def _python_value_to_proto_value(feast_value_type, value) -> ProtoValue:
"""
Converts a Python (native, pandas) value to a Feast Proto Value based
on a provided value type
Args:
feast_value_type: The target value type
value: Value that will be converted
Returns:
Feast Value Proto
"""
# Detect list type and handle separately
if "list" in feast_value_type.name.lower():
if feast_value_type == ValueType.FLOAT_LIST:
return ProtoValue(float_list_val=FloatList(val=[
item if type(item) in
[np.float32, np.float64] else _type_err(item, np.float32)
for item in value
]))
if feast_value_type == ValueType.DOUBLE_LIST:
return ProtoValue(double_list_val=DoubleList(val=[
item if type(item) in
[np.float64, np.float32] else _type_err(item, np.float64)
for item in value
]))
if feast_value_type == ValueType.INT32_LIST:
return ProtoValue(int32_list_val=Int32List(val=[
item if type(item) is np.int32 else _type_err(item, np.int32)
for item in value
]))
if feast_value_type == ValueType.INT64_LIST:
return ProtoValue(int64_list_val=Int64List(val=[
item if type(item) in
[np.int64, np.int32] else _type_err(item, np.int64)
for item in value
]))
if feast_value_type == ValueType.STRING_LIST:
return ProtoValue(string_list_val=StringList(val=[
item if type(item) in
[np.str_, str] else _type_err(item, np.str_) for item in value
]))
if feast_value_type == ValueType.BOOL_LIST:
return ProtoValue(bool_list_val=BoolList(val=[
item if type(item) in
[np.bool_, bool] else _type_err(item, np.bool_)
for item in value
]))
if feast_value_type == ValueType.BYTES_LIST:
return ProtoValue(bytes_list_val=BytesList(val=[
item if type(item) in
[np.bytes_, bytes] else _type_err(item, np.bytes_)
for item in value
]))
# Handle scalar types below
else:
if pd.isnull(value):
return ProtoValue()
elif feast_value_type == ValueType.INT32:
return ProtoValue(int32_val=int(value))
elif feast_value_type == ValueType.INT64:
return ProtoValue(int64_val=int(value))
elif feast_value_type == ValueType.FLOAT:
return ProtoValue(float_val=float(value))
elif feast_value_type == ValueType.DOUBLE:
assert type(value) is float or np.float64
return ProtoValue(double_val=value)
elif feast_value_type == ValueType.STRING:
return ProtoValue(string_val=str(value))
elif feast_value_type == ValueType.BYTES:
assert type(value) is bytes
return ProtoValue(bytes_val=value)
elif feast_value_type == ValueType.BOOL:
assert type(value) is bool
return ProtoValue(bool_val=value)
raise Exception(f"Unsupported data type: ${str(type(value))}")
def test_basic_retrieve_online_entity_listform(client, list_entity_dataframe):
# Case 1: Features retrieval with entity in list format check
district_fs = FeatureSet(
name="district",
features=[
Feature(name="district_rating", dtype=ValueType.INT64),
Feature(name="district_cost", dtype=ValueType.FLOAT),
Feature(name="district_past_transactions_int",
dtype=ValueType.INT64_LIST),
Feature(name="district_past_transactions_double",
dtype=ValueType.DOUBLE_LIST),
Feature(name="district_past_transactions_float",
dtype=ValueType.FLOAT_LIST),
Feature(name="district_past_transactions_string",
dtype=ValueType.STRING_LIST),
Feature(name="district_past_transactions_bool",
dtype=ValueType.BOOL_LIST),
],
entities=[Entity("district_ids", dtype=ValueType.INT64_LIST)],
max_age=Duration(seconds=3600),
)
client.set_project(PROJECT_NAME)
client.apply(district_fs)
district_fs = client.get_feature_set(name="district")
client.ingest(district_fs, list_entity_dataframe, timeout=600)
time.sleep(15)
online_request_entity = [{
"district_ids": [np.int64(1), np.int64(2),
np.int64(3)]
}]
online_request_features = [
"district_rating",
"district_cost",
"district_past_transactions_int",
"district_past_transactions_double",
"district_past_transactions_float",
"district_past_transactions_string",
"district_past_transactions_bool",
]
online_request_entity2 = [{
"district_ids":
