####################################################################
#Kendall Curves (score driven threshold)
#Inputs: predictions, classes ,loss2skew, ...
#predictions: list of Scores array values
#classes: list of class boolean array
#uniquec: option to use the same array classes for each predictions
#array in a list.
#loss2skew: TRUE, FALSE, NULL. It's TRUE ploting loss by Skew otherwise
#ploting loss by cost.
#... : plot options (hold, gridOFF, pointsOFF, legendOFF,
# main, xlab, ylab, nameClassifiers lwd, lty, col, pch, cex...)
####################################################################
KendallCurves=function(predictions,classes,uniquec=FALSE, loss2skew=FALSE, hold=FALSE,
plotOFF=FALSE, gridOFF=TRUE, pointsOFF=TRUE, legendOFF=FALSE,
main, xlab, ylab, namesClassifiers, lwd, lty, col, pch, cex,
xPosLegend,yPosLegend,cexL){
####################################################################
Np =length(predictions)
if ((typeof(predictions)!="list")||(typeof(classes)!="list")){
stop("Predictions type and classes type must be a list")
}
if(!exists("TP_FP.rates", mode="function")) source("TP_FP.rates.R")
if(missing(col)){col=c("orange", cm.colors((Np-1)*2)[-seq((Np-1))])}
if(missing(pch)){pch=sample(c(0,1,2,5,6,c(15:25)),Np, replace=F)}
if(missing(lwd)){lwd=2}
if(missing(lty)){lty=rep(1, Np)}
if(missing(cex)){cex=1.2}
if (length(lty)==1){lty=rep(lty[1], Np)}
if (length(pch)==1){pch=rep(pch[1], Np)}
if (length(col)==1){col=rep(col[1], Np)
if (Np>1){
warning(
"You have more than one curve to plot,
you should define other colors to visualize curves better")}}
if(missing(ylab)){ylab="Loss"}
if(missing(xPosLegend)){xPosLegend=0.7}
if(missing(yPosLegend)){yPosLegend=0.97}
if(missing(cexL)){cexL=0.75}
if(plotOFF==FALSE){
if(hold==FALSE){plot.new()
plot.window(xlim=c(0,1),ylim=c(0,1),xaxs="i", yaxs="i");
axis(1, at=seq(from = 0, to = 1, by = 0.1));
axis(2,at=seq(from = 0, to = 1, by = 0.1));
box();
if(gridOFF==FALSE){
grid(nx = 10, ny =10,col = "lightgray", lty = "dotted",
lwd = par("lwd"), equilogs = TRUE)}}}
if(missing(namesClassifiers)){
namesClassifiers=NULL
for (i in seq(Np)){namesClassifiers=c(namesClassifiers,
paste("C", i, sep=""))}}
####################################################################
result=c(NULL)
nameslegend <- c(NULL)
namesResult <- c(NULL)
for (pred in seq(predictions)){
S<-unlist(predictions[pred])
if (uniquec==TRUE) {
c<-unlist(classes)}
else{
if (length(predictions)!=length(classes)){
stop ("prediction list and classes list may have the same length")}
else{c<-unlist(classes[pred])}
}
rates<-TP_FP.rates(S,c)
TP<-rates[,2]# F0 o valores de Y de Curva ROC
FP<-rates[,1]# F1 o valores de X de curva ROC
pd=seq(0,1, length.out = length(S)+1)
pd=c(pd[which(!duplicated(sort(S)))],1)
y<-c(NULL)
AUC=0
####################################################################
#Loss by Cost Curve
#loss2skew=NULL or FALSE
####################################################################
if(loss2skew==FALSE){
if (plotOFF==FALSE){if(missing(main)){main="Loss by Cost"}
if(missing(xlab)){xlab="Cost"}
title(main=main,xlab=xlab,ylab=ylab,font.main= 14)}
pi0=sum(c==0)/length(c);
pi1=1-pi0;
for (i in 1:(length(pd))){
if (signif(pd[i],digits = 6)<=signif(pi0,digits=6)){
y=c(y,2*pi1*FP[i])
}else{
y=c(y,2*(pi0*(1-TP[i])))
}
} }
####################################################################
#Loss by Skew Curve
#loss2skew=TRUE
####################################################################
else{
if (plotOFF==FALSE){
if(missing(main)){main="Loss by Skew"}
if(missing(xlab)){xlab="Skew"}
title(main=main,xlab=xlab,ylab=ylab,font.main= 14)}
for (i in 1:(length(pd))){
if (signif(pd[i],digits = 6)<=signif(0.5,digits=6)){
y=c(y,FP[i])
}else{
y=c(y,(1-TP[i]))
}
}}
AUC=round(sum(AUC+auc(pd,y, dens=1000)), 3)
if(plotOFF==FALSE){
if(pointsOFF==FALSE)
{points(pd,y,col=col[pred],pch =pch[pred],cex=cex)}
lines(pd, y,col=col[pred], lwd=lwd, lty=lty[pred])}
nameslegend = c(nameslegend, paste(namesClassifiers[pred], "AUKC:",
AUC, sep=" "))
namesResult = c(namesResult, paste(namesClassifiers[pred], "AUKC:", sep=" "))
result=c(result, AUC)
}
#Legend
if(plotOFF==FALSE){
if(legendOFF == FALSE){
legend(xPosLegend, yPosLegend, nameslegend, lty = lty, col = col,cex=cexL,
y.intersp=0.7, x.intersp=0.3, bty="n"); box()
}}
names(result)<-namesResult
return(result)
}
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