This operation is only valid for legacy predictors created with CreatePredictor¶

Description¶

This operation is only valid for legacy predictors created with CreatePredictor. If you are not using a legacy predictor, use describe_auto_predictor.

Describes a predictor created using the create_predictor operation.

In addition to listing the properties provided in the create_predictor request, this operation lists the following properties:

DatasetImportJobArns - The dataset import jobs used to import training data.
AutoMLAlgorithmArns - If AutoML is performed, the algorithms that were evaluated.
CreationTime
LastModificationTime
Status
Message - If an error occurred, information about the error.

Usage¶

forecastservice_describe_predictor(PredictorArn)

Arguments¶

PredictorArn

[required] The Amazon Resource Name (ARN) of the predictor that you want information about.

Value¶

A list with the following syntax:

list(
  PredictorArn = "string",
  PredictorName = "string",
  AlgorithmArn = "string",
  AutoMLAlgorithmArns = list(
    "string"
  ),
  ForecastHorizon = 123,
  ForecastTypes = list(
    "string"
  ),
  PerformAutoML = TRUE|FALSE,
  AutoMLOverrideStrategy = "LatencyOptimized"|"AccuracyOptimized",
  PerformHPO = TRUE|FALSE,
  TrainingParameters = list(
    "string"
  ),
  EvaluationParameters = list(
    NumberOfBacktestWindows = 123,
    BackTestWindowOffset = 123
  ),
  HPOConfig = list(
    ParameterRanges = list(
      CategoricalParameterRanges = list(
        list(
          Name = "string",
          Values = list(
            "string"
          )
        )
      ),
      ContinuousParameterRanges = list(
        list(
          Name = "string",
          MaxValue = 123.0,
          MinValue = 123.0,
          ScalingType = "Auto"|"Linear"|"Logarithmic"|"ReverseLogarithmic"
        )
      ),
      IntegerParameterRanges = list(
        list(
          Name = "string",
          MaxValue = 123,
          MinValue = 123,
          ScalingType = "Auto"|"Linear"|"Logarithmic"|"ReverseLogarithmic"
        )
      )
    )
  ),
  InputDataConfig = list(
    DatasetGroupArn = "string",
    SupplementaryFeatures = list(
      list(
        Name = "string",
        Value = "string"
      )
    )
  ),
  FeaturizationConfig = list(
    ForecastFrequency = "string",
    ForecastDimensions = list(
      "string"
    ),
    Featurizations = list(
      list(
        AttributeName = "string",
        FeaturizationPipeline = list(
          list(
            FeaturizationMethodName = "filling",
            FeaturizationMethodParameters = list(
              "string"
            )
          )
        )
      )
    )
  ),
  EncryptionConfig = list(
    RoleArn = "string",
    KMSKeyArn = "string"
  ),
  PredictorExecutionDetails = list(
    PredictorExecutions = list(
      list(
        AlgorithmArn = "string",
        TestWindows = list(
          list(
            TestWindowStart = as.POSIXct(
              "2015-01-01"
            ),
            TestWindowEnd = as.POSIXct(
              "2015-01-01"
            ),
            Status = "string",
            Message = "string"
          )
        )
      )
    )
  ),
  EstimatedTimeRemainingInMinutes = 123,
  IsAutoPredictor = TRUE|FALSE,
  DatasetImportJobArns = list(
    "string"
  ),
  Status = "string",
  Message = "string",
  CreationTime = as.POSIXct(
    "2015-01-01"
  ),
  LastModificationTime = as.POSIXct(
    "2015-01-01"
  ),
  OptimizationMetric = "WAPE"|"RMSE"|"AverageWeightedQuantileLoss"|"MASE"|"MAPE"
)

Request syntax¶

svc$describe_predictor(
  PredictorArn = "string"
)