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Fast AUC and ROC calculations in R
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fastROC <- function(probs, class) { | |
class_sorted <- class[order(probs, decreasing=T)] | |
TPR <- cumsum(class_sorted) / sum(class) | |
FPR <- cumsum(class_sorted == 0) / sum(class == 0) | |
return(list(tpr=TPR, fpr=FPR)) | |
} | |
# Helpful function adapted from: https://stat.ethz.ch/pipermail/r-help/2005-September/079872.html | |
fastAUC <- function(probs, class) { | |
x <- probs | |
y <- class | |
x1 = x[y==1]; n1 = length(x1); | |
x2 = x[y==0]; n2 = length(x2); | |
r = rank(c(x1,x2)) | |
auc = (sum(r[1:n1]) - n1*(n1+1)/2) / n1 / n2 | |
return(auc) | |
} | |
#################################################### | |
### Some tests on random datasets | |
#small test dataset | |
probs <- runif(50000) | |
class <- sample(c(1,0), 50000, replace=T) | |
system.time(pROC_auc_results <- pROC::auc(pROC::roc(class, probs))) | |
#Elapsed: 17.906s | |
system.time(fast_auc_results <- fastAUC(probs, class)) | |
#Elapsed: 0.022s | |
system.time(ROCR_auc_results <- ROCR::performance(ROCR::prediction(probs,class), measure="auc")) | |
#Elapsed: 0.208s | |
#Check that the results are the same -- True up to some float precision | |
abs(as.numeric(pROC_auc_results) - fast_auc_results) < 1e-12 | |
abs(ROCR_auc_results@y.values[[1]] - fast_auc_results) < 1e-12 | |
#Larger test dataset | |
probs <- runif(5e6) | |
class <- sample(c(1,0), 5e6, replace=T) | |
probs <- ifelse(class==1, probs+0.01, probs-0.01) | |
system.time(fast_auc_results <- fastAUC(probs, class)) | |
#Elapsed: 3.687s | |
system.time(ROCR_auc_results <- ROCR::performance(ROCR::prediction(probs,class), measure="auc")) | |
#Elapsed: 16.006 | |
#Check that the results are the same -- True up to some float precision | |
abs(ROCR_auc_results@y.values[[1]] - fast_auc_results) < 1e-12 | |
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