diff options
Diffstat (limited to 'analysis/learning.R')
| -rw-r--r-- | analysis/learning.R | 144 |
1 files changed, 77 insertions, 67 deletions
diff --git a/analysis/learning.R b/analysis/learning.R index cb8a3b4..b3798e6 100644 --- a/analysis/learning.R +++ b/analysis/learning.R @@ -34,74 +34,84 @@ data_seed=data%>%group_by(simkey,wireless,wakeupfor,seed)%>%summarize(energy=sum mutate(efficiency=energy/coverage)%>% ungroup() -## 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$wireless])#%>%filter(simkey!="hint") -train_set=data_ml%>%sample_frac(0.8) # 80% of the data -test_set=data_ml%>%anti_join(train_set) # 20% of the data +generate_inputs=function(ignore_hint=FALSE) { + ## Prepare data for traning + set.seed(1) # Reproducibility + wireless_map=c("lora"=1,"nbiot"=2) + data_ml=data_seed%>%select(-efficiency,-seed)%>%mutate(wireless=wireless_map[data_seed$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) + ## 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$simkey) -tree_accuracy=round((sum(diag(tree_cont_table)/sum(rowSums(tree_cont_table))))*100) -tree_prop_table=round(prop.table(tree_cont_table),digits=2) + ## Decision tree + tree=rpart( + simkey ~ wireless + wakeupfor + energy + coverage, + data=train_set, + method="class", + minsplit=60, + minbucket=1) + tree_predictions=predict(tree,newdata=test_set%>%select(-simkey),type="class") + tree_cont_table=table(tree_predictions,test_set$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) -## Prints -print(paste0("Accuracy: KNN=",knn_accuracy,"% CART=",tree_accuracy,"%")) -pdf("figures/tree.pdf") -tree_plot=rpart.plot(tree,box.palette=as.list(viridis::viridis(4,begin=0.48))) -silent_call=dev.off() -## Notes: KNN accuracy jump to 76% and CART to 80% accuracy without the hint policy + ## 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))) + 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=20 - 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$simkey,k=10) - predictions_tree=predict(tree,newdata=current_inputs%>% - rename(energy=energy_constraint,coverage=coverage_constraint)%>% - mutate(wireless=wireless_map[wireless]),type="class") - knn_final=tibble(cbind(current_inputs,tibble(simkey=predictions_knn,model="knn"))) - tree_final=tibble(cbind(current_inputs,tibble(simkey=predictions_tree,model="tree"))) - rbind(knn_final,tree_final) -}) -inputs=do.call("rbind",constraints)%>%distinct() -## Dimension Energy/Coverage -ggplot(data_seed%>%mutate(wakeupfor=as.character(wakeupfor)), - aes(coverage,energy,color=simkey))+geom_point()+ - geom_point(data=inputs%>%mutate(wakeupfor=as.character(wakeupfor)),aes(coverage_constraint,energy_constraint),size=3,pch=5)+ - ggtitle("Dimension Energy/Coverage")+xlab("Coverage")+ylab("Sum of nodes energy consumption (J)")+ - facet_wrap(~wakeupfor+wireless,scale="free") -ggsave("figures/random_inputs.pdf") -write.csv(inputs,"../inputs.csv",row.names=FALSE, quote=FALSE) + ## Generate simulation inputs + inputs=tibble( + wakeupfor = c(60,180,60,180), + wireless = c("lora", "lora", "nbiot", "nbiot")) + constraints=apply(inputs,1,function(row){ + wi=row["wireless"] + wa=as.numeric(row["wakeupfor"]) + ## First extract energy/coverage boundaries + min_energy=min((data_seed%>%filter(wireless==wi,wakeupfor==wa))$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) |