Clean repo and debug setup.sh

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diff --git a/analysis/learning.R b/analysis/learning.R
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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)