def test_get_feature_set(self, mocked_client, mocker):
mocked_client._core_service_stub = Core.CoreServiceStub(
grpc.insecure_channel("")
)
from google.protobuf.duration_pb2 import Duration
mocker.patch.object(
mocked_client._core_service_stub,
"GetFeatureSet",
return_value=GetFeatureSetResponse(
feature_set=FeatureSetProto(
spec=FeatureSetSpecProto(
name="my_feature_set",
max_age=Duration(seconds=3600),
labels={"key1": "val1", "key2": "val2"},
features=[
FeatureSpecProto(
name="my_feature_1",
value_type=ValueProto.ValueType.FLOAT,
),
FeatureSpecProto(
name="my_feature_2",
value_type=ValueProto.ValueType.FLOAT,
),
],
entities=[
EntitySpecProto(
name="my_entity_1",
value_type=ValueProto.ValueType.INT64,
)
],
source=Source(
type=SourceType.KAFKA,
kafka_source_config=KafkaSourceConfig(
bootstrap_servers="localhost:9092", topic="topic"
),
),
),
meta=FeatureSetMetaProto(),
)
),
)
mocked_client.set_project("my_project")
feature_set = mocked_client.get_feature_set("my_feature_set")
assert (
feature_set.name == "my_feature_set"
and "key1" in feature_set.labels
and feature_set.labels["key1"] == "val1"
and "key2" in feature_set.labels
and feature_set.labels["key2"] == "val2"
and feature_set.fields["my_feature_1"].name == "my_feature_1"
and feature_set.fields["my_feature_1"].dtype == ValueType.FLOAT
and feature_set.fields["my_entity_1"].name == "my_entity_1"
and feature_set.fields["my_entity_1"].dtype == ValueType.INT64
and len(feature_set.features) == 2
and len(feature_set.entities) == 1
)
def test_list_feature_sets(self, mocked_client, mocker):
mocker.patch.object(
mocked_client,
"_core_service_stub",
return_value=Core.CoreServiceStub(grpc.insecure_channel("")),
)
feature_set_1_proto = FeatureSetProto(
spec=FeatureSetSpecProto(
project="test",
name="driver_car",
max_age=Duration(seconds=3600),
labels={"key1": "val1", "key2": "val2"},
features=[
FeatureSpecProto(
name="feature_1", value_type=ValueProto.ValueType.FLOAT
)
],
)
)
feature_set_2_proto = FeatureSetProto(
spec=FeatureSetSpecProto(
project="test",
name="driver_ride",
max_age=Duration(seconds=3600),
labels={"key1": "val1"},
features=[
FeatureSpecProto(
name="feature_1", value_type=ValueProto.ValueType.FLOAT
)
],
)
)
mocker.patch.object(
mocked_client._core_service_stub,
"ListFeatureSets",
return_value=ListFeatureSetsResponse(
feature_sets=[feature_set_1_proto, feature_set_2_proto]
),
)
feature_sets = mocked_client.list_feature_sets(labels={"key1": "val1"})
assert len(feature_sets) == 2
feature_set = feature_sets[0]
assert (
feature_set.name == "driver_car"
and "key1" in feature_set.labels
and feature_set.labels["key1"] == "val1"
and "key2" in feature_set.labels
and feature_set.labels["key2"] == "val2"
and feature_set.fields["feature_1"].name == "feature_1"
and feature_set.fields["feature_1"].dtype == ValueType.FLOAT
and len(feature_set.features) == 1
)
def test_list_features(self, mocked_client, mocker):
mocker.patch.object(
mocked_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(
mocked_client._core_service_stub,
"ListFeatures",
return_value=ListFeaturesResponse(
features={
"driver_car:feature_1": feature1_proto,
"driver_car:feature_2": feature2_proto,
}
),
)
features = mocked_client.list_features_by_ref(project="test")
assert len(features) == 2
ref_str_list = []
feature_name_list = []
feature_dtype_list = []
for ref_str, feature_proto in features.items():
ref_str_list.append(ref_str)
feature_name_list.append(feature_proto.name)
feature_dtype_list.append(feature_proto.dtype)
assert (
set(ref_str_list) == set(["driver_car:feature_1", "driver_car:feature_2"])
and set(feature_name_list) == set(["feature_1", "feature_2"])
and set(feature_dtype_list) == set([ValueType.FLOAT, ValueType.STRING])
)
def test_get_batch_features(self, mocked_client, mocker):
mocked_client._serving_service_stub = Serving.ServingServiceStub(
grpc.insecure_channel(""))
mocked_client._core_service_stub = Core.CoreServiceStub(
grpc.insecure_channel(""))
mocker.patch.object(
mocked_client._core_service_stub,
"GetFeatureSet",
return_value=GetFeatureSetResponse(feature_set=FeatureSetProto(
spec=FeatureSetSpecProto(
name="driver",
project="driver_project",
entities=[
EntitySpecProto(name="driver",
value_type=ValueProto.ValueType.INT64),
EntitySpecProto(
name="transaction",
value_type=ValueProto.ValueType.INT64,
),
],
features=[
FeatureSpecProto(
name="driver_id",
value_type=ValueProto.ValueType.FLOAT,
),
FeatureSpecProto(
name="driver_name",
value_type=ValueProto.ValueType.STRING,
),
],
),
meta=FeatureSetMetaProto(
status=FeatureSetStatusProto.STATUS_READY),
)),
)
expected_dataframe = pd.DataFrame({
"datetime": [datetime.utcnow() for _ in range(3)],
"driver": [1001, 1002, 1003],
"transaction": [1001, 1002, 1003],
"driver_id": [1001, 1002, 1003],
})
final_results = tempfile.mktemp()
pandavro.to_avro(file_path_or_buffer=final_results,
df=expected_dataframe)
mocker.patch.object(
mocked_client._serving_service_stub,
"GetBatchFeatures",
return_value=GetBatchFeaturesResponse(job=BatchRetrievalJob(
id="123",
type=JobType.JOB_TYPE_DOWNLOAD,
status=JobStatus.JOB_STATUS_DONE,
file_uris=[f"file://{final_results}"],
data_format=DataFormat.DATA_FORMAT_AVRO,
)),
)
mocker.patch.object(
mocked_client._serving_service_stub,
"GetJob",
return_value=GetJobResponse(job=BatchRetrievalJob(
id="123",
type=JobType.JOB_TYPE_DOWNLOAD,
status=JobStatus.JOB_STATUS_DONE,
file_uris=[f"file://{final_results}"],
data_format=DataFormat.DATA_FORMAT_AVRO,
)),
)
mocker.patch.object(
mocked_client._serving_service_stub,
"GetFeastServingInfo",
return_value=GetFeastServingInfoResponse(
job_staging_location=f"file://{tempfile.mkdtemp()}/",
type=FeastServingType.FEAST_SERVING_TYPE_BATCH,
),
)
mocked_client.set_project("project1")
