From 377c0109ec73482613269124480b47d7056691ec Mon Sep 17 00:00:00 2001 From: Loic Guegan Date: Wed, 23 Nov 2022 10:47:14 +0100 Subject: Minor changes --- analysis/days.R | 125 +++++++++++++++++++++ analysis/figures/combined.pdf | Bin 1075731 -> 751386 bytes analysis/figures/days_knn.pdf | Bin 0 -> 9586 bytes analysis/figures/days_tree.pdf | Bin 0 -> 8654 bytes analysis/figures/dimension_coverage.pdf | Bin 18212 -> 18266 bytes analysis/figures/dimension_efficiency.pdf | Bin 78855 -> 78811 bytes .../figures/dimension_energy-coverage-policy.pdf | Bin 138901 -> 139129 bytes .../dimension_energy-coverage-wakeupfor.pdf | Bin 138987 -> 139434 bytes analysis/figures/dimension_energy-coverage.pdf | Bin 149307 -> 150220 bytes analysis/figures/dimension_energy.pdf | Bin 111807 -> 111805 bytes analysis/figures/random_inputs_NoHintIsFALSE.pdf | Bin 159562 -> 0 bytes analysis/figures/random_inputs_NoHintIsTRUE.pdf | Bin 159315 -> 0 bytes .../figures/sim_dimension_coverage_NO_HINT.pdf | Bin 27081 -> 7163 bytes .../figures/sim_dimension_coverage_WITH_HINT.pdf | Bin 27773 -> 33108 bytes analysis/figures/sim_dimension_energy_NO_HINT.pdf | Bin 29929 -> 7508 bytes .../figures/sim_dimension_energy_WITH_HINT.pdf | Bin 30846 -> 37719 bytes analysis/figures/tree.pdf | Bin 7964 -> 0 bytes analysis/learning.R | 13 +++ 18 files changed, 138 insertions(+) create mode 100644 analysis/days.R create mode 100644 analysis/figures/days_knn.pdf create mode 100644 analysis/figures/days_tree.pdf delete mode 100644 analysis/figures/random_inputs_NoHintIsFALSE.pdf delete mode 100644 analysis/figures/random_inputs_NoHintIsTRUE.pdf delete mode 100644 analysis/figures/tree.pdf (limited to 'analysis') diff --git a/analysis/days.R b/analysis/days.R new file mode 100644 index 0000000..1ba131d --- /dev/null +++ b/analysis/days.R @@ -0,0 +1,125 @@ +library("tidyverse") +library("class") +library("rpart") +library("rpart.plot") +library("viridis") + +## 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) + +nseed=200 +nwakeupfor=2 +nwireless=2 +nsimkey=4 +nsimulations=nseed*nwakeupfor*nwireless*nsimkey # Must be 3200 + +## Load data +data=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 +tmp_data_coverage=data%>%group_by(simkey,wireless,wakeupfor,seed)%>%mutate(coverage=sum(nDataRcv))%>%ungroup()%>%filter(isSender==1)%>%select(simkey,wireless,wakeupfor,seed,coverage) +data_seed_isSender=data%>%group_by(simkey,wireless,wakeupfor,seed,isSender)%>%summarize(energy_mean=mean(energy))%>% + left_join(tmp_data_coverage,by=c("simkey","wireless","wakeupfor","seed"))%>% + mutate(efficiency=energy_mean/coverage)%>% + ungroup() +data_seed=data%>%group_by(simkey,wireless,wakeupfor,seed)%>%summarize(energy=sum(energy),coverage=sum(nDataRcv))%>% + mutate(efficiency=energy/coverage)%>% + ungroup() + +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) + data_seed=data_seed%>%filter(wakeupfor==wuf,wireless==wrl) + data_ml=data_seed%>%select(-efficiency)%>%mutate(wireless=wireless_map[data_seed$wireless]) + if(ignore_hint){ + data_ml=data_ml%>%filter(simkey!