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

Inherits:
Struct
  • Object
show all
Defined in:
gems/aws-sdk-forecastservice/lib/aws-sdk-forecastservice/types.rb

Overview

Note:

When making an API call, you may pass CreatePredictorRequest data as a hash:

{
  predictor_name: "Name", # required
  algorithm_arn: "Arn",
  forecast_horizon: 1, # required
  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
  },
}

Instance Attribute Summary collapse

Instance Attribute Details

#algorithm_arnString

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

Supported algorithms

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

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

    - supports hyperparameter optimization (HPO)

  • arn:aws:forecast:::algorithm/ETS

  • arn:aws:forecast:::algorithm/NPTS

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

Returns:

  • (String)


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# File 'gems/aws-sdk-forecastservice/lib/aws-sdk-forecastservice/types.rb', line 576

class CreatePredictorRequest < Struct.new(
  :predictor_name,
  :algorithm_arn,
  :forecast_horizon,
  :perform_auto_ml,
  :perform_hpo,
  :training_parameters,
  :evaluation_parameters,
  :hpo_config,
  :input_data_config,
  :featurization_config,
  :encryption_config)
  include Aws::Structure
end

#encryption_configTypes::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.



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# File 'gems/aws-sdk-forecastservice/lib/aws-sdk-forecastservice/types.rb', line 576

class CreatePredictorRequest < Struct.new(
  :predictor_name,
  :algorithm_arn,
  :forecast_horizon,
  :perform_auto_ml,
  :perform_hpo,
  :training_parameters,
  :evaluation_parameters,
  :hpo_config,
  :input_data_config,
  :featurization_config,
  :encryption_config)
  include Aws::Structure
end

#evaluation_parametersTypes::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.



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# File 'gems/aws-sdk-forecastservice/lib/aws-sdk-forecastservice/types.rb', line 576

class CreatePredictorRequest < Struct.new(
  :predictor_name,
  :algorithm_arn,
  :forecast_horizon,
  :perform_auto_ml,
  :perform_hpo,
  :training_parameters,
  :evaluation_parameters,
  :hpo_config,
  :input_data_config,
  :featurization_config,
  :encryption_config)
  include Aws::Structure
end

#featurization_configTypes::FeaturizationConfig

The featurization configuration.



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# File 'gems/aws-sdk-forecastservice/lib/aws-sdk-forecastservice/types.rb', line 576

class CreatePredictorRequest < Struct.new(
  :predictor_name,
  :algorithm_arn,
  :forecast_horizon,
  :perform_auto_ml,
  :perform_hpo,
  :training_parameters,
  :evaluation_parameters,
  :hpo_config,
  :input_data_config,
  :featurization_config,
  :encryption_config)
  include Aws::Structure
end

#forecast_horizonInteger

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.

Returns:

  • (Integer)


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# File 'gems/aws-sdk-forecastservice/lib/aws-sdk-forecastservice/types.rb', line 576

class CreatePredictorRequest < Struct.new(
  :predictor_name,
  :algorithm_arn,
  :forecast_horizon,
  :perform_auto_ml,
  :perform_hpo,
  :training_parameters,
  :evaluation_parameters,
  :hpo_config,
  :input_data_config,
  :featurization_config,
  :encryption_config)
  include Aws::Structure
end

#hpo_configTypes::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.



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# File 'gems/aws-sdk-forecastservice/lib/aws-sdk-forecastservice/types.rb', line 576

class CreatePredictorRequest < Struct.new(
  :predictor_name,
  :algorithm_arn,
  :forecast_horizon,
  :perform_auto_ml,
  :perform_hpo,
  :training_parameters,
  :evaluation_parameters,
  :hpo_config,
  :input_data_config,
  :featurization_config,
  :encryption_config)
  include Aws::Structure
end

#input_data_configTypes::InputDataConfig

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



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# File 'gems/aws-sdk-forecastservice/lib/aws-sdk-forecastservice/types.rb', line 576

class CreatePredictorRequest < Struct.new(
  :predictor_name,
  :algorithm_arn,
  :forecast_horizon,
  :perform_auto_ml,
  :perform_hpo,
  :training_parameters,
  :evaluation_parameters,
  :hpo_config,
  :input_data_config,
  :featurization_config,
  :encryption_config)
  include Aws::Structure
end

#perform_auto_mlBoolean

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

If you want Amazon Forecast to evaluate the algorithms it provides and choose the best algorithm and configuration for your training dataset, set PerformAutoML to true. This is a good option if you aren't sure which algorithm is suitable for your application.

Returns:

  • (Boolean)


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# File 'gems/aws-sdk-forecastservice/lib/aws-sdk-forecastservice/types.rb', line 576

class CreatePredictorRequest < Struct.new(
  :predictor_name,
  :algorithm_arn,
  :forecast_horizon,
  :perform_auto_ml,
  :perform_hpo,
  :training_parameters,
  :evaluation_parameters,
  :hpo_config,
  :input_data_config,
  :featurization_config,
  :encryption_config)
  include Aws::Structure
end

#perform_hpoBoolean

Whether to perform hyperparameter optimization (HPO). HPO finds optimal hyperparameter values for your training data. The process of performing HPO is known as 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 supply the HyperParameterTuningJobConfig object. The tuning job specifies an objective metric, the hyperparameters to optimize, and the valid range for each hyperparameter.

The following algorithms support HPO:

  • DeepAR+

^

Returns:

  • (Boolean)


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# File 'gems/aws-sdk-forecastservice/lib/aws-sdk-forecastservice/types.rb', line 576

class CreatePredictorRequest < Struct.new(
  :predictor_name,
  :algorithm_arn,
  :forecast_horizon,
  :perform_auto_ml,
  :perform_hpo,
  :training_parameters,
  :evaluation_parameters,
  :hpo_config,
  :input_data_config,
  :featurization_config,
  :encryption_config)
  include Aws::Structure
end

#predictor_nameString

A name for the predictor.

Returns:

  • (String)


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# File 'gems/aws-sdk-forecastservice/lib/aws-sdk-forecastservice/types.rb', line 576

class CreatePredictorRequest < Struct.new(
  :predictor_name,
  :algorithm_arn,
  :forecast_horizon,
  :perform_auto_ml,
  :perform_hpo,
  :training_parameters,
  :evaluation_parameters,
  :hpo_config,
  :input_data_config,
  :featurization_config,
  :encryption_config)
  include Aws::Structure
end

#training_parametersHash<String,String>

The training parameters to override for model training. The parameters that you can override are listed in the individual algorithms in aws-forecast-choosing-recipes.

Returns:

  • (Hash<String,String>)


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# File 'gems/aws-sdk-forecastservice/lib/aws-sdk-forecastservice/types.rb', line 576

class CreatePredictorRequest < Struct.new(
  :predictor_name,
  :algorithm_arn,
  :forecast_horizon,
  :perform_auto_ml,
  :perform_hpo,
  :training_parameters,
  :evaluation_parameters,
  :hpo_config,
  :input_data_config,
  :featurization_config,
  :encryption_config)
  include Aws::Structure
end