Minor changes

18 files changed, 138 insertions, 0 deletions

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
--- a/analysis/figures/combined.pdf
+++ b/analysis/figures/combined.pdf
diff --git a/analysis/figures/days_knn.pdf b/analysis/figures/days_knn.pdf
new file mode 100644
index 0000000..a3a4f88
--- /dev/null
+++ b/analysis/figures/days_knn.pdf
diff --git a/analysis/figures/days_tree.pdf b/analysis/figures/days_tree.pdf
new file mode 100644
index 0000000..fb8c411
--- /dev/null
+++ b/analysis/figures/days_tree.pdf
diff --git a/analysis/figures/dimension_coverage.pdf b/analysis/figures/dimension_coverage.pdf
index f77bee9..c7575b6 100644
--- a/analysis/figures/dimension_coverage.pdf
+++ b/analysis/figures/dimension_coverage.pdf
diff --git a/analysis/figures/dimension_efficiency.pdf b/analysis/figures/dimension_efficiency.pdf
index 06d53cb..59c4f38 100644
--- a/analysis/figures/dimension_efficiency.pdf
+++ b/analysis/figures/dimension_efficiency.pdf
diff --git a/analysis/figures/dimension_energy-coverage-policy.pdf b/analysis/figures/dimension_energy-coverage-policy.pdf
index bb6c27e..0281894 100644
--- a/analysis/figures/dimension_energy-coverage-policy.pdf
+++ b/analysis/figures/dimension_energy-coverage-policy.pdf
diff --git a/analysis/figures/dimension_energy-coverage-wakeupfor.pdf b/analysis/figures/dimension_energy-coverage-wakeupfor.pdf
index 52de042..9f51c55 100644
--- a/analysis/figures/dimension_energy-coverage-wakeupfor.pdf
+++ b/analysis/figures/dimension_energy-coverage-wakeupfor.pdf
diff --git a/analysis/figures/dimension_energy-coverage.pdf b/analysis/figures/dimension_energy-coverage.pdf
index 977adf6..43e25f6 100644
--- a/analysis/figures/dimension_energy-coverage.pdf
+++ b/analysis/figures/dimension_energy-coverage.pdf
diff --git a/analysis/figures/dimension_energy.pdf b/analysis/figures/dimension_energy.pdf
index bf4d1b9..8902663 100644
--- a/analysis/figures/dimension_energy.pdf
+++ b/analysis/figures/dimension_energy.pdf
diff --git a/analysis/figures/random_inputs_NoHintIsFALSE.pdf b/analysis/figures/random_inputs_NoHintIsFALSE.pdf
deleted file mode 100644
index c48174c..0000000
--- a/analysis/figures/random_inputs_NoHintIsFALSE.pdf
+++ /dev/null
diff --git a/analysis/figures/random_inputs_NoHintIsTRUE.pdf b/analysis/figures/random_inputs_NoHintIsTRUE.pdf
deleted file mode 100644
index 1153418..0000000
--- a/analysis/figures/random_inputs_NoHintIsTRUE.pdf
+++ /dev/null
diff --git a/analysis/figures/sim_dimension_coverage_NO_HINT.pdf b/analysis/figures/sim_dimension_coverage_NO_HINT.pdf
index 7f45e6b..2b5d154 100644
--- a/analysis/figures/sim_dimension_coverage_NO_HINT.pdf
+++ b/analysis/figures/sim_dimension_coverage_NO_HINT.pdf
diff --git a/analysis/figures/sim_dimension_coverage_WITH_HINT.pdf b/analysis/figures/sim_dimension_coverage_WITH_HINT.pdf
index 9081c60..cc72a3e 100644
--- a/analysis/figures/sim_dimension_coverage_WITH_HINT.pdf
+++ b/analysis/figures/sim_dimension_coverage_WITH_HINT.pdf
diff --git a/analysis/figures/sim_dimension_energy_NO_HINT.pdf b/analysis/figures/sim_dimension_energy_NO_HINT.pdf
index efcf376..048f1a8 100644
--- a/analysis/figures/sim_dimension_energy_NO_HINT.pdf
+++ b/analysis/figures/sim_dimension_energy_NO_HINT.pdf
diff --git a/analysis/figures/sim_dimension_energy_WITH_HINT.pdf b/analysis/figures/sim_dimension_energy_WITH_HINT.pdf
index 937e413..d68df7b 100644
--- a/analysis/figures/sim_dimension_energy_WITH_HINT.pdf
+++ b/analysis/figures/sim_dimension_energy_WITH_HINT.pdf
diff --git a/analysis/figures/tree.pdf b/analysis/figures/tree.pdf
deleted file mode 100644
index 097ad26..0000000
--- a/analysis/figures/tree.pdf
+++ /dev/null
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"))