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library("tidyverse")
options(dplyr.summarise.inform = FALSE)
library("class")
library("rpart")
library("rpart.plot")
library("viridis")
library("MLmetrics")
## 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=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_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)
}
build_models=function(ignore_hint=TRUE){
## Prepare data for traning
set.seed(1) # Reproducibility
wireless_map=c("lora"=1,"nbiot"=2)
data_ml=data_seed%>%select(-efficiency,-seed)%>%mutate(wireless=wireless_map[data_seed$wireless])
if(ignore_hint){
data_ml=data_ml%>%filter(simkey!="hint")
}
train_set=data_ml%>%sample_frac(0.8) # 80% of the data
test_set=data_ml%>%suppressMessages(anti_join(train_set)) # 20% of the data
## KNN predict function
knn_fn=function(inputs){
as.vector(knn(train=train_set%>%select(-simkey),test=inputs%>%select(-simkey),cl=train_set$simkey,k=10))
}
## Decision tree
tree=rpart(
simkey ~ wireless + wakeupfor + energy + coverage,
data=train_set,
method="class",
minsplit=60,
minbucket=1)
## Tree predict function
tree_fn=function(inputs){
as.vector(predict(tree,newdata=inputs%>%select(-simkey),type="class"))
}
## Build models
models=list(predict_knn=knn_fn,predict_tree=tree_fn)
## Computer performances
perfs=sapply(seq(1,20),function(i){
## Prepare data for traning
set.seed(1) # Reproducibility
wireless_map=c("lora"=1,"nbiot"=2)
data_ml=data_seed%>%select(-efficiency,-seed)%>%mutate(wireless=wireless_map[data_seed$wireless])
if(ignore_hint){
data_ml=data_ml%>%filter(simkey!="hint")
}
train_set=data_ml%>%sample_frac(0.8) # 80% of the data
test_set=data_ml%>%suppressMessages(anti_join(train_set)) # 20% of the data
## KNN
knn_predictions=as.vector(knn(train=train_set%>%select(-simkey),test=test_set%>%select(-simkey),cl=train_set$simkey,k=10))
## Decision tree
tree=rpart(
simkey ~ wireless + wakeupfor + energy + coverage,
data=train_set,
method="class",
minsplit=60,
minbucket=1)
tree_predictions=as.vector(predict(tree,newdata=test_set%>%select(-simkey),type="class"))
## Prefs
f1_knn=F1_Score2(test_set$simkey,knn_predictions)
f1_tree=F1_Score2(test_set$simkey,tree_predictions)
accuracy_knn=sum(test_set$simkey==knn_predictions)/length(test_set$simkey)
accuracy_tree=sum(test_set$simkey==tree_predictions)/length(test_set$simkey)
list(cbind(tibble(model=c("knn","tree")),rbind(f1_knn,f1_tree),tibble(accuracy=c(accuracy_knn,accuracy_tree))))
})
perfs=do.call("rbind",perfs)%>%mutate_if(is.numeric, ~round(.,digits=2))
perfs=perfs%>%group_by(model)%>%summarize(
f1_baseline=mean(f1_baseline),
f1_hint=mean(f1_hint),
f1_extended=mean(f1_extended),
f1_hintandextended=mean(f1_hintandextended),
accuracy=mean(accuracy))
write.csv(perfs,paste0("figures/f1_scores_offline_ignoreHINT",ignore_hint,".csv"),quote=FALSE,row.names=FALSE)
## Return models
models
}
generate_inputs=function(ignore_hint=FALSE) {
## Prepare data for traning
set.seed(1) # Reproducibility
wireless_map=c("lora"=1,"nbiot"=2)
data_ml=data_seed%>%select(-efficiency,-seed)%>%mutate(wireless=wireless_map[data_seed$wireless])
if(ignore_hint){
data_ml=data_ml%>%filter(simkey!="hint")
}
train_set=data_ml%>%sample_frac(0.8) # 80% of the data
test_set=data_ml%>%anti_join(train_set) # 20% of the data
## KNN training
knn_predictions=knn(train=train_set%>%select(-simkey),test=test_set%>%select(-simkey),cl=train_set$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=(sum(diag(tree_cont_table)/sum(rowSums(tree_cont_table))))
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=(sum(diag(knn_cont_table)/sum(rowSums(knn_cont_table))))
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)))+ylim(c(0,1))
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"))
tree_plot=rpart.plot(tree,box.palette=as.list(viridis::viridis(4,begin=0.48)),tweak=1.111)
silent_call=dev.off()
## Notes: KNN accuracy jump to 76% and CART to 80% accuracy without the hint policy
## Generate simulation inputs
inputs=tibble(
wakeupfor = c(60,180,60,180),
wireless = c("lora", "lora", "nbiot", "nbiot"))
constraints=apply(inputs,1,function(row){
wi=row["wireless"]
wa=as.numeric(row["wakeupfor"])
## First extract energy/coverage boundaries
min_energy=min((data_seed%>%filter(wireless==wi,wakeupfor==wa))$energy)
max_energy=max((data_seed%>%filter(wireless==wi,wakeupfor==wa))$energy)
min_coverage=min((data_seed%>%filter(wireless==wi,wakeupfor==wa))$coverage)
max_coverage=max((data_seed%>%filter(wireless==wi,wakeupfor==wa))$coverage)
## Generate random points (10 per scenarios)
n=100
current_inputs=tibble(
wireless=rep(wi,n),
wakeupfor=rep(wa,n),
energy_constraint=runif(n,min_energy,max_energy),
coverage_constraint=round(runif(n,min_coverage,max_coverage)))
predictions_knn=knn(train=train_set%>%select(-simkey),test=current_inputs%>%
rename(energy=energy_constraint,coverage=coverage_constraint)%>%
mutate(wireless=wireless_map[wireless]),cl=train_set$simkey,k=10)
predictions_tree=predict(tree,newdata=current_inputs%>%
rename(energy=energy_constraint,coverage=coverage_constraint)%>%
mutate(wireless=wireless_map[wireless]),type="class")
knn_final=tibble(cbind(current_inputs,tibble(simkey=predictions_knn,model="knn")))
tree_final=tibble(cbind(current_inputs,tibble(simkey=predictions_tree,model="tree")))
rbind(knn_final,tree_final)
})
inputs=do.call("rbind",constraints)%>%distinct()
## Dimension Energy/Coverage
ggplot(data_seed%>%mutate(wakeupfor=as.character(wakeupfor)),
aes(coverage,energy,color=simkey))+geom_point()+
geom_point(data=inputs%>%mutate(wakeupfor=as.character(wakeupfor)),aes(coverage_constraint,energy_constraint),size=3,pch=5)+
ggtitle("Dimension Energy/Coverage")+xlab("Coverage")+ylab("Sum of nodes energy consumption (J)")+
facet_wrap(~wakeupfor+wireless,scale="free")
ggsave(paste0("figures/random_inputs_NoHintIs",as.character(ignore_hint),".pdf"),width=15)
write.csv(inputs,paste0("../inputs_NoHintIs",as.character(ignore_hint),".csv"),row.names=FALSE, quote=FALSE)
}
## Generate inputs
generate_inputs(FALSE)
generate_inputs(TRUE)
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