R/frauddetector_service.R

In paws.machine.learning: 'Amazon Web Services' Machine Learning Services

# This file is generated by make.paws. Please do not edit here.
#' @importFrom paws.common new_handlers new_service set_config merge_config
NULL

#' Amazon Fraud Detector
#'
#' @description
#' This is the Amazon Fraud Detector API Reference. This guide is for
#' developers who need detailed information about Amazon Fraud Detector API
#' actions, data types, and errors. For more information about Amazon Fraud
#' Detector features, see the [Amazon Fraud Detector User
#' Guide](https://docs.aws.amazon.com/frauddetector/latest/ug/).
#' 
#' We provide the Query API as well as AWS software development kits (SDK)
#' for Amazon Fraud Detector in Java and Python programming languages.
#' 
#' The Amazon Fraud Detector Query API provides HTTPS requests that use the
#' HTTP verb GET or POST and a Query parameter `Action`. AWS SDK provides
#' libraries, sample code, tutorials, and other resources for software
#' developers who prefer to build applications using language-specific APIs
#' instead of submitting a request over HTTP or HTTPS. These libraries
#' provide basic functions that automatically take care of tasks such as
#' cryptographically signing your requests, retrying requests, and handling
#' error responses, so that it is easier for you to get started. For more
#' information about the AWS SDKs, go to [Tools to build on
#' AWS](https://aws.amazon.com/developer/tools/) page, scroll down to the
#' **SDK** section, and choose plus (+) sign to expand the section.
#'
#' @param
#' config
#' Optional configuration of credentials, endpoint, and/or region.
#' \itemize{
#' \item{\strong{credentials}:} {\itemize{
#' \item{\strong{creds}:} {\itemize{
#' \item{\strong{access_key_id}:} {AWS access key ID}
#' \item{\strong{secret_access_key}:} {AWS secret access key}
#' \item{\strong{session_token}:} {AWS temporary session token}
#' }}
#' \item{\strong{profile}:} {The name of a profile to use. If not given, then the default profile is used.}
#' \item{\strong{anonymous}:} {Set anonymous credentials.}
#' \item{\strong{endpoint}:} {The complete URL to use for the constructed client.}
#' \item{\strong{region}:} {The AWS Region used in instantiating the client.}
#' }}
#' \item{\strong{close_connection}:} {Immediately close all HTTP connections.}
#' \item{\strong{timeout}:} {The time in seconds till a timeout exception is thrown when attempting to make a connection. The default is 60 seconds.}
#' \item{\strong{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`.}
#' \item{\strong{sts_regional_endpoint}:} {Set sts regional endpoint resolver to regional or legacy \url{https://docs.aws.amazon.com/sdkref/latest/guide/feature-sts-regionalized-endpoints.html}}
#' }
#' @param
#' credentials
#' Optional credentials shorthand for the config parameter
#' \itemize{
#' \item{\strong{creds}:} {\itemize{
#' \item{\strong{access_key_id}:} {AWS access key ID}
#' \item{\strong{secret_access_key}:} {AWS secret access key}
#' \item{\strong{session_token}:} {AWS temporary session token}
#' }}
#' \item{\strong{profile}:} {The name of a profile to use. If not given, then the default profile is used.}
#' \item{\strong{anonymous}:} {Set anonymous credentials.}
#' }
#' @param
#' endpoint
#' Optional shorthand for complete URL to use for the constructed client.
#' @param
#' region
#' Optional shorthand for AWS Region used in instantiating the client.
#'
#' @section Service syntax:
#' ```
#' svc <- frauddetector(
#'   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"
#' )
#' ```
#'
#' @examples
#' \dontrun{
#' svc <- frauddetector()
#' svc$batch_create_variable(
#'   Foo = 123
#' )
#' }
#'
#' @section Operations:
#' \tabular{ll}{
#'  \link[=frauddetector_batch_create_variable]{batch_create_variable} \tab Creates a batch of variables\cr
#'  \link[=frauddetector_batch_get_variable]{batch_get_variable} \tab Gets a batch of variables\cr
#'  \link[=frauddetector_cancel_batch_import_job]{cancel_batch_import_job} \tab Cancels an in-progress batch import job\cr
#'  \link[=frauddetector_cancel_batch_prediction_job]{cancel_batch_prediction_job} \tab Cancels the specified batch prediction job\cr
#'  \link[=frauddetector_create_batch_import_job]{create_batch_import_job} \tab Creates a batch import job\cr
#'  \link[=frauddetector_create_batch_prediction_job]{create_batch_prediction_job} \tab Creates a batch prediction job\cr
#'  \link[=frauddetector_create_detector_version]{create_detector_version} \tab Creates a detector version\cr
