paws.machine.learning: personalize_create_solution – R documentation

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paws.machine.learning

personalize_create_solution

Creates the configuration for training a model

Description

Creates the configuration for training a model. A trained model is known as a solution. After the configuration is created, you train the model (create a solution) by calling the create_solution_version operation. Every time you call create_solution_version, a new version of the solution is created.

After creating a solution version, you check its accuracy by calling get_solution_metrics. When you are satisfied with the version, you deploy it using create_campaign. The campaign provides recommendations to a client through the GetRecommendations API.

To train a model, Amazon Personalize requires training data and a recipe. The training data comes from the dataset group that you provide in the request. A recipe specifies the training algorithm and a feature transformation. You can specify one of the predefined recipes provided by Amazon Personalize. Alternatively, you can specify performAutoML and Amazon Personalize will analyze your data and select the optimum USER_PERSONALIZATION recipe for you.

Status

A solution can be in one of the following states:

CREATE PENDING > CREATE IN_PROGRESS > ACTIVE -or- CREATE FAILED
DELETE PENDING > DELETE IN_PROGRESS

To get the status of the solution, call describe_solution. Wait until the status shows as ACTIVE before calling create_solution_version.

Related APIs

Usage

personalize_create_solution(name, performHPO, performAutoML, recipeArn,
  datasetGroupArn, eventType, solutionConfig)

Arguments

`name`	[required] The name for the solution.
`performHPO`	Whether to perform hyperparameter optimization (HPO) on the specified or selected recipe. The default is `false`. When performing AutoML, this parameter is always `true` and you should not set it to `false`.
`performAutoML`	Whether to perform automated machine learning (AutoML). The default is `false`. For this case, you must specify `recipeArn`. When set to `true`, Amazon Personalize analyzes your training data and selects the optimal USER_PERSONALIZATION recipe and hyperparameters. In this case, you must omit `recipeArn`. Amazon Personalize determines the optimal recipe by running tests with different values for the hyperparameters. AutoML lengthens the training process as compared to selecting a specific recipe.
`recipeArn`	The ARN of the recipe to use for model training. Only specified when `performAutoML` is false.
`datasetGroupArn`	[required] The Amazon Resource Name (ARN) of the dataset group that provides the training data.
`eventType`	When your have multiple event types (using an `EVENT_TYPE` schema field), this parameter specifies which event type (for example, 'click' or 'like') is used for training the model.
`solutionConfig`	The configuration to use with the solution. When `performAutoML` is set to true, Amazon Personalize only evaluates the `autoMLConfig` section of the solution configuration.

Value

A list with the following syntax:

list(
  solutionArn = "string"
)

Request syntax

svc$create_solution(
  name = "string",
  performHPO = TRUE|FALSE,
  performAutoML = TRUE|FALSE,
  recipeArn = "string",
  datasetGroupArn = "string",
  eventType = "string",
  solutionConfig = list(
    eventValueThreshold = "string",
    hpoConfig = list(
      hpoObjective = list(
        type = "string",
        metricName = "string",
        metricRegex = "string"
      ),
      hpoResourceConfig = list(
        maxNumberOfTrainingJobs = "string",
        maxParallelTrainingJobs = "string"
      ),
      algorithmHyperParameterRanges = list(
        integerHyperParameterRanges = list(
          list(
            name = "string",
            minValue = 123,
            maxValue = 123
          )
        ),
        continuousHyperParameterRanges = list(
          list(
            name = "string",
            minValue = 123.0,
            maxValue = 123.0
          )
        ),
        categoricalHyperParameterRanges = list(
          list(
            name = "string",
            values = list(
              "string"
            )
          )
        )
      )
    ),
    algorithmHyperParameters = list(
      "string"
    ),
    featureTransformationParameters = list(
      "string"
    ),
    autoMLConfig = list(
      metricName = "string",
      recipeList = list(
        "string"
      )
    )
  )
)

paws.machine.learning

Amazon Web Services Machine Learning Services

v0.1.11

Apache License (>= 2.0)

Authors

David Kretch [aut, cre], Adam Banker [aut], Amazon.com, Inc. [cph]

Initial release

personalize_create_solution

Description

Usage

Arguments

Value

Request syntax

paws.machine.learning

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