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c97d2992 · Sanghoon · ff61d903 · c97d2992
Commit c97d2992 authored May 04, 2022 by Sanghoon
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VotingModel_code.R ...ve Bayes Classification/Voting Example/VotingModel_code.R +108 -0

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--- a/Crash Course on Naive Bayes Classification/Voting Example/VotingModel_code.R
+++ b/Crash Course on Naive Bayes Classification/Voting Example/VotingModel_code.R
+# Problem : Can these models be used to predict how lawmakers may vote?
+
+#Prepare a clean R environment in work space.
+rm(list=ls())
+
+#Use setwd() to navigate the data directory and specify desired folder. Here we are using Rstudio Editor directory.
+setwd(dirname(rstudioapi::getSourceEditorContext()$path))
+
+#Import our csv file data
+data=read.csv("VotingData.csv",header=TRUE) #Load data
+
+TrainingPct=0.6 #Percent of data to train model on
+TrainingSample=floor(TrainingPct*dim(data)[1]) #Number of observations to train the model on
+TestSample=dim(data)[1]-TrainingSample #Number of observations to test the model on
+
+TrainingData=data[1:TrainingSample,] #Get the training data
+
+#Find probabilities associtaed with democrat voting
+DemData=subset(TrainingData,TrainingData$Party=="democrat")
+
+#Store All Probabilities in a Matrix (2 rows, across all votes)
+ProbMat=matrix(0,2,(dim(DemData)[2]-3+1+1))
+
+m=2 #Equivalent sample size for Laplacian correction
+p=1/2 #Prior probability for Laplacian correction
+
+for (j in 3:dim(DemData)[2])
+{
+ ProbMat[1,j-2]=(sum(DemData[,j]=="y")+m*p)/(dim(DemData)[1]+m)
+ 
+}
+
+#Find Probabilities Associated with Republican Voting
+GOPData=subset(TrainingData,TrainingData$Party=="republican")
+
+for (j in 3:dim(GOPData)[2])
+{
+ ProbMat[2,j-2]=(sum(GOPData[,j]=="y")+m*p)/(dim(GOPData)[1]+m)
+ 
+}
+
+#Tag on marignal probabilities
+FinalInd=dim(ProbMat)[2]
+ProbMat[1:2,FinalInd]=c(sum(TrainingData$Party=="democrat")/dim(TrainingData)[1],sum(TrainingData$Party=="republican")/dim(TrainingData)[1])
+colnames(ProbMat)=c(names(data)[3:dim(data)[2]],"MargProb")
+rownames(ProbMat)=unique(TrainingData$Party)
+
+TestData=data[(TrainingSample+1):dim(data)[1],]
+AssignedMat=matrix(0,dim(TestData)[1],3)
+
+
+#Use the NB classifier on test Data
+VotingModel_fn<-function(TestVec,ProbMat){
+ 
+ 
+ ProbTestMat=matrix(0,2,dim(ProbMat)[2])
+ #TestVec is the member of interests' vote record
+ 
+ for (j in 1:length(TestVec)){
+ for (k in 1:2){
+ #Compute probabilities if vote yes or no via if loop
+ 
+ if (TestVec[j]=="y"){
+ ProbTestMat[k,j]=ProbMat[k,j]
+ } else {
+ ProbTestMat[k,j]=1-ProbMat[k,j]
+ }
+ }
+ }
+ 
+ ProbTestMat[1:2,(length(TestVec)+1)]=ProbMat[1:2,(length(TestVec)+1)]
+ Probs=apply(ProbTestMat,1,prod) #Compute product of probabilities for the candidate being of either party
+ ind=which.max(Probs) #Find which probability is higher
+ AssignedVec=c(Probs,unique(TrainingData$Party)[ind]) #Probability of being a democrat, being a republican, and which one is assigned
+ 
+ 
+ return(list(AssignedVec=as.numeric(AssignedVec[1:2]),AssignedParty=AssignedVec[3])) #Elements returned as a list.
+}
+
+
+for (i in 1:dim(TestData)[1]){
+ for (j in 3:dim(TestData)[2]){
+ 
+ TestVec=TestData[i,3:dim(TestData)[2]]
+ result<-VotingModel_fn(TestVec,ProbMat)
+ AssignedMat[i,]=c(as.numeric(result$AssignedVec),result$AssignedParty)
+ }
+}
+ 
+
+CheckMat=data.frame(cbind(TestData$Party,AssignedMat[,3]))
+colnames(CheckMat)=c("Actual","Assigned")
+Pct_Accuracy=sum(CheckMat$Actual==CheckMat$Assigned)/dim(TestData)[1] #computes the percent accuracy
+
+
+print("Classifier Percent Accuracy") #Print our accuracy as percent value.
+print(Pct_Accuracy)
+
+Example=read.csv("ArbitraryMember.csv")
+result<-VotingModel_fn(Example,ProbMat)
+print(result)
+
+#Executing function in a sample data set to predict likelihood of voting
+Example=read.csv("ArbitraryMember.csv") #load data
+
+result<-VotingModel_fn(Example,ProbMat)
+print(result)
+