Module aws_sdk_personalize::types

Data structures used by operation inputs/outputs.

Modules

• builders
  Builders
• error
  Error types that Amazon Personalize can respond with.

Structs

• Algorithm

  Describes a custom algorithm.

• AlgorithmImage

  Describes an algorithm image.

• AutoMlConfig

  When the solution performs AutoML (performAutoML is true in CreateSolution), Amazon Personalize determines which recipe, from the specified list, optimizes the given metric. Amazon Personalize then uses that recipe for the solution.

• AutoMlResult

  When the solution performs AutoML (performAutoML is true in CreateSolution), specifies the recipe that best optimized the specified metric.

• AutoTrainingConfig

  The automatic training configuration to use when performAutoTraining is true.

• BatchInferenceJob

  Contains information on a batch inference job.

• BatchInferenceJobConfig

  The configuration details of a batch inference job.

• BatchInferenceJobInput

  The input configuration of a batch inference job.

• BatchInferenceJobOutput

  The output configuration parameters of a batch inference job.

• BatchInferenceJobSummary

  A truncated version of the BatchInferenceJob. The ListBatchInferenceJobs operation returns a list of batch inference job summaries.

• BatchSegmentJob

  Contains information on a batch segment job.

• BatchSegmentJobInput

  The input configuration of a batch segment job.

• BatchSegmentJobOutput

  The output configuration parameters of a batch segment job.

• BatchSegmentJobSummary

  A truncated version of the BatchSegmentJob datatype. ListBatchSegmentJobs operation returns a list of batch segment job summaries.

• Campaign

  An object that describes the deployment of a solution version. For more information on campaigns, see CreateCampaign.

• CampaignConfig

  The configuration details of a campaign.

• CampaignSummary

  Provides a summary of the properties of a campaign. For a complete listing, call the DescribeCampaign API.

• CampaignUpdateSummary

  Provides a summary of the properties of a campaign update. For a complete listing, call the DescribeCampaign API.

• CategoricalHyperParameterRange

  Provides the name and range of a categorical hyperparameter.

• ContinuousHyperParameterRange

  Provides the name and range of a continuous hyperparameter.

• DataDeletionJob

  Describes a job that deletes all references to specific users from an Amazon Personalize dataset group in batches. For information about creating a data deletion job, see Deleting users.

• DataDeletionJobSummary

  Provides a summary of the properties of a data deletion job. For a complete listing, call the DescribeDataDeletionJob API operation.

• DataSource

  Describes the data source that contains the data to upload to a dataset, or the list of records to delete from Amazon Personalize.

• Dataset

  Provides metadata for a dataset.

• DatasetExportJob

  Describes a job that exports a dataset to an Amazon S3 bucket. For more information, see CreateDatasetExportJob.

• DatasetExportJobOutput

  The output configuration parameters of a dataset export job.

• DatasetExportJobSummary

  Provides a summary of the properties of a dataset export job. For a complete listing, call the DescribeDatasetExportJob API.

• DatasetGroup

  A dataset group is a collection of related datasets (Item interactions, Users, Items, Actions, Action interactions). You create a dataset group by calling CreateDatasetGroup. You then create a dataset and add it to a dataset group by calling CreateDataset. The dataset group is used to create and train a solution by calling CreateSolution. A dataset group can contain only one of each type of dataset.

• DatasetGroupSummary

  Provides a summary of the properties of a dataset group. For a complete listing, call the DescribeDatasetGroup API.

• DatasetImportJob

  Describes a job that imports training data from a data source (Amazon S3 bucket) to an Amazon Personalize dataset. For more information, see CreateDatasetImportJob.

• DatasetImportJobSummary

  Provides a summary of the properties of a dataset import job. For a complete listing, call the DescribeDatasetImportJob API.

• DatasetSchema

  Describes the schema for a dataset. For more information on schemas, see CreateSchema.

• DatasetSchemaSummary

  Provides a summary of the properties of a dataset schema. For a complete listing, call the DescribeSchema API.

• DatasetSummary

  Provides a summary of the properties of a dataset. For a complete listing, call the DescribeDataset API.

• DatasetUpdateSummary

  Describes an update to a dataset.

• DefaultCategoricalHyperParameterRange

  Provides the name and default range of a categorical hyperparameter and whether the hyperparameter is tunable. A tunable hyperparameter can have its value determined during hyperparameter optimization (HPO).

• DefaultContinuousHyperParameterRange

  Provides the name and default range of a continuous hyperparameter and whether the hyperparameter is tunable. A tunable hyperparameter can have its value determined during hyperparameter optimization (HPO).

