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step_analysis.R
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step_analysis.R
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source("./read_stepdata.R")
source("./functions.R")
dim(step) ## input: 1440 * 21394
#step=t(step)
### 79 individual
length(step.list)
num.day<-vector()
for(i in 1: length(step.list) ){
num.day[i]=ncol(step.list[[i]])
}
num.day
which.max(num.day)
range(num.day)
###################################################
############### Number of clusters #################
###################################################
Up_step = prepare_up(step,FUN=sqBound_es,t1,r1)
library(parallel)
ncore= detectCores()
pam1 <- function(x,k) list(cluster = pam(x,k,metric="manhattan",cluster.only=TRUE))
#gap.step= clusGap(t(log(Up_step)), pam1,10, B=10, do_parallel=T)
#saveRDS(gap.step,file="gap.step0223.rds")
gap.step=readRDS("gap.step0223.rds")
plot(gap.step)
K=6
########################################################
############## cv code for thickness ####################
########################################################
data=step
t1_index=c(10,20,30,50,100)
cv_error<-vector()
for(k in 1:length(t1_index)){
t1=t1_index[k]
r1=0.2
K_cv=5
folds <- cvFolds(ncol(data), K=K_cv)
temp_error_si<-vector()
for(i in 1:K_cv){
train <- data[,folds$subsets[folds$which != i] ] #Set the training set
validation <- data[,folds$subsets[folds$which == i] ] #Set the validation set
Up_step = prepare_up(train,FUN=sqBound_es,t1,r1)
a1= cluster_zits(K=K,Up=log(Up_step), FUN=l1_mean ,t1,r1,is.median=T)
for(l in 1:K){
a1$pivot[,l]= apply( log(Up_step)[ ,a1$clusters[[l]]],1, median)}
Up_test = prepare_up(validation,FUN=sqBound_es,t1,r1)
pmeasure=matrix(nrow=ncol(Up_test) , ncol=K)
for(clstr in 1:K){
for(j in 1:ncol(Up_test)){
pmeasure[j,clstr] = l1_mean(a1$pivot[,clstr],log(Up_test[,j]))
}
}
yhat=apply(pmeasure,1, which.min)
#assign each validation set to the pivots derived using train set
si2 <- silhouette((yhat), dist(t(validation), "euclidean"))
#for each fold, the silhouette values are saved in temp_error_si
temp_error_si[i]= summary(si2)$avg.width
}
cv_error[k]=mean(temp_error_si)
}
opt_k=which.max(cv_error)
opt_k
t1=t1_index[opt_k] #optimal thickness
Up_step = prepare_up(data,FUN=sqBound_es,t1,r1) ## Ensemble pen result
a1= cluster_zits(K=K,Up=log(Up_step), FUN=l1_mean ,t1,r1,is.median=T)
yhat=vector()
for(l in 1:K ){
yhat[a1$clusters[[l]]]=l
}
cv_result=yhat
################################################################
########################## Do Clustering ######################
################################################################
## various t values ####
t1_index=c(10,20,30,50,100)
result_step<-matrix(nrow=length(t1_index), ncol=ncol(step))
for(k in 1:length(t1_index)){
t1=t1_index[k]
r1=0.2
Up_step = prepare_up(step,FUN=sqBound_es,t1,r1)
a1= cluster_zits(K=K,Up=log(Up_step), FUN=l1_mean ,t1,r1,is.median=T)
yhat1=vector()
for(j in 1:K){
yhat1[a1$clusters[[j]]]=j
}
result_step[k,]=yhat1
}
table( result_step[k,])
dim(result_step)
save(result_step, file='step_result.RData')
