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performance <- function(prediction.obj, measure,
x.measure="cutoff", ...) {
## define the needed environments
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
## abort in case of misuse
if (!('prediction' %in% class(prediction.obj)) ||
!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!"))
}
## abort, if attempt is made to use a measure that has an obligatory
## x.axis as the x.measure (cannot be combined)
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 measure is a performance measure with obligatory x.axis, then
## enforce this axis:
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 )) {
## fetch from '...' any optional arguments for the performance
## measure at hand that are given, otherwise fill up the default values
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 )
## determine function name
function.name <- get( measure, envir=function.names )
## for each x-validation run, compute the requested performance measure
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= prediction.obj@n.pos[[i]],
n.neg= prediction.obj@n.neg[[i]],
n.pos.pred= prediction.obj@n.pos.pred[[i]],
n.neg.pred= prediction.obj@n.neg.pred[[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.")
}
## create a new performance object
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 ) )
}
}
.combine.performance.objects <- function( p.obj.1, p.obj.2 ) {
## some checks for misusage (in any way, this function is
## only for internal use)
if ( p.obj.1@x.name != p.obj.2@x.name ) {
stop("Error: Objects need to have identical x axis.")
}
if ( p.obj.1@alpha.name != "none" || p.obj.2@alpha.name != "none") {
stop("Error: At least one of the two objects has already been merged.")
}
if (length(p.obj.1@x.values) != length(p.obj.2@x.values)) {
stop(paste("Only performance objects with identical number of",
"cross-validation runs can be combined."))
}
x.values <- list()
x.name <- p.obj.1@y.name
y.values <- list()
y.name <- p.obj.2@y.name
alpha.values <- list()
alpha.name <- p.obj.1@x.name
for (i in 1:length( p.obj.1@x.values )) {
x.values.1 <- p.obj.1@x.values[[i]]
y.values.1 <- p.obj.1@y.values[[i]]
x.values.2 <- p.obj.2@x.values[[i]]
y.values.2 <- p.obj.2@y.values[[i]]
## cutoffs of combined object = merged cutoffs of simple objects
cutoffs <- sort( unique( c(x.values.1, x.values.2)), decreasing=TRUE )
## calculate y.values at cutoffs using step function
y.values.int.1 <- approxfun(x.values.1, y.values.1,
method="constant",f=1,rule=2)(cutoffs)
y.values.int.2 <- approxfun(x.values.2, y.values.2,
method="constant",f=1,rule=2)(cutoffs)
## 'approxfun' ignores NA and NaN
objs <- list( y.values.int.1, y.values.int.2)
objs.x <- list( x.values.1, x.values.2 )
na.cutoffs.1.bool <- is.na( y.values.1) & !is.nan( y.values.1 )
nan.cutoffs.1.bool <- is.nan( y.values.1)
na.cutoffs.2.bool <- is.na( y.values.2) & !is.nan( y.values.2 )
nan.cutoffs.2.bool <- is.nan( y.values.2)
bools <- list(na.cutoffs.1.bool, nan.cutoffs.1.bool,
na.cutoffs.2.bool, nan.cutoffs.2.bool)
values <- c(NA,NaN,NA,NaN)
for (j in 1:4) {
for (k in which(bools[[j]])) {
interval.max <- objs.x[[ ceiling(j/2) ]][k]
interval.min <- -Inf
if (k < length(objs.x[[ ceiling(j/2) ]])) {
interval.min <- objs.x[[ ceiling(j/2) ]][k+1]
}
objs[[ ceiling(j/2) ]][cutoffs <= interval.max &
cutoffs > interval.min ] <- values[j]
}
}
alpha.values <- c(alpha.values, list(cutoffs))
x.values <- c(x.values, list(objs[[1]]))
y.values <- c(y.values, list(objs[[2]]))
}
return( new("performance",
x.name=x.name, y.name=y.name,
alpha.name=alpha.name, x.values=x.values,
y.values=y.values, alpha.values=alpha.values))
