diff options
Diffstat (limited to 'analysis/days.R')
| -rw-r--r-- | analysis/days.R | 47 |
1 files changed, 41 insertions, 6 deletions
diff --git a/analysis/days.R b/analysis/days.R index ce12930..2b671d1 100644 --- a/analysis/days.R +++ b/analysis/days.R @@ -107,11 +107,12 @@ generate_accuracy = function(wireless,wakeupfor,steps=10, accuracy=10,ignore_hin } # 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)) - +if(F){ # Toggle to train +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,months,model)%>% summarize( @@ -125,7 +126,41 @@ result_summary=accuracy%>%group_by(wireless,wakeupfor,months,model)%>% metrics_peak=result_summary%>%group_by(wireless,wakeupfor,model)%>% summarize(max_accuracy=max(mean_accuracy)) -## Plot + +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)+ + geom_line(aes(months,mean_f1_hintandextended,color="Hintandextended"),size=1.2)+labs(color="Classes",linetype="Model")+ + facet_wrap(~wireless+wakeupfor)+ + scale_x_continuous(breaks = seq(0, 15, by = 1))+ + scale_y_continuous(breaks = seq(0, 1, by = 0.1))+ + theme_bw()+ + theme(panel.grid.minor = element_blank(), + legend.position = c(0.9,0.74), + legend.margin = margin(2,4,2,4), + legend.spacing=unit(-0.2,"cm"), + legend.box.margin=margin(1,1,1,1), + legend.box.background = element_rect(fill = "white", color = "black",size=0.8))+ + 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")+ + scale_x_continuous(breaks = seq(0, 15, by = 1))+ + scale_y_continuous(breaks = seq(0, 1, by = 0.1))+ + facet_wrap(~wireless+wakeupfor)+ + theme_bw()+ + theme(panel.grid.minor = element_blank(), + legend.position = c(0.38,0.68), + 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) + + +## Plot accuracy + F1-Score sapply(c("knn","tree"),function(grp){ data=result_summary%>%filter(model==grp) plot=ggplot(data,aes(months,mean_accuracy))+ |