="hint") + } + train_set=data_ml%>%filter(seed<=seed_max)%>%select(-seed) # train data on seed_max*3 days + test_set=data_ml%>%anti_join(train_set)%>%select(-seed) # build test_sed excluding training set + + ## KNN training + knn_predictions=knn(train=train_set%>%select(-simkey),test=test_set%>%select(-simkey),cl=train_set$simkey,k=10) + ## KNN analysis + knn_cont_table=table(knn_predictions,test_set$simkey) + knn_accuracy=round((sum(diag(knn_cont_table)/sum(rowSums(knn_cont_table))))*100) + knn_prop_table=round(prop.table(knn_cont_table),digits=2) + + ## Decision tree + tree=rpart( + simkey ~ wireless + wakeupfor + energy + coverage, + data=train_set, + method="class", + minsplit=60, + minbucket=1) + tree_predictions=predict(tree,newdata=test_set%>%select(-simkey),type="class") + tree_cont_table=table(tree_predictions,test_set$simkey) + tree_accuracy=round((sum(diag(tree_cont_table)/sum(rowSums(tree_cont_table))))*100) + tree_prop_table=round(prop.table(tree_cont_table),digits=2) + list(tibble(seed_max=seed_max,knn_accuracy=knn_accuracy,tree_accuracy=tree_accuracy)) + }) + ## Prints + results=do.call("rbind",results) + results%>%mutate(seed_max=seed_max,attempts_max=attempts_max,wireless=wrl,wakeupfor=wuf) + +} + +generate_accuracy = function(wireless,wakeupfor,steps=20, accuracy=10){ + ## Generate inputs + result=tibble() + for(i in seq(1,200,by=steps)){ + acc=generate_accuracy_for(ignore_hint=TRUE,seed=i,attempts_max=accuracy,wrl=wireless,wuf=wakeupfor) + result=rbind(result,acc) + } + result%>%mutate(days=seed_max*3) # Since 3 policies (since ignore_hint=TRUE) +} + +## Generate accuracy for each wireless and uptime +accuracy=rbind(generate_accuracy("lora",60), + generate_accuracy("lora",180), + generate_accuracy("nbiot",60), + generate_accuracy("nbiot",180)) + +## Summarize +result_summary=accuracy%>%group_by(wireless,wakeupfor,seed_max)%>%summarize(attempts_max=mean(attempts_max),days=mean(days),mean_knn_accuracy=mean(knn_accuracy),sd_knn_accuracy=sd(knn_accuracy),min_knn_accuracy=min(knn_accuracy),max_knn_accuracy=max(knn_accuracy),mean_tree_accuracy=mean(tree_accuracy),sd_tree_accuracy=sd(tree_accuracy),min_tree_accuracy=min(tree_accuracy),max_tree_accuracy=max(tree_accuracy)) + +## Result max +result_max=result_summary%>%group_by(wireless,wakeupfor)%>%summarize(max_knn_mean=max(mean_knn_accuracy),max_tree_mean=max(mean_tree_accuracy)) + +## Plot +ggplot(result_summary,aes(days,mean_knn_accuracy))+ + geom_errorbar(aes(ymin=min_knn_accuracy,ymax=max_knn_accuracy),width=5)+ + geom_boxplot(aes(ymin=min_knn_accuracy,ymax=max_knn_accuracy, + middle=mean_knn_accuracy, + upper=mean_knn_accuracy+sd_knn_accuracy, + lower=mean_knn_accuracy-sd_knn_accuracy,group=days),stat="identity",fill="grey")+ + geom_line(size=1.1)+geom_point(size=3,pch=15)+xlab("Number of training days")+ylab("Mean KNN accuracy")+ggtitle("KNN Accuracy")+ + ylim(c(0,100))+ + facet_wrap(~wireless+wakeupfor)+ + geom_hline(data=result_max,aes(yintercept=max_knn_mean),color="red",size=1)+ + geom_text(data=result_max, geom="text",x=0,aes(y=max_knn_mean,label = max_knn_mean,vjust=-1),color="red") +ggsave("figures/days_knn.pdf") + +ggplot(result_summary,aes(days,mean_tree_accuracy))+ + geom_errorbar(aes(ymin=min_tree_accuracy,ymax=max_tree_accuracy),width=5)+ + geom_boxplot(aes(ymin=min_tree_accuracy,ymax=max_tree_accuracy, + middle=mean_tree_accuracy, + upper=mean_tree_accuracy+sd_tree_accuracy, + lower=mean_tree_accuracy-sd_tree_accuracy,group=days),stat="identity",fill="grey")+ + geom_line(size=1.1)+geom_point(size=3,pch=15)+xlab("Number of training days")+ylab("Mean tree accuracy")+ggtitle("TREE Accuracy")+ + ylim(c(0,100))+ + facet_wrap(~wireless+wakeupfor)+ + geom_hline(data=result_max,aes(yintercept=max_tree_mean),color="red",size=1)+ + geom_text(data=result_max, geom="text",x=0,aes(y=max_tree_mean,label = max_tree_mean,vjust=-1),color="red") +ggsave("figures/days_tree.pdf") + diff --git a/analysis/figures/combined.pdf b/analysis/figures/combined.pdf index 162ff49..11f3916 100644 Binary files a/analysis/figures/combined.pdf and b/analysis/figures/combined.pdf differ diff --git a/analysis/figures/days_knn.pdf b/analysis/figures/days_knn.pdf new file mode 100644 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b/analysis/figures/sim_dimension_energy_NO_HINT.pdf index efcf376..048f1a8 100644 Binary files a/analysis/figures/sim_dimension_energy_NO_HINT.pdf and b/analysis/figures/sim_dimension_energy_NO_HINT.pdf differ diff --git a/analysis/figures/sim_dimension_energy_WITH_HINT.pdf b/analysis/figures/sim_dimension_energy_WITH_HINT.pdf index 937e413..d68df7b 100644 Binary files a/analysis/figures/sim_dimension_energy_WITH_HINT.pdf and b/analysis/figures/sim_dimension_energy_WITH_HINT.pdf differ diff --git a/analysis/figures/tree.pdf b/analysis/figures/tree.pdf deleted file mode 100644 index 097ad26..0000000 Binary files a/analysis/figures/tree.pdf and /dev/null differ diff --git a/analysis/learning.R b/analysis/learning.R index b3798e6..d21b8cd 100644 --- a/analysis/learning.R +++ b/analysis/learning.R @@ -64,6 +64,19 @@ generate_inputs=function(ignore_hint=FALSE) { tree_accuracy=round((sum(diag(tree_cont_table)/sum(rowSums(tree_cont_table))))*100) tree_prop_table=round(prop.table(tree_cont_table),digits=2) + ## Elbow plot + elbow_data=lapply(seq(1,10),function(kvalue){ + knn_predictions=knn(train=train_set%>%select(-simkey),test=test_set%>%select(-simkey),cl=train_set$simkey,k=kvalue) + ## KNN analysis + knn_cont_table=table(knn_predictions,test_set$simkey) + knn_accuracy=round((sum(diag(knn_cont_table)/sum(rowSums(knn_cont_table))))*100) + knn_prop_table=round(prop.table(knn_cont_table),digits=2) + tibble(k=kvalue,accuracy=knn_accuracy) + }) + elbow_data=do.call("rbind",elbow_data) + ggplot(data=elbow_data,aes(k,accuracy))+geom_point()+geom_line()+ggtitle(paste("K-elbow for with NoHint to",as.character(ignore_hint))) + ggsave(paste0("figures/knn_elbow_NoHintIs",as.character(ignore_hint),".pdf")) + ## Prints print(paste0("Accuracy: KNN=",knn_accuracy,"% CART=",tree_accuracy,"%")) pdf(paste0("figures/tree_",as.character(ignore_hint),".pdf")) -- cgit v1.2.3