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Class: Aws::ForecastService::Types::CreatePredictorRequest

Inherits:
Struct
  • Object
show all
Defined in:
(unknown)

Overview

Note:

When passing CreatePredictorRequest as input to an Aws::Client method, you can use a vanilla Hash:

{
  predictor_name: "Name", # required
  algorithm_arn: "Arn",
  forecast_horizon: 1, # required
  forecast_types: ["ForecastType"],
  perform_auto_ml: false,
  perform_hpo: false,
  training_parameters: {
    "ParameterKey" => "ParameterValue",
  },
  evaluation_parameters: {
    number_of_backtest_windows: 1,
    back_test_window_offset: 1,
  },
  hpo_config: {
    parameter_ranges: {
      categorical_parameter_ranges: [
        {
          name: "Name", # required
          values: ["Value"], # required
        },
      ],
      continuous_parameter_ranges: [
        {
          name: "Name", # required
          max_value: 1.0, # required
          min_value: 1.0, # required
          scaling_type: "Auto", # accepts Auto, Linear, Logarithmic, ReverseLogarithmic
        },
      ],
      integer_parameter_ranges: [
        {
          name: "Name", # required
          max_value: 1, # required
          min_value: 1, # required
          scaling_type: "Auto", # accepts Auto, Linear, Logarithmic, ReverseLogarithmic
        },
      ],
    },
  },
  input_data_config: { # required
    dataset_group_arn: "Arn", # required
    supplementary_features: [
      {
        name: "Name", # required
        value: "Value", # required
      },
    ],
  },
  featurization_config: { # required
    forecast_frequency: "Frequency", # required
    forecast_dimensions: ["Name"],
    featurizations: [
      {
        attribute_name: "Name", # required
        featurization_pipeline: [
          {
            featurization_method_name: "filling", # required, accepts filling
            featurization_method_parameters: {
              "ParameterKey" => "ParameterValue",
            },
          },
        ],
      },
    ],
  },
  encryption_config: {
    role_arn: "Arn", # required
    kms_key_arn: "KMSKeyArn", # required
  },
  tags: [
    {
      key: "TagKey", # required
      value: "TagValue", # required
    },
  ],
}

See Also:

Instance Attribute Summary collapse

Instance Attribute Details

#algorithm_arn ⇒ String

The Amazon Resource Name (ARN) of the algorithm to use for model training. Required if PerformAutoML is not set to true.

Supported algorithms: .title

  • arn:aws:forecast:::algorithm/ARIMA

  • arn:aws:forecast:::algorithm/CNN-QR

  • arn:aws:forecast:::algorithm/Deep_AR_Plus

  • arn:aws:forecast:::algorithm/ETS

  • arn:aws:forecast:::algorithm/NPTS

  • arn:aws:forecast:::algorithm/Prophet

Returns:

  • (String) —

    The Amazon Resource Name (ARN) of the algorithm to use for model training.

#encryption_config ⇒ Types::EncryptionConfig

An AWS Key Management Service (KMS) key and the AWS Identity and Access Management (IAM) role that Amazon Forecast can assume to access the key.

Returns:

  • (Types::EncryptionConfig) —

    An AWS Key Management Service (KMS) key and the AWS Identity and Access Management (IAM) role that Amazon Forecast can assume to access the key.

#evaluation_parameters ⇒ Types::EvaluationParameters

Used to override the default evaluation parameters of the specified algorithm. Amazon Forecast evaluates a predictor by splitting a dataset into training data and testing data. The evaluation parameters define how to perform the split and the number of iterations.

Returns:

#featurization_config ⇒ Types::FeaturizationConfig

The featurization configuration.

Returns:

#forecast_horizon ⇒ Integer

Specifies the number of time-steps that the model is trained to predict. The forecast horizon is also called the prediction length.

For example, if you configure a dataset for daily data collection (using the DataFrequency parameter of the CreateDataset operation) and set the forecast horizon to 10, the model returns predictions for 10 days.

The maximum forecast horizon is the lesser of 500 time-steps or 1/3 of the TARGET_TIME_SERIES dataset length.