• DefaultHyperParameterRanges

  Specifies the hyperparameters and their default ranges. Hyperparameters can be categorical, continuous, or integer-valued.

• DefaultIntegerHyperParameterRange

  Provides the name and default range of a integer-valued hyperparameter and whether the hyperparameter is tunable. A tunable hyperparameter can have its value determined during hyperparameter optimization (HPO).

• EventTracker

  Provides information about an event tracker.

• EventTrackerSummary

  Provides a summary of the properties of an event tracker. For a complete listing, call the DescribeEventTracker API.

• FeatureTransformation

  Provides feature transformation information. Feature transformation is the process of modifying raw input data into a form more suitable for model training.

• FieldsForThemeGeneration

  A string to string map of the configuration details for theme generation.

• Filter

  Contains information on a recommendation filter, including its ARN, status, and filter expression.

• FilterSummary

  A short summary of a filter's attributes.

• HpoConfig

  Describes the properties for hyperparameter optimization (HPO).

• HpoObjective

  The metric to optimize during hyperparameter optimization (HPO).

• HpoResourceConfig

  Describes the resource configuration for hyperparameter optimization (HPO).

• HyperParameterRanges

  Specifies the hyperparameters and their ranges. Hyperparameters can be categorical, continuous, or integer-valued.

• IntegerHyperParameterRange

  Provides the name and range of an integer-valued hyperparameter.

• MetricAttribute

  Contains information on a metric that a metric attribution reports on. For more information, see Measuring impact of recommendations.

• MetricAttribution

  Contains information on a metric attribution. A metric attribution creates reports on the data that you import into Amazon Personalize. Depending on how you import the data, you can view reports in Amazon CloudWatch or Amazon S3. For more information, see Measuring impact of recommendations.

• MetricAttributionOutput

  The output configuration details for a metric attribution.

• MetricAttributionSummary

  Provides a summary of the properties of a metric attribution. For a complete listing, call the DescribeMetricAttribution.

• OptimizationObjective

  Describes the additional objective for the solution, such as maximizing streaming minutes or increasing revenue. For more information see Optimizing a solution.

• Recipe

  Provides information about a recipe. Each recipe provides an algorithm that Amazon Personalize uses in model training when you use the CreateSolution operation.

• RecipeSummary

  Provides a summary of the properties of a recipe. For a complete listing, call the DescribeRecipe API.

• Recommender

  Describes a recommendation generator for a Domain dataset group. You create a recommender in a Domain dataset group for a specific domain use case (domain recipe), and specify the recommender in a GetRecommendations request.

• RecommenderConfig

  The configuration details of the recommender.

• RecommenderSummary

  Provides a summary of the properties of the recommender.

• RecommenderUpdateSummary

  Provides a summary of the properties of a recommender update. For a complete listing, call the DescribeRecommender API.

• S3DataConfig

  The configuration details of an Amazon S3 input or output bucket.

• Solution
• SolutionConfig

  Describes the configuration properties for the solution.

• SolutionSummary

  Provides a summary of the properties of a solution. For a complete listing, call the DescribeSolution API.

• SolutionVersion

  An object that provides information about a specific version of a Solution in a Custom dataset group.

• SolutionVersionSummary

  Provides a summary of the properties of a solution version. For a complete listing, call the DescribeSolutionVersion API.

• Tag

  The optional metadata that you apply to resources to help you categorize and organize them. Each tag consists of a key and an optional value, both of which you define. For more information see Tagging Amazon Personalize resources.

• ThemeGenerationConfig

  The configuration details for generating themes with a batch inference job.

• TrainingDataConfig

  The training data configuration to use when creating a domain recommender or custom solution version (trained model).

• TunedHpoParams

  If hyperparameter optimization (HPO) was performed, contains the hyperparameter values of the best performing model.

Enums

• BatchInferenceJobMode
  When writing a match expression against BatchInferenceJobMode, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature.
• Domain
  When writing a match expression against Domain, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature.
• ImportMode
  When writing a match expression against ImportMode, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature.
• IngestionMode
  When writing a match expression against IngestionMode, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature.
• ObjectiveSensitivity
  When writing a match expression against ObjectiveSensitivity, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature.
• RecipeProvider
  When writing a match expression against RecipeProvider, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature.
• TrainingMode
  When writing a match expression against TrainingMode, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature.
• TrainingType
  When writing a match expression against TrainingType, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature.