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diff --git a/analysis/in-situ.R b/analysis/in-situ.R new file mode 100644 index 0000000..2d57666 --- /dev/null +++ b/analysis/in-situ.R @@ -0,0 +1,257 @@ +########## INFORMATIONS ########## +# This file is made to study online classification +# So, each pair (wireless,wakeupfor) has its classification models (knn and decision tree) +# Note that is the following error appears: object 'accuracy' not found +# it means you should toggle the boolean in the if condition in the code to generate the accuracy onbject +################################## + +library("tidyverse") +options(dplyr.summarise.inform = FALSE) +library("class") +library("rpart") +library("rpart.plot") +library("viridis") +library("MLmetrics") +library("latex2exp") + +## Simulation Parameters: +## simkey {baseline,extended,hint,hintandextended} +## wireless {lora,nbiot} +## wakeupfor {60s,180s} +## seed [1,200] +## node on[0,12] +## isSender {0,1} +## dataSize {1MB} + +## Metrics: +## energy [0,+inf) +## nDataRcv [0,+inf) + +## WARNING: Goto line 138 first and set the boolean to T (populate the R environment with accuracy results) afterwhich you can set it to FALSE (save time) + +nseed=200 +nwakeupfor=2 +nwireless=2 +nsimkey=4 +nsimulations=nseed*nwakeupfor*nwireless*nsimkey # Must be 3200 + +## Load data and prepare the data +data=suppressMessages(read_csv("../CCGRID2022.csv"))%>%distinct() # Note that in the data experiment wireless=="lora",seed==1,wakeupfor==60,simkey=="baseline" is present 2 times in the CSV file +data_seed=data%>%group_by(simkey,wireless,wakeupfor,seed)%>%summarize(energy=sum(energy),coverage=sum(nDataRcv))%>% + mutate(efficiency=energy/coverage)%>% + ungroup() + +## F1_Score of multiclass vectors +F1_Score2=function(truth, pred){ + result=sapply(c("baseline","extended","hint","hintandextended"),function(c){ + cur_truth=truth[truth==c] + cur_pred=pred[truth==c] + col=paste0("f1_",c) + score=F1_Score(cur_truth,cur_pred) + if(is.nan(score)){score=0} + list(tibble(!!col:=score)) + }) + do.call("cbind",result) +} + +## Down scale data +reduce_days=function(data,every=6){data%>%filter(((days/4) %% every) == 0)} + +## GGPlot Theme +th = function(option="D") {list(theme_bw(), + theme(legend.box.background = element_rect(fill = "white", color = "black",size=0.9), + strip.background =element_rect(fill="#F5F5F5")), + scale_color_viridis(discrete=TRUE,option=option,end=0.95), + scale_fill_viridis(discrete=TRUE,option=option,end=0.95))} + +## Train models +generate_accuracy_for=function(ignore_hint=FALSE,seed_max=200,attempts_max=2,wrl="lora",wuf=180) { + attempts=seq(1,attempts_max) + results=sapply(attempts,function(attempt){ + ## Prepare data for traning + set.seed(1+attempt) # Reproducibility + wireless_map=c("lora"=1,"nbiot"=2) + cur_data_seed=data_seed%>%filter(wakeupfor==wuf,wireless==wrl) + data_ml=cur_data_seed%>%select(-efficiency)%>%mutate(wireless=wireless_map[cur_data_seed$wireless]) + if(ignore_hint){ + data_ml=data_ml%>%filter(simkey!="hint") + } + train_set=data_ml%>%filter(seed<=seed_max)%>%select(-seed) # train data on seed_max*3 days + test_set=data_ml%>%select(-seed) # build test_set + #print(paste0("Test set ",NROW(test_set))) + #print(paste0("Train set ",NROW(train_set))) + ## KNN training + knn_predictions=knn(train=train_set%>%select(-simkey),test=test_set%>%select(-simkey),cl=train_set$simkey,k=min(10,NROW(train_set))) + ## KNN analysis + knn_cont_table=table(knn_predictions,test_set$simkey) + knn_accuracy=(sum(diag(knn_cont_table)/sum(rowSums(knn_cont_table)))) + #print(knn_accuracy) + knn_prop_table=round(prop.table(knn_cont_table),digits=2) + knn_f1_score=F1_Score2(test_set$simkey,knn_predictions) + + ## Decision tree + tree=rpart( + simkey ~ wireless + wakeupfor + energy + coverage, + data=train_set, + method="class", + minsplit=60, + minbucket=1) + tree_predictions=predict(tree,newdata=test_set%>%select(-simkey),type="class") + tree_cont_table=table(tree_predictions,test_set$simkey) + tree_accuracy=(sum(diag(tree_cont_table)/sum(rowSums(tree_cont_table)))) + tree_prop_table=round(prop.table(tree_cont_table),digits=2) + tree_f1_score=F1_Score2(test_set$simkey,tree_predictions) + + ## Format