R/prauc_table.CV.SuperLearner.R

In ck37/ckTools: Chris Kennedy's R Toolkit

#' @title Table of cross-validated PR-AUCs from CV.SuperLearner result
#' @description
#' Calculates cross-validated PR-AUC for each learner in the CV.SuperLearner. Also
#' calculates standard-error, confidence interval and p-value. Based on initial
#' code by Alan Hubbard.
#'
#' @param x CV.SuperLearner object
#' @param y Outcome vector, if not already added to CV.SL object.
#' @param sort Sort table by order of AUC.
#' @param null_hypothesis Not implemented yet
#' @param two_tailed Not implemented yet
#' @param lower.tail Not implemented yet
#' @param ... Any additional unused arguments, due to the prauc_table generic.
#'
#' @return Dataframe table with PR-AUC and std dev.
#'
#' @examples
#' library(SuperLearner)
#' library(ck37r)
#'
#' data(Boston, package = "MASS")
#'
#' set.seed(1)
#' y = as.numeric(Boston$medv > 23)
#' cvsl = CV.SuperLearner(Y = y,
#'                        X = subset(Boston, select = -medv),
#'                        family = binomial(),
#'                        cvControl = list(V = 2, stratifyCV = TRUE),
#'                        SL.library = c("SL.mean", "SL.glm"))
#' prauc_table(cvsl, y = y)
#'
#' @references
#'
#' Boyd, K., Eng, K. H., & Page, C. D. (2013). Area under the precision-recall
#' curve: point estimates and confidence intervals. In Joint European conference
#' on machine learning and knowledge discovery in databases (pp. 451-466).
#' Springer, Berlin, Heidelberg.
#'
#' Polley EC, van der Laan MJ (2010) Super Learner in Prediction. U.C. Berkeley
#' Division of Biostatistics Working Paper Series. Paper 226.
#' http://biostats.bepress.com/ucbbiostat/paper266/
#'
#' van der Laan, M. J., Polley, E. C. and Hubbard, A. E. (2007) Super Learner.
#' Statistical Applications of Genetics and Molecular Biology, 6, article 25.
#' http://www.degruyter.com/view/j/sagmb.2007.6.issue-1/sagmb.2007.6.1.1309/sagmb.2007.6.1.1309.xml
#'
#' @seealso \code{\link{cvsl_auc}} \code{\link{plot_roc.SuperLearner}}
#'    \code{\link[cvAUC]{ci.cvAUC}}
#'
#' @importFrom stats pnorm
#'
# Need to explicitly document method due to period in the class name.
#' @method prauc_table CV.SuperLearner
#'
#' @export
prauc_table.CV.SuperLearner = function(x, y = x$Y, sort = TRUE,
                                     null_hypothesis = NULL,
                                     two_tailed = FALSE,
                                     lower.tail = TRUE,
                                     ...) {

  # Use a clearer object name.
  cvsl = x

  # Vector to save the fold id for each observation.
  fold_ids = rep(NA, length(cvsl$SL.predict))

  # Number of CV folds (or bootstrap repetitions in theory).
  n_samples = length(cvsl$folds)

  # Loop over each SL fold and extract which observations were in that fold.
  for (fold_i in seq(n_samples)) {
    fold_ids[cvsl$folds[[fold_i]]] = fold_i
  }

  # Dataframe to save PR-AUC and CI.
  # Add 2 more than the # of learners to hold DiscreteSL + SL.
  #result_df = data.frame(matrix(nrow = ncol(cvsl$library.predict) + 2, ncol = 2L))
  result_df = data.frame(matrix(nrow = ncol(cvsl$library.predict) + 2, ncol = 4L))

  #colnames(aucs) = c("prauc", "se", "ci_lower", "ci_upper", "p-value")
  #colnames(result_df) = c("prauc", "sd")
  colnames(result_df) = c("prauc", "stderr", "ci_lower", "ci_upper")#, "p-value")

  # Loop over each learner.
  for (learner_i in 1:nrow(result_df)) {
    # Create a default in case there is an error. Will be overwritten
    # if successful.
    #result = list(prauc = NA, se = NA, ci = c(NA, NA))
    result = list(prauc = NA, sd = NA)

    try({
      if (learner_i <= ncol(cvsl$library.predict)) {

        result = prauc(cvsl$library.predict[, learner_i], y, test_folds = fold_ids)

      } else if (learner_i == nrow(result_df)) {
        # Do SuperLearner after all of the learners + DiscreteSL.
        result = prauc(cvsl$SL.predict, y, test_folds = fold_ids)
      } else {
        # Do discrete SL after all of the learners.
        result = prauc(cvsl$discreteSL.predict, y, test_folds = fold_ids)
      }
    }, silent = TRUE)

    result_df[learner_i, "prauc"] = result$prauc
    #result_df[learner_i, "sd"] = result$sd

    #std_err = result$sd / sqrt(n_samples)
    #result_df[learner_i, "stderr"] = std_err

    result_df[learner_i, "stderr"] = result$std_err

    #ci = result$prauc + c(-1, 1) * 1.96 * std_err
    #result_df[learner_i, "ci_lower"] = ci[1]
    #result_df[learner_i, "ci_upper"] = ci[2]

    result_df[learner_i, "ci_lower"] = result$ci[1]
    result_df[learner_i, "ci_upper"] = result$ci[2]

    # Don't generate p-value yet.
  }

  rownames(result_df) = c(cvsl$libraryNames, "DiscreteSL", "SuperLearner")

  if (sort) {
    # Sort in ascending order so best AUCs are at bottom of table.
    # This will also place NAs at the bottom of the table.
    result_df = result_df[order(result_df$prauc), ]

  }

  return(result_df)
}