def __init__()

in sagemaker-pyspark-sdk/src/sagemaker_pyspark/algorithms/FactorizationMachinesSageMakerEstimator.py [0:0]

• 46 lines of code
• 4 McCabe index (conditional complexity)

    def __init__(
            self,
            trainingInstanceType,
            trainingInstanceCount,
            endpointInstanceType,
            endpointInitialInstanceCount,
            sagemakerRole=IAMRoleFromConfig(),
            requestRowSerializer=ProtobufRequestRowSerializer(),
            responseRowDeserializer=FactorizationMachinesBinaryClassifierDeserializer(),
            trainingInputS3DataPath=S3AutoCreatePath(),
            trainingOutputS3DataPath=S3AutoCreatePath(),
            trainingInstanceVolumeSizeInGB=1024,
            trainingProjectedColumns=None,
            trainingChannelName="train",
            trainingContentType=None,
            trainingS3DataDistribution="ShardedByS3Key",
            trainingSparkDataFormat="sagemaker",
            trainingSparkDataFormatOptions=None,
            trainingInputMode="File",
            trainingCompressionCodec=None,
            trainingMaxRuntimeInSeconds=24*60*60,
            trainingKmsKeyId=None,
            modelEnvironmentVariables=None,
            endpointCreationPolicy=EndpointCreationPolicy.CREATE_ON_CONSTRUCT,
            sagemakerClient=SageMakerClients.create_sagemaker_client(),
            region=None,
            s3Client=SageMakerClients.create_s3_default_client(),
            stsClient=SageMakerClients.create_sts_default_client(),
            modelPrependInputRowsToTransformationRows=True,
            deleteStagingDataAfterTraining=True,
            namePolicyFactory=RandomNamePolicyFactory(),
            uid=None,
            javaObject=None):

        if trainingSparkDataFormatOptions is None:
            trainingSparkDataFormatOptions = {}

        if modelEnvironmentVariables is None:
            modelEnvironmentVariables = {}

        if uid is None:
            uid = Identifiable._randomUID()

        kwargs = locals().copy()
        del kwargs['self']

        super(FactorizationMachinesBinaryClassifier, self).__init__(**kwargs)

        default_params = {
            'predictor_type': 'binary_classifier'
        }

        self._setDefault(**default_params)