Timing a Neural Net Scoring Process.R

library(nnet)
library(ggplot2)
 
 
neuralNetScoreTime<-function(var, score){
 
modelset<-NULL
  for (i in 1:var) {
    eval(parse(text=paste0("temp.", i, "<-rnorm(150, mean=1, sd=1)")))
    eval(parse(text=paste0("modelset<-cbind(modelset, temp.", i, ")"))) 
  }
target<-c(rep("No",50), rep("Yes",50), rep("No",50))
modelset<-data.frame(modelset, target)
 
ir1 <- nnet(form=target ~ .,data=modelset, size = 10, rang = 0.1, decay = 5e-4, maxit = 200)
 
scoreset<-NULL
for (i in 1:var) {
  eval(parse(text=paste0("temp.", i, "<-rnorm(", score, ", mean=1, sd=1)")))
  eval(parse(text=paste0("scoreset<-cbind(scoreset, temp.", i, ")"))) 
}
 
start<-Sys.time()
prop<-predict(ir1, scoreset, type = "raw")
class<-predict(ir1, scoreset, type = "class")
pred<-cbind(as.numeric(prop), class)
end<-Sys.time()
return(end-start)
}
 
neuralNetScoreTime(5,10000)
 
 
final<-NULL
for (vars in seq(5, 100, 5)){
  for (scores in c(100, 1000, 10000, 100000, 1000000, 10000000)){
 
    time<-neuralNetScoreTime(vars,scores)
    temp<-c(vars, scores, time)
    final<-rbind(final, temp)
  }
}
final<-data.frame(final)
colnames(final)<-c("vars", "scores", "time")
final
 
ggplot(final, aes(x=vars, y=time, group=scores)) + geom_line(aes(colour = scores)) + ylab("Time (s)") +
  xlab("Number of Variables in Neural Net") + labs(colour = "Records Scored")