diff options
Diffstat (limited to 'analysis/days.R')
| -rw-r--r-- | analysis/days.R | 77 |
1 files changed, 66 insertions, 11 deletions
diff --git a/analysis/days.R b/analysis/days.R index 2b671d1..2155819 100644 --- a/analysis/days.R +++ b/analysis/days.R @@ -94,24 +94,41 @@ generate_accuracy_for=function(ignore_hint=FALSE,seed_max=200,attempts_max=2,wrl } -generate_accuracy = function(wireless,wakeupfor,steps=10, accuracy=10,ignore_hint=TRUE){ +generate_accuracy_energy = function(wireless,wakeupfor,steps=10, accuracy=10,ignore_hint=TRUE){ npolicies=4 - if(ignore_hint){npolicies=npolicies-1} + data_seed_for_energy=data_seed%>%ungroup() + if(ignore_hint){npolicies=npolicies-1;data_seed_for_energy=data_seed%>%filter(simkey!="hint")} + data_seed_for_energy=data_seed_for_energy%>%filter(wireless==!!wireless,wakeupfor==!!wakeupfor) ## Generate inputs result=tibble() + result_energy=tibble() for(i in seq(1,160,by=steps)){ # We stop at 80% of the data (this way test set is at least 20%) acc=generate_accuracy_for(ignore_hint=ignore_hint,seed=i,attempts_max=accuracy,wrl=wireless,wuf=wakeupfor) + result_energy=rbind(result_energy,data_seed_for_energy%>%filter(seed<=i)%>%summarize(energy=sum(energy),seed_max=i,days=i*npolicies,months=days/30,setup=paste0(!!wireless," ",!!wakeupfor,"s"),wireless=!!wireless,wakeupfor=!!wakeupfor)) result=rbind(result,acc) } - result%>%mutate(days=seed_max*npolicies,months=days/30) # Since 3 policies (since ignore_hint=TRUE) + list(accuracy=result%>%mutate(days=seed_max*npolicies,months=days/30), # Since 3 policies (since ignore_hint=TRUE) + energy=result_energy) } +result_energy_policy=rbind(data_seed, + data_seed%>%mutate(seed=seed+200), + data_seed%>%mutate(seed=seed+400), + data_seed%>%mutate(seed=seed+600)) # Almost same as if each experiment run 4 times more seed seed +result_energy_policy=result_energy_policy%>%group_by(wireless,wakeupfor,simkey)%>%mutate(energy=cumsum(energy),setup=paste0(wireless," ",wakeupfor,"s"),days=seed,months=days/30) + # Generate accuracy for each wireless and uptime if(F){ # Toggle to train -accuracy=rbind(generate_accuracy("lora",60), - generate_accuracy("lora",180), - generate_accuracy("nbiot",60), - generate_accuracy("nbiot",180)) + lora60=generate_accuracy_energy("lora",60) + lora180=generate_accuracy_energy("lora",180) + nbiot60=generate_accuracy_energy("nbiot",60) + nbiot180=generate_accuracy_energy("nbiot",180) + accuracy=rbind(lora60$accuracy,lora180$accuracy,nbiot60$accuracy,nbiot180$accuracy) + energy=rbind(lora60$energy, + lora180$energy, + nbiot60$energy, + nbiot180$energy) +# energy$setup=factor(energy$setup,levels=c("lora 60s","nbiot 60s","lora 180s", "nbiot 180s"),ordered=TRUE) } ## Summarize result_summary=accuracy%>%group_by(wireless,wakeupfor,months,model)%>% @@ -127,6 +144,44 @@ metrics_peak=result_summary%>%group_by(wireless,wakeupfor,model)%>% summarize(max_accuracy=max(mean_accuracy)) + + + + +result_energy_policy=result_energy_policy%>%filter(days %in% !!energy$days) +csv_table=result_energy_policy%>% + full_join(energy,by=c("days","wireless","wakeupfor"),suffix=c("","_training")) +csv_table=csv_table%>%group_by(wireless,wakeupfor)%>%summarize(delta=energy-energy_training,simkey=simkey,days=days,months=months) + + +write("wireless,wakeupfor,policy,slope,intercept","figures/delta_energy.csv") +csv_table%>%group_by(wireless,wakeupfor,simkey)%>%group_walk(function(data,grp){ + grp=as.list(grp) + reg=lm(delta/1e3 ~ months,data) + slope=round(as.numeric(reg$coefficients["months"]),digits=1) + intercept=round(as.numeric(reg$coefficients[1]),digits=1) + print(paste0("Wireless=",grp$wireless," Wakeupfor=",grp$wakeupfor," Policy=",grp$simkey," Slope=",slope," Intercept=",intercept)) + write(paste(grp$wireless,grp$wakeupfor,grp$simkey,slope,intercept,sep=","),"figures/delta_energy.csv",append=T) +}) + + +ggplot(csv_table,aes(months,delta/1e3,color=simkey,shape=simkey))+ + geom_line(size=1.2)+geom_point(size=3)+ylab("Delta in energy (kJ)")+xlab("Training months")+ + facet_wrap(~wireless+wakeupfor,scale="free")+ + scale_color_viridis(discrete=TRUE,option="H",end=0.95) + +stopifnot(1) + +ggplot(data=energy,aes(months,energy/1e6,group=setup,fill=setup))+ + geom_bar(stat="identity",position="dodge")+ + scale_fill_viridis(discrete=TRUE,option="D")+ + labs(fill="Nodes wireless technology and uptime")+theme(legend.position=c(0.2,0.75))+ + scale_x_continuous(breaks = seq(0, 15, by = 1))+ + xlab("Number of months")+ylab("Energy consumption (MJ)")+ + geom_point(data=result_energy_policy%>%filter(simkey=="hintandextended"),aes(months,energy/1e6,group=setup,color=simkey),size=1)+facet_wrap(~wireless+wakeupfor) +ggsave("figures/months_energy.pdf",width=8.5,height=4) + + ggplot(data=result_summary%>%mutate(model=ifelse(model=="knn","KNN","DT")),aes(linetype=model))+ geom_line(aes(months,mean_f1_baseline,color="Baseline"),size=1.2)+ geom_line(aes(months,mean_f1_extended,color="Extended"),size=1.2)+ @@ -144,11 +199,11 @@ ggplot(data=result_summary%>%mutate(model=ifelse(model=="knn","KNN","DT")),aes(l xlab("Training months")+ylab("Classes F1-Score")+ scale_color_viridis(discrete=TRUE,end=0.7) ggsave("figures/months_f1-score.pdf",width=8.5,height=6) -stopifnot(1) + ## Plot Merge Accuracy -ggplot(data=result_summary%>%mutate(model=ifelse(model=="knn","KNN","DT")),aes(months,mean_accuracy,color=model,shape=model))+ - geom_point(size=3)+geom_line(size=1.2)+xlab("Training months")+ylab("Model accuracy")+labs(color="Models",shape="Models")+ +ggplot(data=result_summary%>%mutate(model=ifelse(model=="knn","KNN","DT")),aes(months,mean_accuracy))+ + geom_line(aes(linetype=model),size=1.2)+xlab("Training months")+ylab("Model accuracy")+labs(linetype="Models")+ scale_x_continuous(breaks = seq(0, 15, by = 1))+ scale_y_continuous(breaks = seq(0, 1, by = 0.1))+ facet_wrap(~wireless+wakeupfor)+ @@ -158,7 +213,7 @@ ggplot(data=result_summary%>%mutate(model=ifelse(model=="knn","KNN","DT")),aes(m legend.background = element_rect(fill = "white", color = "black",size=0.8))+ scale_color_viridis(discrete=TRUE,end=0.7) ggsave("figures/months_accuracy.pdf",width=8.5,height=6) - +stopifnot(1) ## Plot accuracy + F1-Score sapply(c("knn","tree"),function(grp){ |