Source code for feast.feature

# Copyright 2020 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
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# See the License for the specific language governing permissions and
# limitations under the License.

from typing import List, MutableMapping, Optional

from feast.core.Feature_pb2 import FeatureSpecV2 as FeatureSpecProto
from feast.serving.ServingService_pb2 import FeatureReferenceV2 as FeatureRefProto
from feast.types import Value_pb2 as ValueTypeProto
from feast.value_type import ValueType

[docs]class Feature: """Feature field type""" def __init__( self, name: str, dtype: ValueType, labels: Optional[MutableMapping[str, str]] = None, ): self._name = name if not isinstance(dtype, ValueType): raise ValueError("dtype is not a valid ValueType") self._dtype = dtype if labels is None: self._labels = dict() # type: MutableMapping else: self._labels = labels def __eq__(self, other): if ( != or self.dtype != other.dtype or self.labels != other.labels ): return False return True def __lt__(self, other): return < @property def name(self): """ Getter for name of this field """ return self._name @property def dtype(self) -> ValueType: """ Getter for data type of this field """ return self._dtype @property def labels(self) -> MutableMapping[str, str]: """ Getter for labels of this field """ return self._labels
[docs] def to_proto(self) -> FeatureSpecProto: """Converts Feature object to its Protocol Buffer representation""" value_type = ValueTypeProto.ValueType.Enum.Value( return FeatureSpecProto(, value_type=value_type, labels=self.labels, )
[docs] @classmethod def from_proto(cls, feature_proto: FeatureSpecProto): """ Args: feature_proto: FeatureSpecV2 protobuf object Returns: Feature object """ feature = cls(, dtype=ValueType(feature_proto.value_type), labels=feature_proto.labels, ) return feature
[docs]class FeatureRef: """ Feature Reference represents a reference to a specific feature. """ def __init__(self, name: str, feature_table: str = None): self.proto = FeatureRefProto(name=name, feature_table=feature_table)
[docs] @classmethod def from_proto(cls, proto: FeatureRefProto): """ Construct a feature reference from the given FeatureReference proto Arg: proto: Protobuf FeatureReference to construct from Returns: FeatureRef that refers to the given feature """ return cls(, feature_table=proto.feature_table)
[docs] @classmethod def from_str(cls, feature_ref_str: str): """ Parse the given string feature reference into FeatureRef model String feature reference should be in the format feature_table:feature. Where "feature_table" and "name" are the feature_table name and feature name respectively. Args: feature_ref_str: String representation of the feature reference Returns: FeatureRef that refers to the given feature """ proto = FeatureRefProto() # parse feature table name if specified if ":" in feature_ref_str: proto.feature_table, = feature_ref_str.split(":") else: raise ValueError( f"Unsupported feature reference: {feature_ref_str} - Feature reference string should be in the form [featuretable_name:featurename]" ) return cls.from_proto(proto)
[docs] def to_proto(self) -> FeatureRefProto: """ Convert and return this feature table reference to protobuf. Returns: Protobuf respresentation of this feature table reference. """ return self.proto
def __repr__(self): # return string representation of the reference ref_str = self.proto.feature_table + ":" + return ref_str def __str__(self): # readable string of the reference return f"FeatureRef<{self.__repr__()}>"
def _build_feature_references(feature_ref_strs: List[str]) -> List[FeatureRefProto]: """ Builds a list of FeatureReference protos from a list of FeatureReference strings Args: feature_ref_strs: List of string feature references Returns: A list of FeatureReference protos parsed from args. """ feature_refs = [FeatureRef.from_str(ref_str) for ref_str in feature_ref_strs] feature_ref_protos = [ref.to_proto() for ref in feature_refs] return feature_ref_protos