After sourcing this patch, you can use "wss" as a new measure in plotting performance curves, same like fp, tp, rec, auc, ... You should have ROCR installed and imported first

patch_ROCR_WSS.R

# Patch ROCR R package to support plotting of WSS (Work Saved over Sampling)
# Author: Hossam Hammady ([email protected])
# Organization: Qatar Computing Research Institute; Data Analytics Group (http://da.qcri.qa)
# Date: 17-Feb-2014
# License: MIT
# Description: After sourcing this patch, you can use "wss" as a new measure in
# .. plotting performance curves, same like fp, tp, rec, auc, ...
# .. You should have ROCR installed and imported first


original.define.environments <- .define.environments
.define.environments <- function()
{
  # get original environments
  envir.list <- original.define.environments()
  long.unit.names = envir.list$long.unit.names
  function.names = envir.list$function.names   
  obligatory.x.axis = envir.list$obligatory.x.axis
  optional.arguments = envir.list$optional.arguments         
  default.values = envir.list$default.values

  .performance.wss <- function(predictions, labels, cutoffs,
    fp, tp, fn, tn, n.pos, n.neg, n.pos.pred, n.neg.pred) # append any optional arguments
  {
    list(cutoffs, (tn+fn)/(n.pos+n.neg) - 1 + tp/(tp+fn))
  }

  assign("wss", "Work Saved over Random Sampling",
    envir = long.unit.names)
  assign("wss", .performance.wss,
    envir = function.names)

  list(
    long.unit.names = long.unit.names,
    function.names = function.names,
    obligatory.x.axis = obligatory.x.axis,
    optional.arguments = optional.arguments,
    default.values = default.values
  )
}

# copied from original function
performance <- function (prediction.obj, measure, x.measure = "cutoff", ...)
{
    envir.list <- .define.environments()
    long.unit.names <- envir.list$long.unit.names
    function.names <- envir.list$function.names
    obligatory.x.axis <- envir.list$obligatory.x.axis
    optional.arguments <- envir.list$optional.arguments
    default.values <- envir.list$default.values
    if (class(prediction.obj) != "prediction" || !exists(measure,
        where = long.unit.names, inherits = FALSE) || !exists(x.measure,
        where = long.unit.names, inherits = FALSE)) {
        stop(paste("Wrong argument types: First argument must be of type",
            "'prediction'; second and optional third argument must",
            "be available performance measures!"))
    }
    if (exists(x.measure, where = obligatory.x.axis, inherits = FALSE)) {
        message <- paste("The performance measure", x.measure,
            "can only be used as 'measure', because it has",
            "the following obligatory 'x.measure':\n", get(x.measure,
                envir = obligatory.x.axis))
        stop(message)
    }
    if (exists(measure, where = obligatory.x.axis, inherits = FALSE)) {
        x.measure <- get(measure, envir = obligatory.x.axis)
    }
    if (x.measure == "cutoff" || exists(measure, where = obligatory.x.axis,
        inherits = FALSE)) {
        optional.args <- list(...)
        argnames <- c()
        if (exists(measure, where = optional.arguments, inherits = FALSE)) {
            argnames <- get(measure, envir = optional.arguments)
            default.arglist <- list()
            for (i in 1:length(argnames)) {
                default.arglist <- c(default.arglist, get(paste(measure,
                  ":", argnames[i], sep = ""), envir = default.values,
                  inherits = FALSE))
            }
            names(default.arglist) <- argnames
            for (i in 1:length(argnames)) {
                templist <- list(optional.args, default.arglist[[i]])
                names(templist) <- c("arglist", argnames[i])
                optional.args <- do.call(".farg", templist)
            }
        }
        optional.args <- .select.args(optional.args, argnames)
        function.name <- get(measure, envir = function.names)
        x.values <- list()
        y.values <- list()
        for (i in 1:length(prediction.obj@predictions)) {
            argumentlist <- .sarg(optional.args, predictions = prediction.obj@predictions[[i]],
                labels = prediction.obj@labels[[i]], cutoffs = prediction.obj@cutoffs[[i]],
                fp = prediction.obj@fp[[i]], tp = prediction.obj@tp[[i]],
                fn = prediction.obj@fn[[i]], tn = prediction.obj@tn[[i]],
                n.pos = [email protected][[i]], n.neg = [email protected][[i]],
                n.pos.pred = [email protected][[i]],
                n.neg.pred = [email protected][[i]])
            ans <- do.call(function.name, argumentlist)
            if (!is.null(ans[[1]]))
                x.values <- c(x.values, list(ans[[1]]))
            y.values <- c(y.values, list(ans[[2]]))
        }
        if (!(length(x.values) == 0 || length(x.values) == length(y.values))) {
            stop("Consistency error.")
        }
        return(new("performance", x.name = get(x.measure, envir = long.unit.names),
            y.name = get(measure, envir = long.unit.names), alpha.name = "none",
            x.values = x.values, y.values = y.values, alpha.values = list()))
    }
    else {
        perf.obj.1 <- performance(prediction.obj, measure = x.measure,
            ...)
        perf.obj.2 <- performance(prediction.obj, measure = measure,
            ...)
        return(.combine.performance.objects(perf.obj.1, perf.obj.2))
    }
}