# TODO: Abstract away GCS client and GCP dependency
# NOTE: Feast Serving does not allow for feature references
# that specify the same feature in the same request.
with patch("google.cloud.storage.Client"):
response = mocked_client.get_batch_features(
entity_rows=pd.DataFrame({
"datetime": [
pd.datetime.now(tz=timezone("Asia/Singapore"))
for _ in range(3)
],
"driver": [1001, 1002, 1003],
"transaction": [1001, 1002, 1003],
}),
feature_refs=["driver:driver_id", "driver_id"],
project="driver_project",
) # Type: GetBatchFeaturesResponse
assert response.id == "123" and response.status == JobStatus.JOB_STATUS_DONE
actual_dataframe = response.to_dataframe()
assert actual_dataframe[["driver_id"
]].equals(expected_dataframe[["driver_id"]])
def test_get_batch_features(self, mocked_client, mocker):
mocked_client._serving_service_stub = Serving.ServingServiceStub(
grpc.insecure_channel(""))
mocked_client._core_service_stub = Core.CoreServiceStub(
grpc.insecure_channel(""))
mocker.patch.object(
mocked_client._core_service_stub,
"GetFeatureSet",
return_value=GetFeatureSetResponse(feature_set=FeatureSetProto(
spec=FeatureSetSpecProto(
name="customer_fs",
version=1,
project="my_project",
entities=[
EntitySpecProto(name="customer",
value_type=ValueProto.ValueType.INT64),
EntitySpecProto(
name="transaction",
value_type=ValueProto.ValueType.INT64,
),
],
features=[
FeatureSpecProto(
name="customer_feature_1",
value_type=ValueProto.ValueType.FLOAT,
),
FeatureSpecProto(
name="customer_feature_2",
value_type=ValueProto.ValueType.STRING,
),
],
),
meta=FeatureSetMetaProto(
status=FeatureSetStatusProto.STATUS_READY),
)),
)
expected_dataframe = pd.DataFrame({
"datetime": [datetime.utcnow() for _ in range(3)],
"customer": [1001, 1002, 1003],
"transaction": [1001, 1002, 1003],
"my_project/customer_feature_1:1": [1001, 1002, 1003],
"my_project/customer_feature_2:1": [1001, 1002, 1003],
})
final_results = tempfile.mktemp()
to_avro(file_path_or_buffer=final_results, df=expected_dataframe)
mocker.patch.object(
mocked_client._serving_service_stub,
"GetBatchFeatures",
return_value=GetBatchFeaturesResponse(job=BatchFeaturesJob(
id="123",
type=JobType.JOB_TYPE_DOWNLOAD,
status=JobStatus.JOB_STATUS_DONE,
file_uris=[f"file://{final_results}"],
data_format=DataFormat.DATA_FORMAT_AVRO,
)),
)
mocker.patch.object(
mocked_client._serving_service_stub,
"GetJob",
return_value=GetJobResponse(job=BatchFeaturesJob(
id="123",
type=JobType.JOB_TYPE_DOWNLOAD,
status=JobStatus.JOB_STATUS_DONE,
file_uris=[f"file://{final_results}"],
data_format=DataFormat.DATA_FORMAT_AVRO,
)),
)
mocker.patch.object(
mocked_client._serving_service_stub,
"GetFeastServingInfo",
return_value=GetFeastServingInfoResponse(
job_staging_location=f"file://{tempfile.mkdtemp()}/",
type=FeastServingType.FEAST_SERVING_TYPE_BATCH,
),
)
mocked_client.set_project("project1")
response = mocked_client.get_batch_features(
entity_rows=pd.DataFrame({
"datetime": [
pd.datetime.now(tz=timezone("Asia/Singapore"))
for _ in range(3)
],
"customer": [1001, 1002, 1003],
"transaction": [1001, 1002, 1003],
}),
feature_refs=[
"my_project/customer_feature_1:1",
"my_project/customer_feature_2:1",
],
) # type: Job
assert response.id == "123" and response.status == JobStatus.JOB_STATUS_DONE
actual_dataframe = response.to_dataframe()
assert actual_dataframe[[
"my_project/customer_feature_1:1",
"my_project/customer_feature_2:1"
]].equals(expected_dataframe[[
"my_project/customer_feature_1:1",
"my_project/customer_feature_2:1"
]])
