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+# ----------------------------------------------------------------------
+# title: TOM useful functions
+# author: Alassane Samba (alassane.samba@orange.com)
+# Copyright (c) 2017 Orange
+# All rights reserved. This program and the accompanying materials
+# are made available under the terms of the Apache License, Version 2.0
+# which accompanies this distribution, and is available at
+# http://www.apache.org/licenses/LICENSE-2.0
+# ----------------------------------------------------------------------
+############
+### Evaluate a prediction
+############
+prediction_evaluator_with_p<-function(pred, actual, p){
+ ##error
+ error=actual-pred
+ ## null ndeviance
+ null.deviance=sum((mean(actual)-actual)^2)
+ ## deviance : sum of squared error
+ se=sum(error^2)
+ ## n
+ n=length(actual)
+ ## mean squared error
+ mse=se/n
+ ## root mean squared error
+ rmse=sqrt(mse)
+ ## normalized root mean squared error
+ nrmse=sqrt(mse/(null.deviance/n))
+ ## r2 : coef of determination
+ r2=1-(se/null.deviance)
+ #adjusted r2
+ adfR2=1-(1-r2)*((n-1)/(n-p))
+ ## absolute error ratio
+ abs.error.ratio=abs(error/actual)
+ ## mean absolute error ratio
+ mean.abs.error.ratio=mean(abs.error.ratio)
+ ## median absolute error ratio
+ med.abs.error.ratio=median(abs.error.ratio)
+ ## 80th-percentile absolute error ratio
+ perc80.abs.error.ratio=quantile(abs.error.ratio,0.8)
+ ## return :
+ return(list(p=p,n=n,sd=sqrt(null.deviance/n),NRMSE=nrmse,RMSE=rmse,R2=r2,adj.R2=adfR2,mean.abs.error.ratio=mean.abs.error.ratio,med.abs.error.ratio=med.abs.error.ratio,perc80.abs.error.ratio=perc80.abs.error.ratio))
+ ### ajouter error et error ratio au return pour pouvoir faire les graphes, etc.
+}
+#########
+## Calculate R2 (coef of determination / part of explained variance) from the GLM regression
+#########
+betterGenericR2calculator<-function(dataset,targetName,independantVariableNames){
+ dataset2=na.omit(dataset[,c(targetName,independantVariableNames)])
+ numVars=colnames(dataset2)[unlist(lapply(dataset2,is.numeric))&colnames(dataset2)%in%independantVariableNames]
+ factorVars=colnames(dataset2)[(!unlist(lapply(dataset2,is.numeric)))&colnames(dataset2)%in%independantVariableNames]
+ if(length(factorVars)>0&length(numVars)>0){
+ factorVarPasted=droplevels(as.factor(apply(cbind(rep("",nrow(dataset2)),as.data.frame(dataset2[,factorVars])),1,paste,collapse=":")))
+ theformula=paste(targetName,paste(numVars,collapse = "*"), sep='~')
+ nbNumVars=length(numVars)
+ resList=by(dataset2, factorVarPasted, FUN=function(x){m=lm(theformula,data=x,y=T); return(list(pred=m$fitted.values,actual=m$y))})
+ pred=unlist(lapply(resList,function(x){x$pred}))
+ actual=unlist(lapply(resList,function(x){x$actual}))
+ p=((length(levels(droplevels(as.factor(factorVarPasted))))-1)*nbNumVars)+(length(levels(droplevels(as.factor(factorVarPasted))))-1)+nbNumVars+1
+ res=prediction_evaluator_with_p(pred,actual,p)
+ }else if(length(factorVars)==0&length(numVars)>0){
+ theformula=paste(targetName,paste(numVars,collapse = "*"), sep='~')
+ nbNumVars=length(numVars)
+ m=lm(theformula,data=dataset2,y=T)
+ pred=m$fitted.values
+ actual=m$y
+ p=nbNumVars+1
+ res=prediction_evaluator_with_p(pred,actual,p)
+ }else if(length(factorVars)>0&length(numVars)==0){
+ factorVarPasted=droplevels(as.factor(apply(cbind(rep("",nrow(dataset2)),as.data.frame(dataset2[,factorVars])),1,paste,collapse=":")))
+ m=lm(dataset2[,targetName]~factorVarPasted,y=T)
+ pred=m$fitted.values
+ actual=m$y
+ p=length(levels(factorVarPasted))
+ res=prediction_evaluator_with_p(pred,actual,p)
+ }else{
+ res=NULL
+ }
+ return(res)
+}
+###################
+###### Determine the best predictor set (continuous and factor independant variables) to consider for a continuous dependant variable
+###################
+genericBestPredictor<-function(dataset,targetName,independantVariableNames, plot=T, text=T, las=1){
+ ordered_best_additional_predictors=list()
+ ordered_best_predictors_per_level=list(NULL)
+ ordered_best_r2=list()
+ for (i in 1:length(independantVariableNames)){
+ level_i_predictors=as.list(independantVariableNames)[!as.list(independantVariableNames)%in%ordered_best_additional_predictors]
+ varExp_i=lapply(level_i_predictors,function(x){betterGenericR2calculator(dataset,targetName,c(unlist(ordered_best_additional_predictors),x))$R2})
+ if(i==1){
+ bivariateR2<-varExp_i
+ names(bivariateR2)<-unlist(level_i_predictors)
+ }
+ ordered_best_additional_predictors=c(ordered_best_additional_predictors,level_i_predictors[varExp_i%in%max(unlist(varExp_i))])
+ ordered_best_predictors_per_level=c(ordered_best_predictors_per_level,paste(unlist(ordered_best_predictors_per_level[i]),unlist(ordered_best_additional_predictors[i]), sep=":"))
+ ordered_best_r2=c(ordered_best_r2,max(unlist(varExp_i)))
+ ordered_r2_progress=c(round(unlist(ordered_best_r2)[1],2),paste(rep("+",length(ordered_best_r2)-1),round(unlist(ordered_best_r2)[-1]-unlist(ordered_best_r2)[-length(ordered_best_r2)],2)))
+ }
+ ordered_best_predictors_per_level=ordered_best_predictors_per_level[-1]
+
+ mynames=unlist(ordered_best_additional_predictors)
+ mynames=c(mynames[1],paste("+",mynames[2:length(mynames)]))
+ names(ordered_best_r2)<-mynames
+
+ if(plot){
+ #barplot(unlist(ordered_best_r2), names.arg = unlist(ordered_best_predictors_per_level),las=las,ylab="R2",main=paste("Correlation with",targetName))
+ barplot(unlist(ordered_best_r2), names.arg = mynames, las=las, ylab="R2",main=paste("Correlation with",targetName))
+ text(y=0.1, x=((1:length(mynames))-0.4)*1.2,labels = ordered_r2_progress)
+ }
+ if(text) return(list(bivariateR2=bivariateR2,orderedBestR2=ordered_best_r2,orderedBestPredictorsPerLevel=ordered_best_predictors_per_level,orderedBestAdditionalPredictors=ordered_best_additional_predictors, targetName=targetName))
+}
+#################### \ No newline at end of file