######################## comparison ########################
################## Functional Clustering ######################
################################################################
basis <- create.fourier.basis(c(0, nrow(data)), nbasis=11)
fdobj <- smooth.basis(1:nrow(data), data,basis)$fd
cl.sinu = funFEM(fdobj, K=K, init="kmeans", model="all")
elements = list()
for(c in 1:K){
# get the point of cluster c
elements[[c]] <- which(cl.sinu$cls == c)
}
yhat_funfem=vector()
for(l in 1:K ){
yhat_funfem[elements[[l]]]=l
}
res.uni <- funHDDC(fdobj,K=K,model="AkBkQkDk",init="kmeans",threshold=0.2)
yhat_hddc= res.uni$class
### DTW cluster
sam_id=sample(ncol(data), 150)
pc.l2 <-tsclust(t(data[,sam_id]), k = K, distance = "dtw_basic", centroid = "pam",seed = 1, trace = FALSE, control = partitional_control(nrep = 1L))
yhat_dtw=cl_class_ids((pc.l2))
############### Plots for comparison methods ###############
tp = seq(as.POSIXct("00:00:00",format="%H:%M"),
as.POSIXct("23:59:00",format="%H:%M"), by = "1 min")
par(mfrow=c(1,2))
for(k in c(2,5)){
aa=(table(result_step[k,]))
plot( tp, apply(step[,which(result_step[k,]==1)],1, mean), type='n', ylab='', xlab='time', col=1,
main=paste('Thickness - ',t1_index[k] ), lwd=2, ylim=c(0,23))
#axis(side=1, at=seq(1, length(tp), length.out=7), labels=tp[seq(1, length(tp), length.out=7)])
for(j in 1:6){
lines( tp, apply(step[,which(result_step[k,]==order(aa, decreasing=T)[j])],1, mean), col=j,type='l',lwd=2 )
}
leg_tex<-vector()
for(j in order(aa, decreasing=T)){
leg_tex=c(leg_tex,aa[j])
}
legend('topright', paste('Group', c(1:length(aa)) ) , col=c(1:6), lty=1, lwd=2)
}
save(yhat_funfem, yhat_hddc,file='step_comparison_result.RData' )
png(file="./figure/step_com2.png",
width=1400, height=600)
par(mfrow=c(1,2))
aa=(table(yhat_funfem))
plot( tp, apply(step[,which(yhat_funfem ==1)],1, mean), type='n', ylab='', xlab='time', col=1,
main='FunFEM', lwd=2, ylim=c(0,30))
#axis(side=1, at=seq(1, length(tp), length.out=7), labels=tp[seq(1, length(tp), length.out=7)])
for(j in 1:6){
lines( tp, apply(step[,which(yhat_funfem ==order(aa, decreasing=T)[j])],1, mean), col=j,type='l',lwd=2 )
}
leg_tex<-vector()
for(j in order(aa, decreasing=T)){
leg_tex=c(leg_tex,aa[j])
}
legend('topright', paste('Group', c(1:length(aa)) ) , col=c(1:6), lty=1, lwd=2)
aa=(table(yhat_hddc))
plot( tp, apply(step[,which(yhat_hddc ==1)],1, mean), type='n', ylab='', xlab='time', col=1,
main='FunHDDC', lwd=2, ylim=c(0,30))
#axis(side=1, at=seq(1, length(tp), length.out=7), labels=tp[seq(1, length(tp), length.out=7)])
for(j in 1:6){
lines( tp, apply(step[,which(yhat_hddc ==order(aa, decreasing=T)[j])],1, mean), col=j,type='l',lwd=2 )
}
leg_tex<-vector()
for(j in order(aa, decreasing=T)){
leg_tex=c(leg_tex,aa[j])
}
legend('topright', paste('Group', c(1:length(aa)) ) , col=c(1:6), lty=1, lwd=2)
aa=(table(yhat_dtw))
plot( tp, apply(step[,sam_id][,which(yhat_dtw ==1)],1, mean), type='n', ylab='', xlab='time', col=1,
main='DTW', lwd=2, ylim=c(0,30))
#axis(side=1, at=seq(1, length(tp), length.out=7), labels=tp[seq(1, length(tp), length.out=7)])
for(j in order(aa, decreasing=T)){
lines( tp, apply(step[,sam_id][,which(yhat_dtw ==j)],1, mean), col=j,type='l',lwd=2 )
