R/camel.tiger.roc.R

In camel: Calibrated Machine Learning

#-----------------------------------------------------------------------------------#
# Package: camel                                                                    #
# camel.tiger.roc(): Draw ROC Curve for a solution path                             #
#         The ground truth is required                                              #
# Author: Xingguo Li                                                                #
# Email: <xingguo.leo@gmail.com>                                                    #
# Date: Aug 23th, 2013                                                              #
# Version: 0.1.0                                                                    #
#-----------------------------------------------------------------------------------#

camel.tiger.roc <- function(path, theta, verbose = TRUE){
  gcinfo(verbose = FALSE)	
  ROC = list()
  
  theta = as.matrix(theta)
  d = ncol(theta)
  pos.total = sum(theta!=0)
  neg.total = d*(d-1) - pos.total
  
  if(verbose) cat("Computing F1 scores, false positive rates and true positive rates....")
  ROC$tp = rep(0,length(path))
  ROC$fp = rep(0,length(path))
  ROC$F1 = rep(0,length(path))
  for (r in 1:length(path)){
    tmp = as.matrix(path[[r]]) 
    tp.all = (theta!=0)*(tmp!=0)
    diag(tp.all) = 0
    ROC$tp[r] <- sum(tp.all!=0)/pos.total
    fp.all = (theta==0)*(tmp!=0)
    diag(fp.all) = 0
    ROC$fp[r] <- sum(fp.all!=0)/neg.total
    
    fn = 1 - ROC$tp[r]
    precision = ROC$tp[r]/(ROC$tp[r]+ROC$fp[r])
    recall = ROC$tp[r]/(ROC$tp[r]+fn)
    ROC$F1[r] = 2*precision*recall/(precision+recall)
    if(is.na(ROC$F1[r]))	ROC$F1[r] = 0
  }
  if(verbose) cat("done.\n")
  
  rm(precision,recall,tp.all,fp.all,path,theta,fn)
  gc()	
  
  ord.fp = order(ROC$fp)
  
  tmp1 = ROC$fp[ord.fp]
  tmp2 = ROC$tp[ord.fp]
  par(mfrow = c(1,1))	
  plot(tmp1,tmp2,type="b",main = "ROC Curve", xlab = "False Postive Rate", ylab = "True Postive Rate",ylim = c(0,1))
  ROC$AUC = sum(diff(tmp1)*(tmp2[-1]+tmp2[-length(tmp2)]))/2
  
  rm(ord.fp, tmp1, tmp2)
  gc()
  class(ROC) = "roc"
  return(ROC)
}

print.roc = function(x, ...){
  cat("True Postive Rate: from",min(x$tp),"to",max(x$tp),"\n")
  cat("False Positive Rate: from",min(x$fp),"to",max(x$fp),"\n")
  cat("Area under Curve:",x$AUC,"\n")
  cat("Maximum F1 Score:",max(x$F1),"\n")
}

plot.roc = function(x, ...){	
  ord.fp = order(x$fp)
  par(mfrow = c(1,1))
  plot(x$fp[ord.fp],x$tp[ord.fp],type="b",main = "ROC Curve", xlab = "False Postive Rate", ylab = "True Postive Rate",ylim = c(0,1))
}