Client

personalize

R Documentation

Amazon Personalize¶

Description¶

Amazon Personalize is a machine learning service that makes it easy to add individualized recommendations to customers.

Usage¶

personalize(
  config = list(),
  credentials = list(),
  endpoint = NULL,
  region = NULL
)

Arguments¶

config

Optional configuration of credentials, endpoint, and/or region.

credentials:
- creds:
  - access_key_id: AWS access key ID
  - secret_access_key: AWS secret access key
  - session_token: AWS temporary session token
- profile: The name of a profile to use. If not given, then the default profile is used.
- anonymous: Set anonymous credentials.
endpoint: The complete URL to use for the constructed client.
region: The AWS Region used in instantiating the client.
close_connection: Immediately close all HTTP connections.
timeout: The time in seconds till a timeout exception is thrown when attempting to make a connection. The default is 60 seconds.
s3_force_path_style: Set this to true to force the request to use path-style addressing, i.e. ⁠http://s3.amazonaws.com/BUCKET/KEY⁠.
sts_regional_endpoint: Set sts regional endpoint resolver to regional or legacy https://docs.aws.amazon.com/sdkref/latest/guide/feature-sts-regionalized-endpoints.html

credentials

Optional credentials shorthand for the config parameter

creds:
- access_key_id: AWS access key ID
- secret_access_key: AWS secret access key
- session_token: AWS temporary session token
profile: The name of a profile to use. If not given, then the default profile is used.
anonymous: Set anonymous credentials.

endpoint

Optional shorthand for complete URL to use for the constructed client.

region

Optional shorthand for AWS Region used in instantiating the client.

Value¶

A client for the service. You can call the service's operations using syntax like svc$operation(...), where svc is the name you've assigned to the client. The available operations are listed in the Operations section.

Service syntax¶

svc <- personalize(
  config = list(
    credentials = list(
      creds = list(
        access_key_id = "string",
        secret_access_key = "string",
        session_token = "string"
      ),
      profile = "string",
      anonymous = "logical"
    ),
    endpoint = "string",
    region = "string",
    close_connection = "logical",
    timeout = "numeric",
    s3_force_path_style = "logical",
    sts_regional_endpoint = "string"
  ),
  credentials = list(
    creds = list(
      access_key_id = "string",
      secret_access_key = "string",
      session_token = "string"
    ),
    profile = "string",
    anonymous = "logical"
  ),
  endpoint = "string",
  region = "string"
)

Operations¶

create_batch_inference_job: Generates batch recommendations based on a list of items or users stored in Amazon S3 and exports the recommendations to an Amazon S3 bucket
create_batch_segment_job: Creates a batch segment job
create_campaign: You incur campaign costs while it is active
create_data_deletion_job: Creates a batch job that deletes all references to specific users from an Amazon Personalize dataset group in batches
create_dataset: Creates an empty dataset and adds it to the specified dataset group
create_dataset_export_job: Creates a job that exports data from your dataset to an Amazon S3 bucket
create_dataset_group: Creates an empty dataset group
create_dataset_import_job: Creates a job that imports training data from your data source (an Amazon S3 bucket) to an Amazon Personalize dataset
create_event_tracker: Creates an event tracker that you use when adding event data to a specified dataset group using the PutEvents API
create_filter: Creates a recommendation filter
create_metric_attribution: Creates a metric attribution
create_recommender: Creates a recommender with the recipe (a Domain dataset group use case) you specify
create_schema: Creates an Amazon Personalize schema from the specified schema string
create_solution: By default, all new solutions use automatic training
create_solution_version: Trains or retrains an active solution in a Custom dataset group
delete_campaign: Removes a campaign by deleting the solution deployment
delete_dataset: Deletes a dataset
delete_dataset_group: Deletes a dataset group
delete_event_tracker: Deletes the event tracker
delete_filter: Deletes a filter
delete_metric_attribution: Deletes a metric attribution
delete_recommender: Deactivates and removes a recommender
delete_schema: Deletes a schema
delete_solution: Deletes all versions of a solution and the Solution object itself
describe_algorithm: Describes the given algorithm
describe_batch_inference_job: Gets the properties of a batch inference job including name, Amazon Resource Name (ARN), status, input and output configurations, and the ARN of the solution version used to generate the recommendations
describe_batch_segment_job: Gets the properties of a batch segment job including name, Amazon Resource Name (ARN), status, input and output configurations, and the ARN of the solution version used to generate segments
describe_campaign: Describes the given campaign, including its status
describe_data_deletion_job: Describes the data deletion job created by CreateDataDeletionJob, including the job status
describe_dataset: Describes the given dataset
describe_dataset_export_job: Describes the dataset export job created by CreateDatasetExportJob, including the export job status
describe_dataset_group: Describes the given dataset group
describe_dataset_import_job: Describes the dataset import job created by CreateDatasetImportJob, including the import job status
describe_event_tracker: Describes an event tracker
describe_feature_transformation: Describes the given feature transformation
describe_filter: Describes a filter's properties
describe_metric_attribution: Describes a metric attribution
describe_recipe: Describes a recipe
describe_recommender: Describes the given recommender, including its status
describe_schema: Describes a schema
describe_solution: Describes a solution
describe_solution_version: Describes a specific version of a solution
get_solution_metrics: Gets the metrics for the specified solution version
list_batch_inference_jobs: Gets a list of the batch inference jobs that have been performed off of a solution version
list_batch_segment_jobs: Gets a list of the batch segment jobs that have been performed off of a solution version that you specify
list_campaigns: Returns a list of campaigns that use the given solution
list_data_deletion_jobs: Returns a list of data deletion jobs for a dataset group ordered by creation time, with the most recent first
list_dataset_export_jobs: Returns a list of dataset export jobs that use the given dataset
list_dataset_groups: Returns a list of dataset groups
list_dataset_import_jobs: Returns a list of dataset import jobs that use the given dataset
list_datasets: Returns the list of datasets contained in the given dataset group
list_event_trackers: Returns the list of event trackers associated with the account
list_filters: Lists all filters that belong to a given dataset group
list_metric_attribution_metrics: Lists the metrics for the metric attribution
list_metric_attributions: Lists metric attributions
list_recipes: Returns a list of available recipes
list_recommenders: Returns a list of recommenders in a given Domain dataset group
list_schemas: Returns the list of schemas associated with the account
list_solutions: Returns a list of solutions in a given dataset group
list_solution_versions: Returns a list of solution versions for the given solution
list_tags_for_resource: Get a list of tags attached to a resource
start_recommender: Starts a recommender that is INACTIVE
stop_recommender: Stops a recommender that is ACTIVE
stop_solution_version_creation: Stops creating a solution version that is in a state of CREATE_PENDING or CREATE IN_PROGRESS
tag_resource: Add a list of tags to a resource
untag_resource: Removes the specified tags that are attached to a resource
update_campaign: Updates a campaign to deploy a retrained solution version with an existing campaign, change your campaign's minProvisionedTPS, or modify your campaign's configuration
update_dataset: Update a dataset to replace its schema with a new or existing one
update_metric_attribution: Updates a metric attribution
update_recommender: Updates the recommender to modify the recommender configuration
update_solution: Updates an Amazon Personalize solution to use a different automatic training configuration

Examples¶

## Not run: 
svc <- personalize()
svc$create_batch_inference_job(
  Foo = 123
)

## End(Not run)