionType
udf_string: str
description: str
tags: Dict[str, str]
owner: str
@log_exceptions # noqa: C901
def __init__( # noqa: C901
self,
*,
name: str,
schema: List[Field],
sources: List[
Union[
FeatureView,
RequestSource,
FeatureViewProjection,
]
],
udf: FunctionType,
udf_string: str = "",
description: str = "",
tags: Optional[Dict[str, str]] = None,
owner: str = "",
):
"""
Creates an OnDemandFeatureView object.
Args:
name: The unique name of the on demand feature view.
schema: The list of features in the output of the on demand feature view, after
the transformation has been applied.
sources: A map from input source names to the actual input sources, which may be
feature views, or request data sources. These sources serve as inputs to the udf,
which will refer to them by name.
udf: The user defined transformation function, which must take pandas
dataframes as inputs.
udf_string: The source code version of the udf (for diffing and displaying in Web UI)
description (optional): A human-readable description.
tags (optional): A dictionary of key-value pairs to store arbitrary metadata.
owner (optional): The owner of the on demand feature view, typically the email
of the primary maintainer.
"""
super().__init__(
name=name,
features=schema,
description=description,
tags=tags,
owner=owner,
)
self.source_feature_view_projections: Dict[str, FeatureViewProjection] = {}
self.source_request_sources: Dict[str, RequestSource] = {}
for odfv_source in sources:
if isinstance(odfv_source, RequestSource):
self.source_request_sources[odfv_source.name] = odfv_source
elif isinstance(odfv_source, FeatureViewProjection):
self.source_feature_view_projections[odfv_source.name] = odfv_source
else:
self.source_feature_view_projections[
odfv_source.name
] = odfv_source.projection
self.udf = udf # type: ignore
self.udf_string = udf_string
@property
def proto_class(self) -> Type[OnDemandFeatureViewProto]:
return OnDemandFeatureViewProto
def __copy__(self):
fv = OnDemandFeatureView(
name=self.name,
schema=self.features,
sources=list(self.source_feature_view_projections.values())
+ list(self.source_request_sources.values()),
udf=self.udf,
udf_string=self.udf_string,
description=self.description,
tags=self.tags,
owner=self.owner,
)
fv.projection = copy.copy(self.projection)
return fv
def __eq__(self, other):
if not isinstance(other, OnDemandFeatureView):
raise TypeError(
"Comparisons should only involve OnDemandFeatureView class objects."
)
if not super().__eq__(other):
return False
if (
self.source_feature_view_projections
!= other.source_feature_view_projections
or self.source_request_sources != other.source_request_sources
or self.udf_string != other.udf_string
or self.udf.__code__.co_code != other.udf.__code__.co_code
):
return False
return True
def __hash__(self):
return super().__hash__()
[docs] def to_proto(self) -> OnDemandFeatureViewProto:
"""
Converts an on demand feature view object to its protobuf representation.
Returns:
A OnDemandFeatureViewProto protobuf.
"""
meta = OnDemandFeatureViewMeta()
if self.created_timestamp:
meta.created_timestamp.FromDatetime(self.created_timestamp)
if self.last_updated_timestamp:
meta.last_updated_timestamp.FromDatetime(self.last_updated_timestamp)
sources = {}
for source_name, fv_projection in self.source_feature_view_projections.items():
sources[source_name] = OnDemandSource(
feature_view_projection=fv_projection.to_proto()
)
for (
source_name,
request_sources,
) in self.source_request_sources.items():
sources[source_name] = OnDemandSource(
request_data_source=request_sources.to_proto()
)
spec = OnDemandFeatureViewSpec(
name=self.name,
features=[feature.to_proto() for feature in self.features],
sources=sources,
user_defined_function=UserDefinedFunctionProto(
name=self.udf.__name__,
body=dill.dumps(self.udf, recurse=True),
body_text=self.udf_string,
),
description=self.description,
tags=self.tags,
owner=self.owner,
)
return OnDemandFeatureViewProto(spec=spec, meta=meta)
[docs] @classmethod
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.
"""
sources = []
for (
_,
on_demand_source,
) in on_demand_feature_view_proto.spec.sources.items():
if on_demand_source.WhichOneof("source") == "feature_view":
sources.append(
FeatureView.from_proto(on_demand_source.feature_view).projection
)
elif on_demand_source.WhichOneof("source") == "feature_view_projection":
sources.append(
FeatureViewProjection.from_proto(
on_demand_source.feature_view_projection
)
)
else:
sources.append(
RequestSource.from_proto(on_demand_source.request_data_source)
)
on_demand_feature_view_obj = cls(
name=on_demand_feature_view_proto.spec.name,
schema=[
Field(
name=feature.name,
dtype=from_value_type(ValueType(feature.value_type)),
)
for feature in on_demand_feature_view_proto.spec.features
],
sources=sources,
udf=dill.loads(
on_demand_feature_view_proto.spec.user_defined_function.body
),
udf_string=on_demand_feature_view_proto.spec.user_defined_function.body_text,
description=on_demand_feature_view_proto.spec.description,
tags=dict(on_demand_feature_view_proto.spec.tags),
owner=on_demand_feature_view_proto.spec.owner,
)
# 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()
)
if on_demand_feature_view_proto.meta.HasField("last_updated_timestamp"):
on_demand_feature_view_obj.last_updated_timestamp = (
on_demand_feature_view_proto.meta.last_updated_timestamp.ToDatetime()
)
return on_demand_feature_view_obj
[docs] def get_request_data_schema(self) -> Dict[str, ValueType]:
schema: Dict[str, ValueType] = {}
for request_source in self.source_request_sources.values():
if isinstance(request_source.schema, List):
new_schema = {}
for field in request_source.schema:
new_schema[field.name] = field.dtype.to_value_type()
schema.update(new_schema)
elif isinstance(request_source.schema, Dict):
schema.update(request_source.schema)
else:
raise Exception(
f"Request source schema is not correct type: ${str(type(request_source.schema))}"
)
return schema
[docs] def infer_features(self):
"""
Infers the set of features associated to this feature view from the input source.
Raises:
RegistryInferenceFailure: The set of features could not be inferred.
"""
rand_df_value: Dict[str, Any] = {
"float": 1.0,
"int": 1,
"str": "hello world",
"bytes": str.encode("hello world"),
"bool": True,
"datetime64[ns]": datetime.utcnow(),
}
df = pd.DataFrame()
for feature_view_projection in self.source_feature_view_projections.values():
for feature in feature_view_projection.features:
dtype = feast_value_type_to_pandas_type(feature.dtype.to_value_type())
df[f"{feature_view_projection.name}__{feature.name}"] = pd.Series(
dtype=dtype
)
sample_val = rand_df_value[dtype] if dtype in rand_df_value else None
df[f"{feature.name}"] = pd.Series(data=sample_val, dtype=dtype)
for request_data in self.source_request_sources.values():
for field in request_data.schema:
dtype = feast_value_type_to_pandas_type(field.dtype.to_value_type())
sample_val = rand_df_value[dtype] if dtype in rand_df_value else None
df[f"{field.name}"] = pd.Series(sample_val, dtype=dtype)
output_df: pd.DataFrame = self.udf.__call__(df)
inferred_features = []
for f, dt in zip(output_df.columns, output_df.dtypes):
inferred_features.append(
Field(
name=f,
dtype=from_value_type(
python_type_to_feast_value_type(f, type_name=str(dt))
),
)
)
if self.features:
missing_features = []
for specified_features in self.features:
if specified_features not in inferred_features:
missing_features.append(specified_features)
if missing_features:
raise SpecifiedFeaturesNotPresentError(
missing_features, inferred_features, self.name
)
else:
self.features = inferred_features
if not self.features:
raise RegistryInferenceFailure(
"OnDemandFeatureView",
f"Could not infer Features for the feature view '{self.name}'.",
)
[docs] @staticmethod
def get_requested_odfvs(feature_refs, project, registry):
all_on_demand_feature_views = registry.list_on_demand_feature_views(
project, allow_cache=True
)
requested_on_demand_feature_views: List[OnDemandFeatureView] = []
for odfv in all_on_demand_feature_views:
for feature in odfv.features:
if f"{odfv.name}:{feature.name}" in feature_refs:
requested_on_demand_feature_views.append(odfv)
break
return requested_on_demand_feature_views
[docs]def on_demand_feature_view(
*,
schema: List[Field],
sources: List[
Union[
FeatureView,
RequestSource,
FeatureViewProjection,
]
],
description: str = "",
tags: Optional[Dict[str, str]] = None,
owner: str = "",
):
"""
Creates an OnDemandFeatureView object with the given user function as udf.
Args:
schema: The list of features in the output of the on demand feature view, after
the transformation has been applied.
sources: A map from input source names to the actual input sources, which may be
feature views, or request data sources. These sources serve as inputs to the udf,
which will refer to them by name.
description (optional): A human-readable description.
tags (optional): A dictionary of key-value pairs to store arbitrary metadata.
owner (optional): The owner of the on demand feature view, typically the email
of the primary maintainer.
"""
def mainify(obj):
# Needed to allow dill to properly serialize the udf. Otherwise, clients will need to have a file with the same
# name as the original file defining the ODFV.
if obj.__module__ != "__main__":
obj.__module__ = "__main__"
def decorator(user_function):
udf_string = dill.source.getsource(user_function)
mainify(user_function)
on_demand_feature_view_obj = OnDemandFeatureView(
name=user_function.__name__,
sources=sources,
schema=schema,
udf=user_function,
description=description,
tags=tags,
owner=owner,
udf_string=udf_string,
)
functools.update_wrapper(
wrapper=on_demand_feature_view_obj, wrapped=user_function
)
return on_demand_feature_view_obj
return decorator
[docs]def feature_view_to_batch_feature_view(fv: FeatureView) -> BatchFeatureView:
bfv = BatchFeatureView(
name=fv.name,
entities=fv.entities,
ttl=fv.ttl,
tags=fv.tags,
online=fv.online,
owner=fv.owner,
schema=fv.schema,
source=fv.batch_source,
)
bfv.features = copy.copy(fv.features)
bfv.entities = copy.copy(fv.entities)
return bfv