def test_deserializer(): array_data = [[1.0, 2.0, 3.0], [10.0, 20.0, 30.0]] s = numpy_to_record_serializer() buf = s(np.array(array_data)) d = record_deserializer() for record, expected in zip(d(buf, 'who cares'), array_data): assert record.features["values"].float64_tensor.values == expected
def __init__(self, endpoint, sagemaker_session=None): super(LDAPredictor, self).__init__( endpoint, sagemaker_session, serializer=numpy_to_record_serializer(), deserializer=record_deserializer(), )
def __init__(self, endpoint, sagemaker_session=None): """ Args: endpoint: sagemaker_session: """ super(LinearLearnerPredictor, self).__init__( endpoint, sagemaker_session, serializer=numpy_to_record_serializer(), deserializer=record_deserializer(), )
def __init__(self, endpoint, sagemaker_session=None): """ Args: endpoint: sagemaker_session: """ super(FactorizationMachinesPredictor, self).__init__( endpoint, sagemaker_session, serializer=numpy_to_record_serializer(), deserializer=record_deserializer(), )
def __init__(self, endpoint, sagemaker_session=None): super(LinearLearnerPredictor, self).__init__(endpoint, sagemaker_session, serializer=numpy_to_record_serializer(), deserializer=record_deserializer())
def __init__(self, endpoint, sagemaker_session=None): super(FactorizationMachinesPredictor, self).__init__(endpoint, sagemaker_session, serializer=numpy_to_record_serializer(), deserializer=record_deserializer())
def __init__(self, endpoint, sagemaker_session=None): super(RandomCutForestPredictor, self).__init__(endpoint, sagemaker_session, serializer=numpy_to_record_serializer(), deserializer=record_deserializer())