}
leg_tex<-vector()
for(j in order(aa, decreasing=T)){
leg_tex=c(leg_tex,aa[j])
}
legend('topright', paste('Group', c(1:length(aa)) ) , col=order(aa, decreasing=T), lty=1, lwd=2)
dev.off()
############### Table 4 ###############
weekend.percent = matrix(NA,nrow=length(t1_index),ncol=6)
for(k in c(2,5)){
numstep=c()
aa=(table(result_step[k,]))
for(j in order(aa, decreasing=T)){
numstep = c(numstep, mean(apply(step[,which(result_step[k,]==j)],2,sum) ) )
weekend.percent[k,j]=(round( length(which(is.wkend [which(result_step[k,]==j) ] ==T) )/ length(which(result_step[k,]==j))*100,1))
}
print( (table(result_step[k,]))[order(aa, decreasing=T) ] )
print(round( numstep),2)
print(weekend.percent[k,order(aa, decreasing=T) ])
}
########## Only one person ########## ########## ##########
library(clusterSim)
dim(step.list[[67]])
for(tau in c(20,100)){
Up_step = prepare_up(step.list[[67]],FUN=sqBound_es,t1,r1) ## Ensemble pen result
fun.step <- function(x,k, t1=tau ){
print(k)
a = cluster_zits(K=k,Up=x, FUN=l1_mean ,t1,r1=0.2,is.median=T)
clusters = rep(NA, ncol(step.list[[67]]))
for(c in 1:length(a$clusters)){
clusters[a$clusters[[c]]]<-c
}
return(list(cluster = clusters))
}
gap.step= clusGap(Up_step,fun.step,10, B=20)
plot(gap.step)
}
############### Plot ###############
ID=67
order(table(people.save))
table(is.wkend[which(people.save==ID)])
tp = seq(as.POSIXct("00:00:00",format="%H:%M"),
as.POSIXct("23:59:00",format="%H:%M"), by = "1 min")
par(mfrow=c(2,2))
plot( tp, apply(step.list[[ID]][,which(is.wkend[which(people.save==ID)]==F) ],1, mean), type='l', ylab='', xlab='time', col=1,
main='Weekday average', lwd=2, ylim=c(0,25))
plot( tp, apply(step.list[[ID]][,which(is.wkend[which(people.save==ID)]==T) ],1, mean), type='l', ylab='', xlab='time', col=1,
main='Weekend average', lwd=2, ylim=c(0,25))
order( apply(step.list[[ID]],2, sum) )
which.max( apply(step.list[[ID]],2, sum) )
#par(mfrow=c(1,2))
plot( tp, step.list[[ID]][,order( apply(step.list[[ID]],2, sum) )[2] ], type='l', ylab='', xlab='time', col=1,
main='Minimum activity day', lwd=2 , ylim=c(0,120))
plot( tp, step.list[[ID]][,which.max( apply(step.list[[ID]],2, sum) ) ], type='l', ylab='', xlab='time', col=1,
main='Maximum activity day', lwd=2)
K=4
t1_index=c(10,20,30,50,100)
result_1_step<-matrix(nrow=length(t1_index), ncol=ncol(step.list[[67]]))
for(k in c(2,5)){
t1=t1_index[k]
r1=0.2
Up_step = prepare_up(step.list[[67]],FUN=sqBound_es,t1,r1) ## Ensemble pen result
a1= cluster_zits(K=K,Up=log(Up_step), FUN=l1_mean ,t1,r1,is.median=T)
yhat1=vector()
for(j in 1:K){
yhat1[a1$clusters[[j]]]=j
}
result_1_step[k,]=yhat1
}
table( result_1_step[k,])
dim(result_1_step)
############### Plot 67th ###############
tp = seq(as.POSIXct("00:00:00",format="%H:%M"),
as.POSIXct("23:59:00",format="%H:%M"), by = "1 min")
par(mfrow=c(1,K))
for(k in c(5)){
aa=(table(result_1_step[k,]))
for(j in 1:K){
plot( tp, apply(step.list[[67]][,which(result_1_step[k,]==j)],1, mean), type='l', ylab='', xlab='time', col=1,
main=paste('Group - ',j, '(',aa[j] ,')' ), lwd=2, ylim=c(0,26))
}
#legend('topright', paste(leg_tex), col=order(aa, decreasing=T), lty=1, lwd=2)
