def test_deserializer():
array_data = [[1.0, 2.0, 3.0], [10.0, 20.0, 30.0]]
s = RecordSerializer()
buf = s.serialize(np.array(array_data))
d = RecordDeserializer()
for record, expected in zip(d.deserialize(buf, "who cares"), array_data):
assert record.features["values"].float64_tensor.values == expected
def __init__(
self,
endpoint_name,
sagemaker_session=None,
serializer=RecordSerializer(),
deserializer=RecordDeserializer(),
):
"""Initialization for LinearLearnerPredictor.
Args:
endpoint_name (str): Name of the Amazon SageMaker endpoint to which
requests are sent.
sagemaker_session (sagemaker.session.Session): A SageMaker Session
object, used for SageMaker interactions (default: None). If not
specified, one is created using the default AWS configuration
chain.
serializer (sagemaker.serializers.BaseSerializer): Optional. Default
serializes input data to x-recordio-protobuf format.
deserializer (sagemaker.deserializers.BaseDeserializer): Optional.
Default parses responses from x-recordio-protobuf format.
"""
super(LinearLearnerPredictor, self).__init__(
endpoint_name,
sagemaker_session,
serializer=serializer,
deserializer=deserializer,
)
def __init__(self, endpoint_name, sagemaker_session=None):
"""
Args:
endpoint_name (str): Name of the Amazon SageMaker endpoint to which
requests are sent.
sagemaker_session (sagemaker.session.Session): A SageMaker Session
object, used for SageMaker interactions (default: None). If not
specified, one is created using the default AWS configuration
chain.
"""
super(FactorizationMachinesPredictor, self).__init__(
endpoint_name,
sagemaker_session,
serializer=RecordSerializer(),
deserializer=RecordDeserializer(),
)