#'  \link[=frauddetector_create_list]{create_list} \tab Creates a list\cr
#'  \link[=frauddetector_create_model]{create_model} \tab Creates a model using the specified model type\cr
#'  \link[=frauddetector_create_model_version]{create_model_version} \tab Creates a version of the model using the specified model type and model id\cr
#'  \link[=frauddetector_create_rule]{create_rule} \tab Creates a rule for use with the specified detector\cr
#'  \link[=frauddetector_create_variable]{create_variable} \tab Creates a variable\cr
#'  \link[=frauddetector_delete_batch_import_job]{delete_batch_import_job} \tab Deletes the specified batch import job ID record\cr
#'  \link[=frauddetector_delete_batch_prediction_job]{delete_batch_prediction_job} \tab Deletes a batch prediction job\cr
#'  \link[=frauddetector_delete_detector]{delete_detector} \tab Deletes the detector\cr
#'  \link[=frauddetector_delete_detector_version]{delete_detector_version} \tab Deletes the detector version\cr
#'  \link[=frauddetector_delete_entity_type]{delete_entity_type} \tab Deletes an entity type\cr
#'  \link[=frauddetector_delete_event]{delete_event} \tab Deletes the specified event\cr
#'  \link[=frauddetector_delete_events_by_event_type]{delete_events_by_event_type} \tab Deletes all events of a particular event type\cr
#'  \link[=frauddetector_delete_event_type]{delete_event_type} \tab Deletes an event type\cr
#'  \link[=frauddetector_delete_external_model]{delete_external_model} \tab Removes a SageMaker model from Amazon Fraud Detector\cr
#'  \link[=frauddetector_delete_label]{delete_label} \tab Deletes a label\cr
#'  \link[=frauddetector_delete_list]{delete_list} \tab Deletes the list, provided it is not used in a rule\cr
#'  \link[=frauddetector_delete_model]{delete_model} \tab Deletes a model\cr
#'  \link[=frauddetector_delete_model_version]{delete_model_version} \tab Deletes a model version\cr
#'  \link[=frauddetector_delete_outcome]{delete_outcome} \tab Deletes an outcome\cr
#'  \link[=frauddetector_delete_rule]{delete_rule} \tab Deletes the rule\cr
#'  \link[=frauddetector_delete_variable]{delete_variable} \tab Deletes a variable\cr
#'  \link[=frauddetector_describe_detector]{describe_detector} \tab Gets all versions for a specified detector\cr
#'  \link[=frauddetector_describe_model_versions]{describe_model_versions} \tab Gets all of the model versions for the specified model type or for the specified model type and model ID\cr
#'  \link[=frauddetector_get_batch_import_jobs]{get_batch_import_jobs} \tab Gets all batch import jobs or a specific job of the specified ID\cr
#'  \link[=frauddetector_get_batch_prediction_jobs]{get_batch_prediction_jobs} \tab Gets all batch prediction jobs or a specific job if you specify a job ID\cr
#'  \link[=frauddetector_get_delete_events_by_event_type_status]{get_delete_events_by_event_type_status} \tab Retrieves the status of a DeleteEventsByEventType action\cr
#'  \link[=frauddetector_get_detectors]{get_detectors} \tab Gets all detectors or a single detector if a detectorId is specified\cr
#'  \link[=frauddetector_get_detector_version]{get_detector_version} \tab Gets a particular detector version\cr
#'  \link[=frauddetector_get_entity_types]{get_entity_types} \tab Gets all entity types or a specific entity type if a name is specified\cr
#'  \link[=frauddetector_get_event]{get_event} \tab Retrieves details of events stored with Amazon Fraud Detector\cr
#'  \link[=frauddetector_get_event_prediction]{get_event_prediction} \tab Evaluates an event against a detector version\cr
#'  \link[=frauddetector_get_event_prediction_metadata]{get_event_prediction_metadata} \tab Gets details of the past fraud predictions for the specified event ID, event type, detector ID, and detector version ID that was generated in the specified time period\cr
#'  \link[=frauddetector_get_event_types]{get_event_types} \tab Gets all event types or a specific event type if name is provided\cr
#'  \link[=frauddetector_get_external_models]{get_external_models} \tab Gets the details for one or more Amazon SageMaker models that have been imported into the service\cr
#'  \link[=frauddetector_get_kms_encryption_key]{get_kms_encryption_key} \tab Gets the encryption key if a KMS key has been specified to be used to encrypt content in Amazon Fraud Detector\cr
#'  \link[=frauddetector_get_labels]{get_labels} \tab Gets all labels or a specific label if name is provided\cr
#'  \link[=frauddetector_get_list_elements]{get_list_elements} \tab Gets all the elements in the specified list\cr