}
par(mfrow=c(1,2))
for(k in c(2,5)){
aa=(table(result_1_step[k,]))
plot( tp, apply(step.list[[67]][,which(result_1_step[k,]==1)],1, mean), type='l', ylab='', xlab='time', col=1,
main=paste('Thickness - ',t1_index[k] ), lwd=2, ylim=c(0,30))
#axis(side=1, at=seq(1, length(tp), length.out=7), labels=tp[seq(1, length(tp), length.out=7)])
for(j in order(aa, decreasing=T)){
lines( tp, apply(step.list[[67]][,which(result_1_step[k,]==j)],1, mean), col=j,type='l',lwd=2 )
}
leg_tex<-vector()
for(j in order(aa, decreasing=T)){
leg_tex=c(leg_tex,aa[j]) }
legend('topright', paste(leg_tex), col=order(aa, decreasing=T), lty=1, lwd=2)
}
########
#Exploratory plots
########
# heatmap
people = c()
for(i in 1:length(step.list)){
n=0
for(p in step.list[[i]]){ n = n + length(p)/1440 }
people=c(people,rep(i,n))
}
people.save=people
group.by.ppl = matrix(0,ncol=length(step.list), nrow=K)
ttt=0
aa= (table(result_step[2,]))
for(i in order(aa, decreasing=T)){
ttt=ttt+1
e30=which(result_step[2,]==i)
group.by.ppl[ttt,as.integer(names(table(people[e30])))] = table(people[e30])
}
ttt=0
aa= (table(result_step[5,]))
group.by.ppl2 = matrix(0,ncol=length(step.list), nrow=K)
for(i in order(aa, decreasing=T)){
ttt=ttt+1
e100=which(result_step[5,]==i)
group.by.ppl2[ttt,as.integer(names(table(people[e100])))] = table(people[e100])
}
par(mfrow=c(1,2))
par(mar=c(3.5,3.5,1.5,1))
par(oma=c(3,2,1,1.5))
par(mgp=c(2,.5,0))
image(group.by.ppl,axes = F,ylab="individual",xlab="(a) thickness 20")
axis(1, at=seq(0,1,0.2), labels=sprintf("group%d",1:K))
image(group.by.ppl2,axes = F,xlab="(b) thickness 100")
axis(1, at=seq(0,1,0.2), labels=sprintf("group%d",1:K))
par(mfrow=c(1,2))
case= order( apply(step.list[[67]][1:(60*3),],2,sum) , decreasing = T)[1]
#case= order( apply(step.list[[67]],2,sum) )[32]
# plot( tp, step.list[[67]][,case] , type='l', ylab='', xlab='time', col=1, main='', lwd=1)
plot( tp, step.list[[67]][,case] , type='l', ylab='', xlab='time', col=1,
main='', lwd=1, ylim=c(-10,180))
polygon( c(tp,rev(tp)) ,c(sqBound_es(step.list[[67]][,case], 20, max), rep(0, 1440)),col="skyblue" )
lines(tp,step.list[[67]][,case],type='l')
plot( tp, step.list[[67]][,case] , type='l', ylab='', xlab='time', col=1,
main='', lwd=1, ylim=c(-10,180))
polygon( c(tp,rev(tp)) ,c(sqBound_es(step.list[[67]][,case], 100, max), rep(0, 1440)),col="skyblue" )
lines(tp,step.list[[67]][,case],type='l')
############### cluster validation ###############
clust_val<-matrix(nrow=4, ncol=4)
df2=dist(t(data))
for(j in 1:2){
clust_stats <- cluster.stats(df2, result_step[c(2,5)[j],] )
clust_val[j,]=round(c( clust_stats $dunn, clust_stats$avg.silwidth, clust_stats $entropy ,clust_stats $ch),3)
}
clust_stats <- cluster.stats((df2), yhat_funfem)
clust_val[3,]=round(c( clust_stats $dunn, clust_stats$avg.silwidth, clust_stats $entropy,clust_stats $ch ),3)
clust_stats <- cluster.stats((df2), yhat_hddc)
clust_val[4,]=round(c( clust_stats $dunn, clust_stats$avg.silwidth, clust_stats $entropy,clust_stats $ch ),3)
colnames(clust_val)=c('Dunn','silwidth','entropy','CH')
rownames(clust_val)=c('tau20','tau100','FEM','HDDC')
clust_val