R/machinelearning_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 Machine Learning
#'
#' @description
#' Definition of the public APIs exposed by Amazon Machine Learning
#'
#' @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 <- machinelearning(
#'   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 <- machinelearning()
#' svc$add_tags(
#'   Foo = 123
#' )
#' }
#'
#' @section Operations:
#' \tabular{ll}{
#'  \link[=machinelearning_add_tags]{add_tags} \tab Adds one or more tags to an object, up to a limit of 10\cr
#'  \link[=machinelearning_create_batch_prediction]{create_batch_prediction} \tab Generates predictions for a group of observations\cr
#'  \link[=machinelearning_create_data_source_from_rds]{create_data_source_from_rds} \tab Creates a DataSource object from an Amazon Relational Database Service (Amazon RDS)\cr
#'  \link[=machinelearning_create_data_source_from_redshift]{create_data_source_from_redshift} \tab Creates a DataSource from a database hosted on an Amazon Redshift cluster\cr
#'  \link[=machinelearning_create_data_source_from_s3]{create_data_source_from_s3} \tab Creates a DataSource object\cr
#'  \link[=machinelearning_create_evaluation]{create_evaluation} \tab Creates a new Evaluation of an MLModel\cr
#'  \link[=machinelearning_create_ml_model]{create_ml_model} \tab Creates a new MLModel using the DataSource and the recipe as information sources\cr
#'  \link[=machinelearning_create_realtime_endpoint]{create_realtime_endpoint} \tab Creates a real-time endpoint for the MLModel\cr
#'  \link[=machinelearning_delete_batch_prediction]{delete_batch_prediction} \tab Assigns the DELETED status to a BatchPrediction, rendering it unusable\cr
#'  \link[=machinelearning_delete_data_source]{delete_data_source} \tab Assigns the DELETED status to a DataSource, rendering it unusable\cr
#'  \link[=machinelearning_delete_evaluation]{delete_evaluation} \tab Assigns the DELETED status to an Evaluation, rendering it unusable\cr
#'  \link[=machinelearning_delete_ml_model]{delete_ml_model} \tab Assigns the DELETED status to an MLModel, rendering it unusable\cr
#'  \link[=machinelearning_delete_realtime_endpoint]{delete_realtime_endpoint} \tab Deletes a real time endpoint of an MLModel\cr
#'  \link[=machinelearning_delete_tags]{delete_tags} \tab Deletes the specified tags associated with an ML object\cr
#'  \link[=machinelearning_describe_batch_predictions]{describe_batch_predictions} \tab Returns a list of BatchPrediction operations that match the search criteria in the request\cr
#'  \link[=machinelearning_describe_data_sources]{describe_data_sources} \tab Returns a list of DataSource that match the search criteria in the request\cr
#'  \link[=machinelearning_describe_evaluations]{describe_evaluations} \tab Returns a list of DescribeEvaluations that match the search criteria in the request\cr
#'  \link[=machinelearning_describe_ml_models]{describe_ml_models} \tab Returns a list of MLModel that match the search criteria in the request\cr
#'  \link[=machinelearning_describe_tags]{describe_tags} \tab Describes one or more of the tags for your Amazon ML object\cr
#'  \link[=machinelearning_get_batch_prediction]{get_batch_prediction} \tab Returns a BatchPrediction that includes detailed metadata, status, and data file information for a Batch Prediction request\cr
#'  \link[=machinelearning_get_data_source]{get_data_source} \tab Returns a DataSource that includes metadata and data file information, as well as the current status of the DataSource\cr
#'  \link[=machinelearning_get_evaluation]{get_evaluation} \tab Returns an Evaluation that includes metadata as well as the current status of the Evaluation\cr
#'  \link[=machinelearning_get_ml_model]{get_ml_model} \tab Returns an MLModel that includes detailed metadata, data source information, and the current status of the MLModel\cr
#'  \link[=machinelearning_predict]{predict} \tab Generates a prediction for the observation using the specified ML Model\cr
#'  \link[=machinelearning_update_batch_prediction]{update_batch_prediction} \tab Updates the BatchPredictionName of a BatchPrediction\cr
#'  \link[=machinelearning_update_data_source]{update_data_source} \tab Updates the DataSourceName of a DataSource\cr
#'  \link[=machinelearning_update_evaluation]{update_evaluation} \tab Updates the EvaluationName of an Evaluation\cr
#'  \link[=machinelearning_update_ml_model]{update_ml_model} \tab Updates the MLModelName and the ScoreThreshold of an MLModel
#' }
#'
#' @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 machinelearning
#' @export
machinelearning <- function(config = list(), credentials = list(), endpoint = NULL, region = NULL) {
  config <- merge_config(
    config,
    list(
      credentials = credentials,
      endpoint = endpoint,
      region = region
    )
  )
  svc <- .machinelearning$operations
  svc <- set_config(svc, config)
  return(svc)
}

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

.machinelearning$operations <- list()

.machinelearning$metadata <- list(
  service_name = "machinelearning",
  endpoints = list("*" = list(endpoint = "machinelearning.{region}.amazonaws.com", global = FALSE), "cn-*" = list(endpoint = "machinelearning.{region}.amazonaws.com.cn", global = FALSE), "eu-isoe-*" = list(endpoint = "machinelearning.{region}.cloud.adc-e.uk", global = FALSE), "us-iso-*" = list(endpoint = "machinelearning.{region}.c2s.ic.gov", global = FALSE), "us-isob-*" = list(endpoint = "machinelearning.{region}.sc2s.sgov.gov", global = FALSE), "us-isof-*" = list(endpoint = "machinelearning.{region}.csp.hci.ic.gov", global = FALSE)),
  service_id = "Machine Learning",
  api_version = "2014-12-12",
  signing_name = "machinelearning",
  json_version = "1.1",
  target_prefix = "AmazonML_20141212"
)

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