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Var Imp Fil cluster.R
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Var Imp Fil cluster.R
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#options : spearman or person? mean or
#percent of all intersections above a set amount
#diagnol is rangeing data vs asis
S.pear<-"pearson"#"kendall","spearman"
use.mean<-F
min.corr<-.93#only if use.mean is false
#read file
VarImpResults<-data.frame()
VarImpResults<-(read.csv("importance2.csv", sep = ",",fill=TRUE, header = F,quote="",dec=".",stringsAsFactors=F))
u.learns<-unique(VarImpResults[,1])
u.gens<-unique(VarImpResults[,4])
#make 140x140x40 DF/array
pearson.array<-array(NA, c(length(u.learns), length(u.learns),
length(u.gens)))
#check redundancy
#for every generator and model
count.gen<-0
c.gen<-u.gens[9]
for(c.gen in (u.gens)){
count.gen<-1+count.gen
#new df of relevant rows
selVIRL<-as.logical(as.character(VarImpResults[,4])==c.gen)
selVIRL<-as.logical(selVIRL)
selVIR<-VarImpResults[selVIRL,5:13]
#######array fill########
#for every unique model
count.learn<-0
for(ev.learn in u.learns){
count.learn<-1+count.learn
selselVIR<-selVIR[as.logical(VarImpResults[selVIRL,1]==ev.learn),]
#for every other model
countt.learn<-0
for(ev2.learn in u.learns){
sssVIR<-selVIR[as.logical(VarImpResults[selVIRL,1]==ev2.learn),]
countt.learn<-1+countt.learn
#Test for fails
if(length(sssVIR[,])<1 || length(sssVIR[,1])<1) {next()}
if(length(selselVIR[,])<1 || length(selselVIR[,1])<1) {next()}
Lsss<-length(sssVIR[,1]);Lselsel<-length(selselVIR[,1])
#do not assume.be always 1vs1
sum.of.cors<-0
last.count<-0
if(ev.learn==ev2.learn){
for(n in 1:Lselsel){
for(g in 1:Lsss){
if(n==g){next()}
if(is.na(sssVIR[g,])||is.na(selselVIR[n,])){next()}
last.count<-last.count+1
sum.of.cors<-cor(as.numeric(sssVIR[g,]),as.numeric(selselVIR[n,]),method =S.pear)+sum.of.cors
}}
pearson.array[count.learn,countt.learn,count.gen]<-sum.of.cors/last.count
}else{
if(Lselsel==Lsss){
for(n in 1:Lselsel){
if(is.na(sssVIR[n,])||is.na(selselVIR[n,])){next()}
last.count<-last.count+1
sum.of.cors<-cor(as.numeric(sssVIR[n,]),as.numeric(selselVIR[n,]),method =S.pear)+sum.of.cors
}
pearson.array[count.learn,countt.learn,count.gen]<-sum.of.cors/last.count
}else{
every.intersection<-vector(mode = "numeric", length = Lselsel * Lsss)
for(n in 1:Lselsel){
for(g in 1:Lsss){
every.intersection[g+Lsss*(n-1)]<-cor(as.numeric(sssVIR[g,]),as.numeric(selselVIR[n,]),method =S.pear)
}
}
rank.inters<-rank(every.intersection)
min.accept<-Lselsel * Lsss-min(Lselsel,Lsss)
pearson.array[count.learn,countt.learn,count.gen]<-mean(every.intersection[rank.inters>min.accept],na.rm = T)
}
}
}}}
#write pearson to pearson array
#make 140x140DF p.a.1
out.array<-array(0, c(length(u.learns), length(u.learns)))
out.array.mean<-array(0, c(length(u.learns), length(u.learns)))
not.na.array<-array(0, c(length(u.learns), length(u.learns)))
#how often is each intersection true?
#for every model model
for(n in 1:length(u.learns)){
for(c in 1:length(u.learns)){
out.array.mean[n,c]=mean(pearson.array[n,c,], na.rm = T)
#for every generator
for(g in 1:length(u.gens)){
#sum Ts into 140xxdf
if(!is.na(pearson.array[n,c,g])){
not.na.array[n,c]=not.na.array[n,c]+1
if(pearson.array[n,c,g]>=min.corr)
{out.array[n,c]=out.array[n,c]+1}
}
}}}
colnames(out.array) <- u.learns
rownames(out.array) <- u.learns
colnames(out.array.mean) <- u.learns
rownames(out.array.mean) <- u.learns
#outr.array<-out.array/max(out.array)
#corrplot(out.array, method = "circle")
#stickyball selfsticing map
#clustering in other words
#############corrmat##########
if(T){
col1 <- colorRampPalette(c("#7F0000","red","#FF7F00","yellow","white",
"cyan", "#007FFF", "blue","#00007F"))
col2 <- colorRampPalette(c("#67001F", "#B2182B", "#D6604D", "#F4A582", "#FDDBC7",
"#FFFFFF", "#D1E5F0", "#92C5DE", "#4393C3", "#2166AC", "#053061"))
col3 <- colorRampPalette(c("red", "white", "blue"))
col4 <- colorRampPalette(c("#7F0000","red","#FF7F00","yellow","#7FFF7F",
"cyan", "#007FFF", "blue","#00007F"))
if(use.mean){
M<-out.array.mean}else{
M<-out.array/not.na.array}
library('corrplot') #package corrplot
#corrplot(M, method = "circle") #plot matrix
M[is.na(M)]<-0
(order.AOE <- corrMatOrder(M, order = "AOE"))
(order.FPC <- corrMatOrder(M, order = "FPC"))
(order.hc <- corrMatOrder(M, order = "hclust"))
(order.hc2 <- corrMatOrder(M, order = "hclust", hclust.method = "ward.D2"))
M.AOE <- M[order.AOE,order.AOE]
M.FPC <- M[order.FPC,order.FPC]
M.hc <- M[order.hc, order.hc]
M.hc2 <- M[order.hc2,order.hc2]
par(ask = F)
#corrplot(M)
#corrplot(M.AOE, is.corr=FALSE,addrect = 2, method="ellipse", col=col1(200), addCoef.col = "black")
corrplot(M.FPC, is.corr=FALSE,addrect = 2, method="ellipse", col=col1(200), addCoef.col = "black")
#corrplot(M.hc, is.corr=FALSE,addrect = 2, method="ellipse", col=col1(200), addCoef.col = "black")
#corrplot(M.hc2, is.corr=FALSE,addrect = 2, method="ellipse", col=col1(200), addCoef.col = "black")
}