Describe Predictor
forecastservice_describe_predictor | R Documentation |
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¶
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"
)