R/ListModelJobs.R

In datarobot: 'DataRobot' Predictive Modeling API

#' Retrieve status of Autopilot modeling jobs that are not complete
#'
#' This function requests information on DataRobot Autopilot modeling
#' tasks that are not complete, for one of three reasons: the task is
#' running and has not yet completed; the task is queued and has not
#' yet been started; or, the task has terminated due to an error.
#'
#' The jobStatus variable specifies which of the three groups of
#' modeling tasks is of interest. Specifically, if jobStatus has the
#' value 'inprogress', the request returns information about modeling
#' tasks that are running but not yet complete; if jobStatus has the
#' value 'queue', the request returns information about modeling tasks
#' that are scheduled to run but have not yet started; if jobStatus
#' has the value 'error', the request returns information about modeling
#' tasks that have terminated due to an error. By default, jobStatus is
#' NULL, which means jobs with status "inprogress" or "queue" are returned,
#' but not those with status "error".
#'
#' @inheritParams GetPredictJobs
#' @return A list of lists with one element for each modeling task
#' in the group being queried; if there are no tasks in the class
#' being queried, an empty list is returned. If the group is not empty,
#' a list is returned with the following nine elements:
#' \itemize{
#'   \item status. Prediction job status; an element of `JobStatus`, e.g. `JobStatus$Queue`.
#'   \item processes. List of character vectors describing any preprocessing applied.
#'   \item projectId. Character string giving the unique identifier for the project.
#'   \item modelId character. The unique identifier for the related model.
#'   \item samplePct. Numeric: the percentage of the dataset used for model building.
#'   \item modelType. Character string specifying the model type.
#'   \item modelCategory. Character string: what kind of model this is - `prime` for DataRobot
#'     Prime models, `blend` for blender models, and `model` for other models.
#'   \item featurelistId. Character string: id of the featurelist used in fitting the model.
#'   \item blueprintId. Character string: id of the DataRobot blueprint on which the model is based.
#'   \item modelJobId. Character: id of the job.
#'   \item isBlocked logical. If TRUE, the job is blocked (cannot be executed) until its
#'     dependencies are resolved.
#' }
#' @examples
#' \dontrun{
#'   projectId <- "59a5af20c80891534e3c2bde"
#'   ListModelJobs(projectId)
#' }
#' @export
#' @md
ListModelJobs <- function(project, status = NULL) {
  projectId <- ValidateProject(project)
  query <- if (is.null(status)) NULL else list(status = status)
  routeString <- UrlJoin("projects", projectId, "modelJobs")
  pendingList <- DataRobotGET(routeString, query = query)
  if (length(pendingList) == 0) {
    data.frame(status = character(0), processes = I(list()), projectId = character(0),
               modelId = character(0), samplePct = numeric(0), modelType = character(0),
               featurelistId = character(0),  modelCategory = character(0),
               blueprintId = character(0), modelJobId = character(0),
               trainingRowCount = numeric(0), isBlocked = logical(0))
  } else {
    idIndex <- which(names(pendingList) == "id")
    names(pendingList)[idIndex] <- "modelJobId"
    as.dataRobotModelJob(pendingList)
  }
}


#' Request information about a single model job
#'
#' @inheritParams DeleteProject
#' @param modelJobId Character string specifying the job id
#' @return list with following elements:
#' \itemize{
#'   \item status character. Model job status; an element of `JobStatus`, e.g.
#'     `JobStatus$Queue`.
#'   \item processes list. List of character vectors describing any preprocessing applied.
#'   \item projectId character. The unique identifier for the project.
#'   \item modelId character. The unique identifier for the related model.
#'   \item samplePct numeric. The percentage of the dataset used for model building.
#'   \item trainingRowCount. Integer. The number of rows of the project dataset used in training
#'     the model.
#'   \item modelType character. string specifying the model this job builds.
#'   \item modelCategory character. What kind of model this is - `prime` for DataRobot Prime
#'     models, `blend` for blender models, and `model` for other models.
#'   \item featurelistId character. Id of the featurelist used in fitting the model.
#'   \item blueprintId character. Id of the DataRobot blueprint on which the model is based.
#'   \item modelJobId character. Id of the job.
#'   \item isBlocked logical. If TRUE, the job is blocked (cannot be executed) until its
#'     dependencies are resolved.
#' }
#' @examples
#' \dontrun{
#'   projectId <- "59a5af20c80891534e3c2bde"
#'   initialJobs <- ListModelJobs(project)
#'   job <- initialJobs[[1]]
#'   modelJobId <- job$modelJobId
#'   GetModelJob(projectId, modelJobId)
#' }
#' @export
#' @md
GetModelJob <- function(project, modelJobId) {
  projectId <- ValidateProject(project)
  routeString <- UrlJoin("projects", projectId, "modelJobs", modelJobId)
  response <- DataRobotGET(routeString, followLocation = FALSE)
  idIndex <- which(names(response) == "id")
  names(response)[idIndex] <- "modelJobId"
  as.dataRobotModelJob(response)
}

as.dataRobotModelJob <- function(inList) {
  elements <- c("status",
                "processes",
                "projectId",
                "modelId",
                "samplePct",
                "trainingRowCount",
                "modelType",
                "featurelistId",
                "modelCategory",
                "blueprintId",
                "modelJobId",
                "isBlocked")
  ApplySchema(inList, elements)
}