#'  \link[=frauddetector_get_lists_metadata]{get_lists_metadata} \tab Gets the metadata of either all the lists under the account or the specified list\cr
#'  \link[=frauddetector_get_models]{get_models} \tab Gets one or more models\cr
#'  \link[=frauddetector_get_model_version]{get_model_version} \tab Gets the details of the specified model version\cr
#'  \link[=frauddetector_get_outcomes]{get_outcomes} \tab Gets one or more outcomes\cr
#'  \link[=frauddetector_get_rules]{get_rules} \tab Get all rules for a detector (paginated) if ruleId and ruleVersion are not specified\cr
#'  \link[=frauddetector_get_variables]{get_variables} \tab Gets all of the variables or the specific variable\cr
#'  \link[=frauddetector_list_event_predictions]{list_event_predictions} \tab Gets a list of past predictions\cr
#'  \link[=frauddetector_list_tags_for_resource]{list_tags_for_resource} \tab Lists all tags associated with the resource\cr
#'  \link[=frauddetector_put_detector]{put_detector} \tab Creates or updates a detector\cr
#'  \link[=frauddetector_put_entity_type]{put_entity_type} \tab Creates or updates an entity type\cr
#'  \link[=frauddetector_put_event_type]{put_event_type} \tab Creates or updates an event type\cr
#'  \link[=frauddetector_put_external_model]{put_external_model} \tab Creates or updates an Amazon SageMaker model endpoint\cr
#'  \link[=frauddetector_put_kms_encryption_key]{put_kms_encryption_key} \tab Specifies the KMS key to be used to encrypt content in Amazon Fraud Detector\cr
#'  \link[=frauddetector_put_label]{put_label} \tab Creates or updates label\cr
#'  \link[=frauddetector_put_outcome]{put_outcome} \tab Creates or updates an outcome\cr
#'  \link[=frauddetector_send_event]{send_event} \tab Stores events in Amazon Fraud Detector without generating fraud predictions for those events\cr
#'  \link[=frauddetector_tag_resource]{tag_resource} \tab Assigns tags to a resource\cr
#'  \link[=frauddetector_untag_resource]{untag_resource} \tab Removes tags from a resource\cr
#'  \link[=frauddetector_update_detector_version]{update_detector_version} \tab Updates a detector version\cr
#'  \link[=frauddetector_update_detector_version_metadata]{update_detector_version_metadata} \tab Updates the detector version's description\cr
#'  \link[=frauddetector_update_detector_version_status]{update_detector_version_status} \tab Updates the detector version’s status\cr
#'  \link[=frauddetector_update_event_label]{update_event_label} \tab Updates the specified event with a new label\cr
#'  \link[=frauddetector_update_list]{update_list} \tab Updates a list\cr
#'  \link[=frauddetector_update_model]{update_model} \tab Updates model description\cr
#'  \link[=frauddetector_update_model_version]{update_model_version} \tab Updates a model version\cr
#'  \link[=frauddetector_update_model_version_status]{update_model_version_status} \tab Updates the status of a model version\cr
#'  \link[=frauddetector_update_rule_metadata]{update_rule_metadata} \tab Updates a rule's metadata\cr
#'  \link[=frauddetector_update_rule_version]{update_rule_version} \tab Updates a rule version resulting in a new rule version\cr
#'  \link[=frauddetector_update_variable]{update_variable} \tab Updates a variable
#' }
#'
#' @return
#' 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.
#'
#' @rdname frauddetector
#' @export
frauddetector <- function(config = list(), credentials = list(), endpoint = NULL, region = NULL) {
  config <- merge_config(
    config,
    list(
      credentials = credentials,
      endpoint = endpoint,
      region = region
    )
  )
  svc <- .frauddetector$operations
  svc <- set_config(svc, config)
  return(svc)
}

# Private API objects: metadata, handlers, interfaces, etc.
.frauddetector <- list()

.frauddetector$operations <- list()

.frauddetector$metadata <- list(
  service_name = "frauddetector",
  endpoints = list("*" = list(endpoint = "frauddetector.{region}.amazonaws.com", global = FALSE), "cn-*" = list(endpoint = "frauddetector.{region}.amazonaws.com.cn", global = FALSE), "us-iso-*" = list(endpoint = "frauddetector.{region}.c2s.ic.gov", global = FALSE), "us-isob-*" = list(endpoint = "frauddetector.{region}.sc2s.sgov.gov", global = FALSE)),
  service_id = "FraudDetector",
  api_version = "2019-11-15",
  signing_name = "frauddetector",
  json_version = "1.1",
  target_prefix = "AWSHawksNestServiceFacade"
)

.frauddetector$service <- function(config = list()) {
  handlers <- new_handlers("jsonrpc", "v4")
  new_service(.frauddetector$metadata, handlers, config)
}