def test_list_features(self, test_client, mocker):
mocker.patch.object(
test_client,
"_core_service_stub",
return_value=Core.CoreServiceStub(grpc.insecure_channel("")),
)
feature1_proto = FeatureSpecProto(
name="feature_1", value_type=ValueProto.ValueType.FLOAT)
feature2_proto = FeatureSpecProto(
name="feature_2", value_type=ValueProto.ValueType.STRING)
mocker.patch.object(
test_client._core_service_stub,
"ListFeatures",
return_value=ListFeaturesResponse(
features={
"driver_car:feature_1": feature1_proto,
"driver_car:feature_2": feature2_proto,
}),
)
features = test_client.list_features_by_ref(project="test")
assert len(features) == 2
native_feature_list = []
for _, feature_proto in features.items():
native_feature_list.append(feature_proto)
assert sorted(native_feature_list) == sorted([
Feature.from_proto(feature1_proto),
Feature.from_proto(feature2_proto)
])
def from_proto(cls, feature_set_proto: FeatureSetProto):
"""
Creates a feature set from a protobuf representation of a feature set
Args:
feature_set_proto: A protobuf representation of a feature set
Returns:
Returns a FeatureSet object based on the feature set protobuf
"""
feature_set = cls(
name=feature_set_proto.spec.name,
features=[
Feature.from_proto(feature)
for feature in feature_set_proto.spec.features
],
entities=[
Entity.from_proto(entity)
for entity in feature_set_proto.spec.entities
],
max_age=feature_set_proto.spec.max_age,
source=(None if feature_set_proto.spec.source.type == 0 else
Source.from_proto(feature_set_proto.spec.source)),
project=feature_set_proto.spec.project
if len(feature_set_proto.spec.project) == 0 else
feature_set_proto.spec.project,
)
feature_set._version = feature_set_proto.spec.version
feature_set._status = feature_set_proto.meta.status
feature_set._created_timestamp = feature_set_proto.meta.created_timestamp
return feature_set
def from_proto(cls, feature_set_proto: FeatureSetSpecProto):
"""
Creates a feature set from a protobuf representation of a feature set
Args:
from_proto: A protobuf representation of a feature set
Returns:
Returns a FeatureSet object based on the feature set protobuf
"""
feature_set = cls(
name=feature_set_proto.name,
features=[
Feature.from_proto(feature) for feature in feature_set_proto.features
],
entities=[
Entity.from_proto(entity) for entity in feature_set_proto.entities
],
max_age=feature_set_proto.max_age,
source=(
None
if feature_set_proto.source.type == 0
else Source.from_proto(feature_set_proto.source)
),
)
feature_set._version = feature_set_proto.version
feature_set._is_dirty = False
return feature_set
def from_proto(cls, feature_table_proto: FeatureTableProto):
"""
Creates a feature table from a protobuf representation of a feature table
Args:
feature_table_proto: A protobuf representation of a feature table
Returns:
Returns a FeatureTableProto object based on the feature table protobuf
"""
feature_table = cls(
name=feature_table_proto.spec.name,
entities=[entity for entity in feature_table_proto.spec.entities],
features=[
Feature.from_proto(feature)
for feature in feature_table_proto.spec.features
],
labels=feature_table_proto.spec.labels,
max_age=(None if feature_table_proto.spec.max_age.seconds == 0
and feature_table_proto.spec.max_age.nanos == 0 else
feature_table_proto.spec.max_age),
batch_source=DataSource.from_proto(
feature_table_proto.spec.batch_source),
stream_source=(
None
if not feature_table_proto.spec.stream_source.ByteSize() else
DataSource.from_proto(feature_table_proto.spec.stream_source)),
)
feature_table._created_timestamp = feature_table_proto.meta.created_timestamp
return feature_table
def from_proto(proto: FeatureViewProjectionProto):
ref = FeatureViewProjection(
name=proto.feature_view_name,
name_alias=proto.feature_view_name_alias,
features=[],
join_key_map=dict(proto.join_key_map),
)
for feature_column in proto.feature_columns:
ref.features.append(Feature.from_proto(feature_column))
return ref
def list_features_by_ref(
self,
project: str = None,
entities: List[str] = list(),
labels: Dict[str, str] = dict(),
) -> Dict[FeatureRef, Feature]:
"""
Retrieve a dictionary of feature reference to feature from Feast Core based on filters provided.
Args:
project: Feast project that these features belongs to
entities: Feast entity that these features are associated with
labels: Feast labels that these features are associated with
Returns:
Dictionary of <feature references: features>
Examples:
>>> from feast import Client
>>>
>>> feast_client = Client(core_url="localhost:6565")
>>> features = feast_client.list_features(project="test_project", entities=["driver_id"], labels={"key1":"val1","key2":"val2"})
>>> print(features)
"""
if self._use_object_store_registry:
raise NotImplementedError(
"This function is not implemented for object store registry.")
else:
if project is None:
project = self.project
filter = ListFeaturesRequest.Filter(project=project,
entities=entities,
labels=labels)
feature_protos = self._core_service.ListFeatures(
ListFeaturesRequest(filter=filter),
metadata=self._get_grpc_metadata(),
) # type: ListFeaturesResponse
# Extract features and return
features_dict = {}
for ref_str, feature_proto in feature_protos.features.items():
feature_ref = FeatureRef.from_str(ref_str)
feature = Feature.from_proto(feature_proto)
features_dict[feature_ref] = feature
return features_dict
def list_features_by_ref(
self,
project: str = None,
entities: List[str] = list(),
labels: Dict[str, str] = dict(),
) -> Dict[FeatureRef, Feature]:
"""
Returns a list of features based on filters provided.
Args:
project: Feast project that these features belongs to
entities: Feast entity that these features are associated with
labels: Feast labels that these features are associated with
Returns:
Dictionary of <feature references: features>
Examples:
>>> from feast import Client
>>>
>>> feast_client = Client(core_url="localhost:6565")
>>> features = list_features_by_ref(project="test_project", entities=["driver_id"], labels={"key1":"val1","key2":"val2"})
>>> print(features)
"""
self._connect_core()
if project is None:
if self.project is not None:
project = self.project
else:
project = "default"
filter = ListFeaturesRequest.Filter(project=project,
entities=entities,
labels=labels)
feature_protos = self._core_service_stub.ListFeatures(
ListFeaturesRequest(filter=filter)) # type: ListFeaturesResponse
features_dict = {}
for ref_str, feature_proto in feature_protos.features.items():
feature_ref = FeatureRef.from_str(ref_str, ignore_project=True)
feature = Feature.from_proto(feature_proto)
features_dict[feature_ref] = feature
return features_dict
def from_proto(cls, feature_set_proto: FeatureSetSpecProto):
feature_set = cls(
name=feature_set_proto.name,
features=[
Feature.from_proto(feature) for feature in feature_set_proto.features
],
entities=[
Entity.from_proto(entity) for entity in feature_set_proto.entities
],
max_age=feature_set_proto.max_age,
source=(
None
if feature_set_proto.source.type == 0
else Source.from_proto(feature_set_proto.source)
),
)
feature_set._version = feature_set_proto.version
feature_set._is_dirty = False
return feature_set
def from_proto(proto: FeatureViewProjectionProto):
ref = FeatureViewProjection(name=proto.feature_view_name, features=[])
for feature_column in proto.feature_columns:
ref.features.append(Feature.from_proto(feature_column))
return ref