}
.define.environments <- function() {
## There are five environments: long.unit.names, function.names,
## obligatory.x.axis, optional.arguments, default.values
## Define long names corresponding to the measure abbreviations.
long.unit.names <- new.env()
assign("none","None", envir=long.unit.names)
assign("cutoff", "Cutoff", envir=long.unit.names)
assign("acc", "Accuracy", envir=long.unit.names)
assign("err", "Error Rate", envir=long.unit.names)
assign("fpr", "False positive rate", envir=long.unit.names)
assign("tpr", "True positive rate", envir=long.unit.names)
assign("rec", "Recall", envir=long.unit.names)
assign("sens", "Sensitivity", envir=long.unit.names)
assign("fnr", "False negative rate", envir=long.unit.names)
assign("tnr", "True negative rate", envir=long.unit.names)
assign("spec", "Specificity", envir=long.unit.names)
assign("ppv", "Positive predictive value", envir=long.unit.names)
assign("prec", "Precision", envir=long.unit.names)
assign("npv", "Negative predictive value", envir=long.unit.names)
assign("fall", "Fallout", envir=long.unit.names)
assign("miss", "Miss", envir=long.unit.names)
assign("pcfall", "Prediction-conditioned fallout", envir=long.unit.names)
assign("pcmiss", "Prediction-conditioned miss", envir=long.unit.names)
assign("rpp", "Rate of positive predictions", envir=long.unit.names)
assign("rnp", "Rate of negative predictions", envir=long.unit.names)
assign("auc","Area under the ROC curve", envir=long.unit.names)
assign("cal", "Calibration error", envir=long.unit.names)
assign("mwp", "Median window position", envir=long.unit.names)
assign("prbe","Precision/recall break-even point", envir=long.unit.names)
assign("rch", "ROC convex hull", envir=long.unit.names)
assign("mxe", "Mean cross-entropy", envir=long.unit.names)
assign("rmse","Root-mean-square error", envir=long.unit.names)
assign("phi", "Phi correlation coefficient", envir=long.unit.names)
assign("mat","Matthews correlation coefficient", envir=long.unit.names)
assign("mi", "Mutual information", envir=long.unit.names)
assign("chisq", "Chi-square test statistic", envir=long.unit.names)
assign("odds","Odds ratio", envir=long.unit.names)
assign("lift", "Lift value", envir=long.unit.names)
assign("f","Precision-Recall F measure", envir=long.unit.names)
assign("sar", "SAR", envir=long.unit.names)
assign("ecost", "Expected cost", envir=long.unit.names)
assign("cost", "Explicit cost", envir=long.unit.names)
## Define function names corresponding to the measure abbreviations.
function.names <- new.env()
assign("acc", ".performance.accuracy", envir=function.names)
assign("err", ".performance.error.rate", envir=function.names)
assign("fpr", ".performance.false.positive.rate", envir=function.names)
assign("tpr", ".performance.true.positive.rate", envir=function.names)
assign("rec", ".performance.true.positive.rate", envir=function.names)
assign("sens", ".performance.true.positive.rate", envir=function.names)
assign("fnr", ".performance.false.negative.rate", envir=function.names)
assign("tnr", ".performance.true.negative.rate", envir=function.names)
assign("spec", ".performance.true.negative.rate", envir=function.names)
assign("ppv", ".performance.positive.predictive.value",
envir=function.names)
assign("prec", ".performance.positive.predictive.value",
envir=function.names)
assign("npv", ".performance.negative.predictive.value",
envir=function.names)
assign("fall", ".performance.false.positive.rate", envir=function.names)
assign("miss", ".performance.false.negative.rate", envir=function.names)
assign("pcfall", ".performance.prediction.conditioned.fallout",
envir=function.names)
assign("pcmiss", ".performance.prediction.conditioned.miss",
envir=function.names)
assign("rpp", ".performance.rate.of.positive.predictions",
envir=function.names)
assign("rnp", ".performance.rate.of.negative.predictions",
envir=function.names)
assign("auc", ".performance.auc", envir=function.names)
assign("cal", ".performance.calibration.error", envir=function.names)
assign("prbe", ".performance.precision.recall.break.even.point",
envir=function.names)
assign("rch", ".performance.rocconvexhull", envir=function.names)
assign("mxe", ".performance.mean.cross.entropy", envir=function.names)
assign("rmse", ".performance.root.mean.squared.error",
envir=function.names)
assign("phi", ".performance.phi", envir=function.names)
assign("mat", ".performance.phi", envir=function.names)
assign("mi", ".performance.mutual.information", envir=function.names)
assign("chisq", ".performance.chisq", envir=function.names)
assign("odds", ".performance.odds.ratio", envir=function.names)
assign("lift", ".performance.lift", envir=function.names)
assign("f", ".performance.f", envir=function.names)
assign("sar", ".performance.sar", envir=function.names)
assign("ecost", ".performance.expected.cost", envir=function.names)
assign("cost", ".performance.cost", envir=function.names)
## If a measure comes along with an obligatory x axis (including "none"),
## list it here.
obligatory.x.axis <- new.env()
assign("mxe", "none", envir=obligatory.x.axis)
assign("rmse", "none", envir=obligatory.x.axis)
assign("prbe", "none", envir=obligatory.x.axis)
assign("auc", "none", envir=obligatory.x.axis)
assign("rch","none", envir=obligatory.x.axis)
## ecost requires probability cost function as x axis, which is handled
## implicitly, not as an explicit performance measure.
assign("ecost","none", envir=obligatory.x.axis)
## If a measure has optional arguments, list the names of the
## arguments here.
optional.arguments <- new.env()
assign("cal", "window.size", envir=optional.arguments)
assign("f", "alpha", envir=optional.arguments)
assign("cost", c("cost.fp", "cost.fn"), envir=optional.arguments)
assign("auc", "fpr.stop", envir=optional.arguments)
## If a measure has additional arguments, list the default values
## for them here. Naming convention: e.g. "cal" has an optional
## argument "window.size" the key to use here is "cal:window.size"
## (colon as separator)
default.values <- new.env()
assign("cal:window.size", 100, envir=default.values)
assign("f:alpha", 0.5, envir=default.values)
assign("cost:cost.fp", 1, envir=default.values)
assign("cost:cost.fn", 1, envir=default.values)
assign("auc:fpr.stop", 1, envir=default.values)
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)
}
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