Returns:

  • (Integer) —

    Specifies the number of time-steps that the model is trained to predict.

#forecast_types ⇒ Array<String>

Specifies the forecast types used to train a predictor. You can specify up to five forecast types. Forecast types can be quantiles from 0.01 to 0.99, by increments of 0.01 or higher. You can also specify the mean forecast with mean.

The default value is ["0.10", "0.50", "0.9"].

Returns:

  • (Array<String>) —

    Specifies the forecast types used to train a predictor.

#hpo_config ⇒ Types::HyperParameterTuningJobConfig

Provides hyperparameter override values for the algorithm. If you don\'t provide this parameter, Amazon Forecast uses default values. The individual algorithms specify which hyperparameters support hyperparameter optimization (HPO). For more information, see aws-forecast-choosing-recipes.

If you included the HPOConfig object, you must set PerformHPO to true.

Returns:

#input_data_config ⇒ Types::InputDataConfig

Describes the dataset group that contains the data to use to train the predictor.

Returns:

  • (Types::InputDataConfig) —

    Describes the dataset group that contains the data to use to train the predictor.

#perform_auto_ml ⇒ Boolean

Whether to perform AutoML. When Amazon Forecast performs AutoML, it evaluates the algorithms it provides and chooses the best algorithm and configuration for your training dataset.

The default value is false. In this case, you are required to specify an algorithm.

Set PerformAutoML to true to have Amazon Forecast perform AutoML. This is a good option if you aren\'t sure which algorithm is suitable for your training data. In this case, PerformHPO must be false.

Returns:

  • (Boolean) —

    Whether to perform AutoML.

#perform_hpo ⇒ Boolean

Whether to perform hyperparameter optimization (HPO). HPO finds optimal hyperparameter values for your training data. The process of performing HPO is known as running a hyperparameter tuning job.

The default value is false. In this case, Amazon Forecast uses default hyperparameter values from the chosen algorithm.

To override the default values, set PerformHPO to true and, optionally, supply the HyperParameterTuningJobConfig object. The tuning job specifies a metric to optimize, which hyperparameters participate in tuning, and the valid range for each tunable hyperparameter. In this case, you are required to specify an algorithm and PerformAutoML must be false.

The following algorithms support HPO:

  • DeepAR+

  • CNN-QR

Returns:

  • (Boolean) —

    Whether to perform hyperparameter optimization (HPO).

#predictor_name ⇒ String

A name for the predictor.

Returns:

  • (String) —

    A name for the predictor.

#tags ⇒ Array<Types::Tag>

The optional metadata that you apply to the predictor to help you categorize and organize them. Each tag consists of a key and an optional value, both of which you define.

The following basic restrictions apply to tags:

  • Maximum number of tags per resource - 50.

  • For each resource, each tag key must be unique, and each tag key can have only one value.

  • Maximum key length - 128 Unicode characters in UTF-8.

  • Maximum value length - 256 Unicode characters in UTF-8.

  • If your tagging schema is used across multiple services and resources, remember that other services may have restrictions on allowed characters. Generally allowed characters are: letters, numbers, and spaces representable in UTF-8, and the following characters: + - = . _ : / @.

  • Tag keys and values are case sensitive.

  • Do not use aws:, AWS:, or any upper or lowercase combination of such as a prefix for keys as it is reserved for AWS use. You cannot edit or delete tag keys with this prefix. Values can have this prefix. If a tag value has aws as its prefix but the key does not, then Forecast considers it to be a user tag and will count against the limit of 50 tags. Tags with only the key prefix of aws do not count against your tags per resource limit.

Returns:

  • (Array<Types::Tag>) —

    The optional metadata that you apply to the predictor to help you categorize and organize them.

#training_parameters ⇒ Hash<String,String>

The hyperparameters to override for model training. The hyperparameters that you can override are listed in the individual algorithms. For the list of supported algorithms, see aws-forecast-choosing-recipes.

Returns:

  • (Hash<String,String>) —

    The hyperparameters to override for model training.