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#' Get a report on missing values for the model.
#'
#' The missing values report is a list of items, one per feature, sorted by
#' missing count in descending order. Each item in the report contains details on the number
#' of missing values for that feature and how they were handled by the model.
#'
#' @inheritParams GetModel
#' @return A list containing:
#' \itemize{
#' \item feature character. The name of the feature.
#' \item type character. Feature type (numeric or categorical).
#' \item missingCount numeric. The number of missing values in the training data for
#' that feature.
#' \item missingPercentage numeric. The percentage of missing values in the training
#' data for the feature.
#' \item tasks list. A list of information on each task that was applied to that feature
#' to handle missing values. This information contains:
#' \itemize{
#' \item id character. The id of the node in the model blueprint chart for this task.
#' (See \link{GetBlueprintChart} for more information on blueprint charts.)
#' \item name character. The name of the task.
#' \item descriptions character. Aggregated information about how the task handles
#' missing values.
#' }
#' }
#' @examples
#' \dontrun{
#' projectId <- "5984b4d7100d2b31c1166529"
#' modelId <- "5984b4d7100d2b31c1166529"
#' GetMissingValuesReport(projectId, modelId)
#' }
#' @export
GetMissingValuesReport <- function(project, modelId) {
projectId <- ValidateProject(project)
if (!is.character(modelId)) { stop("Invalid modelId") }
routeString <- UrlJoin("projects", projectId, "models", modelId, "missingReport")
result <- DataRobotGET(routeString, simplify = FALSE)
as.dataRobotMissingValuesReport(result$missingValuesReport)
}
# Process the missing values report
#
# Input is:
# list(list(missingCount = <missingCount>,
# tasks = list(<taskId> = list(descriptions = list(<descriptions>),
# name = <name>),
# type = <type>,
# feature = <featureName>,
# missingPercentage = <missingPercentage>), ...)
# Output is:
# list(<featureName> = list(type = <featureType>,
# missingCount = <missingCount>,
# missingPercentage = <missingPercentage>,
# tasks = list(list(name = <Task1Name>,
# descriptions = c(<Task1Description>, ...),
# id = <taskId>), ...)), ...)
as.dataRobotMissingValuesReport <- function(inList) {
features <- lapply(inList, `[[`, "feature")
for (i in seq_along(inList)) {
inList[[i]]$feature <- NULL # Drop feature from within list
for (j in seq_along(inList[[i]]$tasks)) {
inList[[i]]$tasks[[j]]$id <- names(inList[[i]]$tasks)[[j]]
inList[[i]]$tasks[[j]]$name <- as.character(inList[[i]]$tasks[[j]]$name)
inList[[i]]$tasks[[j]]$descriptions <- as.character(inList[[i]]$tasks[[j]]$descriptions)
}
inList[[i]]$tasks <- unname(inList[[i]]$tasks)
}
stats::setNames(inList, features)
}
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