data + result_data=tibble(seed_max=seed_max,model=c("knn","tree"),accuracy=c(knn_accuracy,tree_accuracy)) + result_data=cbind(result_data,rbind(knn_f1_score,tree_f1_score)) + list(result_data) + }) + ## Prints + results=do.call("rbind",results) + results%>%mutate(seed_max=seed_max,attempts_max=attempts_max,wireless=wrl,wakeupfor=wuf) + +} + +########## Train models from seed 0 to seed 160 (this code assumes that each day, a given policy is used) +generate_accuracy_energy = function(wireless,wakeupfor,steps=1, accuracy=10,ignore_hint=FALSE){ + ## Setup variables + npolicies=4 + 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) # Note !! do unquote, here we select the proper scenario + + ## 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%) here 160 over 200 seeds + print(paste("Step",i)) + 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,setup=paste0(!!wireless," ",!!wakeupfor,"s"),wireless=!!wireless,wakeupfor=!!wakeupfor)) # days=i*npolicies since 1 policy only per day is used (see paper section IV.A) + result=rbind(result,acc) + } + list(accuracy=result%>%mutate(days=seed_max*npolicies), # Since 1 policy per days see L130 + energy=result_energy) +} + + +########## Generate accuracy, energy, F1-Score and coverage data ########## +if(F){ # Toggle to train + lora180=generate_accuracy_energy("lora",180) + lora60=generate_accuracy_energy("lora",60) + nbiot60=generate_accuracy_energy("nbiot",60) + nbiot180=generate_accuracy_energy("nbiot",180) + accuracy=rbind(lora60$accuracy,lora180$accuracy,nbiot60$accuracy,nbiot180$accuracy) + coverage=data_seed%>%group_by(wireless,wakeupfor,seed)%>%summarize(coverage=sum(coverage))%>%mutate(days=seed*4)%>%filter(days %in% !!lora60$energy$days)%>%select(-seed) + energy=rbind(lora60$energy, + lora180$energy, + nbiot60$energy, + nbiot180$energy)%>%left_join(coverage,by=c("wireless","wakeupfor","days")) +} + + +########## Build learning curve (Accuracy+F1_Scores) ########## +learning_curves=accuracy%>%group_by(wireless,wakeupfor,days,model)%>% + summarize( + ## Accuracy + mean_accuracy=mean(accuracy),sd_accuracy=sd(accuracy), + min_accuracy=min(accuracy),max_accuracy=max(accuracy), + ## F1-Score Baseline + mean_f1_baseline=mean(f1_baseline),sd_f1_baseline=sd(f1_baseline), + min_f1_baseline=min(f1_baseline),max_f1_baseline=max(f1_baseline), + ## F1-Score Hint + mean_f1_hint=mean(f1_hint),sd_f1_hint=sd(f1_hint),min_f1_hint=min(f1_hint), + max_f1_hint=max(f1_hint), + ## F1-Score Extended + mean_f1_extended=mean(f1_extended),sd_f1_extended=sd(f1_extended), + min_f1_extended=min(f1_extended),max_f1_extended=max(f1_extended), + ## F1-Score HintAndExtended + mean_f1_hintandextended=mean(f1_hintandextended),sd_f1_hintandextended=sd(f1_hintandextended), + min_f1_hintandextended=min(f1_hintandextended),max_f1_hintandextended=max(f1_hintandextended)) + + +ggplot(data=learning_curves%>%mutate(model=ifelse(model=="knn","KNN","DT")),aes(linetype=model))+ + geom_line(aes(days/30,mean_f1_baseline,color="Baseline"),size=1.2)+ + geom_line(aes(days/30,mean_f1_extended,color="Extended"),size=1.2)+ + geom_line(aes(days/30,mean_f1_hint,color="Hint"),size=1.2)+ + geom_line(aes(days/30,mean_f1_hintandextended,color="Hintandextended"),size=1.2)+labs(color="Classes colors",linetype="Model")+ + facet_wrap(~wireless+wakeupfor)+ + scale_x_continuous(breaks = seq(0, max(learning_curves$days/30)))+ + scale_y_continuous(breaks = seq(0, 1, by = 0.1))+ + th()+ + theme(panel.grid.minor = element_blank(), + legend.position = c(0.9,0.72), + legend.margin = margin(2,4,2,4), + legend.spacing=unit(-0.2,"cm"), + legend.box.margin=margin(1,1,1,1))+ + xlab("Training duration (months)")+ylab("Classes F1-Score") +ggsave("figures/months_f1-score.pdf",width=8.5,height=6) + + +## Plot Merge Accuracy +ggplot(data=learning_curves%>%mutate(model=ifelse(model=="knn","KNN","DT")), + aes(days/30,mean_accuracy))+ + geom_line(aes(linetype=model),size=1.2)+xlab("Training duration (months)")+ylab("Model overall accuracy (OAcc)")+labs(linetype="Models")+ + scale_x_continuous(breaks = seq(0, max(learning_curves$days/30)))+ + scale_y_continuous(breaks = seq(0, 1, by = 0.1))+ + facet_wrap(~wireless+wakeupfor)+expand_limits(x = 0, y = 0)+ + th()+ + theme(panel.grid.minor = element_blank(), + legend.position = c(0.38,0.68)) +ggsave("figures/months_accuracy.pdf",width=8.5,height=6) + + +########## Energy and delta with raw policies ########## +npolicies=4 +## First we extended the number of seed to cover the entire duration of the training +data_seed_energy=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 +## Compute the cumulative energy of each policies in each configuration accross the $npolicies*200 days +data_seed_energy=data_seed_energy%>%group_by(wireless,wakeupfor,simkey)%>%mutate(energy=cumsum(energy),setup=paste0(wireless," ",wakeupfor,"s"),days=seed) +## Now sum coverage of each $npolicies days (like in the energy data frame) +data_seed_energy=data_seed_energy%>%group_by(wireless,wakeupfor,simkey)%>%mutate(grp=ceiling(days/npolicies))%>%group_by(wireless,wakeupfor,simkey,grp)%>%mutate(coverage=cumsum(coverage))%>%filter(days%%npolicies==0) +## Now filter the data +data_seed_energy=data_seed_energy%>%filter(days %in% !!energy$days) +## Compute the delta data +energy_coverage_delta=data_seed_energy%>% + full_join(energy,by=c("days","wireless","wakeupfor"),suffix=c("","_training")) +energy_coverage_delta=energy_coverage_delta%>%group_by(wireless,wakeupfor)%>%summarize(delta_energy=energy-energy_training,simkey=simkey,days=days,delta_coverage=(coverage-coverage_training)/npolicies,coverage=coverage/npolicies,coverage_training=coverage_training/npolicies) # delta_coverage divide by $npolicies because we want the average per day (coverage is measure every $npolicies days (round-robin of $npolicies policies)) + + +write("wireless,wakeupfor,policy,slope,intercept,delta_coverage,coverage,coverage_training,latex","figures/delta_energy_coverage.csv") +energy_coverage_delta%>%group_by(wireless,wakeupfor,simkey)%>%group_walk(function(data,grp){ + grp=as.list(grp) + reg=lm(delta_energy ~ days,data) + slope=round(as.numeric(reg$coefficients["days"]),digits=0) + intercept=round(as.numeric(reg$coefficients[1]),digits=0) + mean_delta_coverage=round(mean(data$delta_coverage),digits=1) + mean_coverage=round(mean(data$coverage),digits=1) + mean_coverage_training=round(mean(data$coverage_training),digits=1) + print(paste0("Wireless=",grp$wireless," Wakeupfor=",grp$wakeupfor," Policy=",grp$simkey," Slope=",slope," Intercept=",intercept," Delta Coverage=",mean_delta_coverage)) + write(paste(grp$wireless,grp$wakeupfor,grp$simkey,slope,intercept,mean_delta_coverage,mean_coverage,mean_coverage_training,paste0(r"("$\mathbf{s=)",slope,",c_p=",mean_coverage,",c_t=",mean_coverage_training,r"(}$")"),sep=","),"figures/delta_energy_coverage.csv",append=T) +}) + +ggplot(energy_coverage_delta,aes(days/30,delta_energy/1e3,color=simkey,shape=simkey))+ + geom_line(size=1.2)+ylab(TeX("Delta in $E_{total}$ (kJ)"))+xlab("Training duration (months)")+ + facet_wrap(~wireless+wakeupfor,scale="free")+ + th()+theme(legend.position=c(0.61,0.97))+labs(color="Classes colors") +ggsave("figures/delta_energy_training.pdf",height=6,width=10) + +ggplot(energy_coverage_delta,aes(days/30,delta_coverage,color=simkey))+ + geom_line(size=1.2)+ylab("Delta in network coverage")+xlab("Training duration (months)")+ + facet_wrap(~wireless+wakeupfor,scale="free")+ + th()+theme(legend.position="top")+labs(color="Classes colors") +ggsave("figures/delta_coverage_training.pdf",width=9) + +## ggplot(data=energy,aes(days/30,energy/1e6,group=setup,fill=setup))+ +## geom_bar(stat="identity",position="dodge")+ +## labs(fill="Wireless and Uptime")+ +## scale_x_continuous(breaks = seq(0, max(energy$days/30)))+ +## xlab("Training duration (months)")+ylab("Energy consumption (MJ)")+ +## th()+theme(legend.position=c(0.12,0.75)) +## ggsave("figures/days_energy.pdf",width=8.5,height=4) + + + |