Code to reproduce https://mbq.me/blog/yet-another-feature-filter-bundle

bench-plot.R

library(ggplot2)

readRDS('ans-ps.RDS')->ss
do.call(rbind,lapply(
 split(ss,paste(ss$met,ss$k)),
 function(S){
  median(S$time[S$p==1])->bl
  S$speedup<-bl/S$time
  S
 })
)->ss

do.call(rbind,lapply(
 split(ss,paste(ss$met,ss$p)),
 function(S){
  S$time<-median(S$time)
  S$speedup<-median(S$speedup)
  S[1,]
 })
)->ss

ss$Method<-toupper(ss$met)

scaleplot<-ggplot(ss,aes(y=speedup,x=p))+geom_line()+
 theme(legend.position="bottom")+
 ylab("Speed-up")+
 xlab("Number of threads")+
 facet_wrap(~Method,scales="free")
ggsave("praznik-scale.pdf",scaleplot)


readRDS('ans-pf.RDS')->cc
cc$Package<-cc$pkg
compplot<-ggplot(cc,aes(x=factor(k),col=Package,y=time/1e6))+
 geom_boxplot()+
 facet_wrap(~toupper(met),scales='free')+ylab("Execution time [ms]")+xlab("Number of features to return (k)")+theme(legend.position="bottom")
ggsave("feast-praznik.pdf",compplot)

bench.R

library(microbenchmark)
library(praznik) #CRAN/install_github("mbq/praznik")
library(feast) #install_github("mbq/feast_r")

data(MadelonD)

mets_p<-list(jmi=praznik::JMI,disr=praznik::DISR,jmim=praznik::JMIM,mim=praznik::MIM,njmim=praznik::NJMIM,cmim=praznik::CMIM,mrmr=praznik::MRMR)
mets_f<-list(jmi=feast::JMI,disr=feast::DISR,mim=feast::MIM,cmim=feast::CMIM,mrmr=feast::mRMR_D)

X<-MadelonD$X
Y<-MadelonD$Y
TIMES<-30

message("Praznik vs feast_r")
ans<-NULL
ks<-c(20,40)
#FEAST is single-threaded; make comparison fair
setOmpThreads(1)
for(k in ks){
 for(e in names(mets_f)){ #mets_f \in mets_p
  message(sprintf("Met %s, k=%d",e,k))
  rbind(
   ans,
   data.frame(
    met=e,
    k=k,
    p=1,
    pkg="praznik",
    time=microbenchmark(mets_p[[e]](X,Y,k),times=TIMES)$time
   ),
   data.frame(
    met=e,
    k=k,
    p=1,
    pkg="feast",
    time=microbenchmark(mets_f[[e]](X,Y,k),times=TIMES)$time
   )
  )->ans
 }
}
saveRDS(ans,file="ans-pf.RDS")

message("Praznik scaling")
ans<-NULL
for(p in 1:parallel::detectCores()){
 for(e in names(mets_p)){
  setOmpThreads(p)
  message(sprintf("Met %s, cores=%d",e,p))
  rbind(
   ans,
   data.frame(
    met=e,
    k=20,
    p=p,
    pkg="praznik",
    time=microbenchmark(mets_p[[e]](X,Y,20),times=TIMES)$time
   )
  )->ans
 }
}
saveRDS(ans,file="ans-ps.RDS")

demo.R

library(praznik)
data(MadelonD)
K<-list(
 DISR=DISR,MIM=MIM,MRMR=MRMR,JMI=JMI,
 JMIM=JMIM,NJMIM=NJMIM,CMIM=CMIM)
sapply(
 #Pull 20 features using each praznik method
 lapply(
  K,
  do.call,c(MadelonD,list(k=20))
 ),
 #Relevant features have >>Rel<< in name
 function(ans){
  good<-grepl("Rel",names(ans$selection))
  c(hits=sum(good),streak=rle(good)$len[1])
 }
)

##        DISR MIM MRMR JMI JMIM NJMIM CMIM
## hits     20  15    3  20   12    14   13
## streak   20  14    1  20   11    14   11