Value(int64_list_val=Int64List(val=[1, 2, 3]))
}]
def try_get_features1():
response = client.get_online_features(
entity_rows=online_request_entity,
feature_refs=online_request_features)
return response, True
def try_get_features2():
response = client.get_online_features(
entity_rows=online_request_entity2,
feature_refs=online_request_features)
return response, True
online_features_actual = wait_retry_backoff(
retry_fn=try_get_features1,
timeout_secs=90,
timeout_msg="Timed out trying to get online feature values",
)
online_features_actual2 = wait_retry_backoff(
retry_fn=try_get_features2,
timeout_secs=90,
timeout_msg="Timed out trying to get online feature values",
)
online_features_expected = {
"district_ids": [[np.int64(1), np.int64(2),
np.int64(3)]],
"district_rating": [1],
"district_cost": [1.5],
"district_past_transactions_int": [[1, 3]],
"district_past_transactions_double": [[1.5, 3.0]],
"district_past_transactions_float": [[1.5, 3.0]],
"district_past_transactions_string": [["first_1", "second_1"]],
"district_past_transactions_bool": [[True, False]],
}
assert online_features_actual.to_dict() == online_features_expected
assert online_features_actual2.to_dict() == online_features_expected
# Case 2: Features retrieval with entity in list format check with mixed types
with pytest.raises(ValueError) as excinfo:
online_request_entity2 = [{
"district_ids": [np.int64(1), np.int64(2), True]
}]
online_features_actual2 = client.get_online_features(
entity_rows=online_request_entity2,
feature_refs=online_request_features)
assert (
"List value type for field district_ids is inconsistent. ValueType.INT64 different from ValueType.BOOL."
in str(excinfo.value))
def pd_value_to_proto_value(feast_value_type, value) -> ProtoValue:
# Detect list type and handle separately
if "list" in feast_value_type.name.lower():
if feast_value_type == ValueType.FLOAT_LIST:
return ProtoValue(float_list_val=FloatList(val=[
item if type(item) is np.float32 else type_err(
item, np.float32) for item in value
]))
if feast_value_type == ValueType.DOUBLE_LIST:
return ProtoValue(double_list_val=DoubleList(val=[
item if type(item) is np.float64 else type_err(
item, np.float64) for item in value
]))
if feast_value_type == ValueType.INT32_LIST:
return ProtoValue(int32_list_val=Int32List(val=[
item if type(item) is np.int32 else type_err(item, np.int32)
for item in value
]))
if feast_value_type == ValueType.INT64_LIST:
return ProtoValue(int64_list_val=Int64List(val=[
item if type(item) is np.int64 else type_err(item, np.int64)
for item in value
]))
if feast_value_type == ValueType.STRING_LIST:
return ProtoValue(string_list_val=StringList(val=[
item if type(item) is np.str_ else type_err(item, np.str_)
for item in value
]))
if feast_value_type == ValueType.BOOL_LIST:
return ProtoValue(bool_list_val=BoolList(val=[
item if type(item) is np.bool_ else type_err(item, np.bool_)
for item in value
]))
if feast_value_type == ValueType.BYTES_LIST:
return ProtoValue(bytes_list_val=BytesList(val=[
item if type(item) is np.bytes_ else type_err(item, np.bytes_)
for item in value
]))
# Handle scalar types below
else:
if pd.isnull(value):
return ProtoValue()
elif feast_value_type == ValueType.INT32:
return ProtoValue(int32_val=int(value))
elif feast_value_type == ValueType.INT64:
return ProtoValue(int64_val=int(value))
elif feast_value_type == ValueType.FLOAT:
return ProtoValue(float_val=float(value))
elif feast_value_type == ValueType.DOUBLE:
assert type(value) is float
return ProtoValue(double_val=value)
elif feast_value_type == ValueType.STRING:
return ProtoValue(string_val=str(value))
elif feast_value_type == ValueType.BYTES:
assert type(value) is bytes
return ProtoValue(bytes_val=value)
elif feast_value_type == ValueType.BOOL:
assert type(value) is bool
return ProtoValue(bool_val=value)
raise Exception(f"Unsupported data type: ${str(type(value))}")