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Bosch 2.R
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Bosch 2.R
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########################################################################################
# Part II: Perform knn classified on DR data using Random Forest #
# by Shiou-Shiou Deng #
########################################################################################
# Load data
data_numeric <- read.csv("train_numeric.csv", header = TRUE, sep = ",", nrows = 10000)
data_categorical <- read.csv("train_categorical.csv", header = TRUE, sep = ",", nrows = 10000, stringsAsFactors = FALSE)
# Load the package
library(randomForest)
# Data processing
# Replace all Na value to 0 in numerica data
data_numeric[is.na(data_numeric)] <- 0
# Check all texts in the dataset
#words <- character(0)
#for (i in 2:ncol(data_categorical)){
# ignore the logical columns
# if(is.logical(data_categorical[,i]) == FALSE){
# indexw <- which(data_categorical[,i] %in% "" == FALSE)
# dictionary <-
# words <- append(words, data_categorical[,i][indexw])
# }
#}
# Make a data.frame to represent the categorical data
categorical <- matrix(0, nrow = nrow(data_categorical), ncol = ncol(data_categorical))
colnames(categorical) <- dimnames(data_categorical)[[2]]
for (i in 2:ncol(data_categorical)){
if (is.logical(data_categorical[,i]) == FALSE){
indexw <- which(data_categorical[,i] %in% "" == FALSE)
categorical[,i][indexw] <- 1
}
}
categorical <- as.data.frame(categorical)
# Divide the data into 2 part (80% training and validation, 20% testing)
set.seed(1234)
index <- sample(2, nrow(data_numeric), replace=TRUE, prob=c(0.8, 0.2))
train <- data_numeric[index==1,]
trainC <- categorical[index==1,]
test <- data_numeric[index==2,]
testC <- categorical[index==2,]
# Reliease data space for use
rm(data_categorical, data_numeric, categorical)
gc()
# Divide the data to the training data and the response.
train_response <- train[,970]
train <- train[,2:969]
trainC <- trainC[,2:ncol(trainC)]
trainB <- cbind(train, trainC)
test_response <- test[,970]
test <- test[,2:(ncol(test)-1)]
testC <- testC[,2:ncol(testC)]
testB <- cbind(test, testC)
# Make a function to Normalize the data
normalize <- function(x) {
denominator <- sqrt(sum(x^2))
if (denominator!=0){
norm2 <- x/denominator
} else {
norm2 <- x
}
return(norm2)
}
# Make a assement function to evaluate the results
Assement <- function(TP, FP, FN, TN){
Accuracy = (TP+TN)/ (TP+TN+FP+FN)
MCC = (TP*TN - FP*FN)/sqrt((TP+FP)*(TP+FN)*(TN+FP)*(TN+FN))
Sensitivity = TP / (TP+FN)
Precision = TP/(TP+FP)
F.measure = 2*TP/(2*TP+FN+FP)
return(c(Accuracy, Sensitivity, Precision, F.measure, MCC))
}
# Perform random forest algorithm to reduce the dimesnsions of the training data
set.seed(1234)
model <- randomForest(as.factor(train_response) ~., data = trainB, ntree=500, mtry=sqrt(ncol(trainB)),proximity=TRUE, maxnodes = 5)
varImpPlot(model, n.var = 15, main = "The top 15 Important Features")
# Find the features which the MeanDecreaseGini is equal to 0
ind0 <- importance(model) > 0
# Find the features which the MeanDecreaseGini is small than 0.01
ind1 <- importance(model) > 0.01
# Find features which give contributions on data
num_id <- ind1[1:ncol(train)]
cat_id <- ind1[(ncol(train)+1):ncol(trainB)]
# Get the new data set after applying Dimension Reduction and normalize the data
newtrain <- train[,num_id]
newtrainC <- trainC[,cat_id]
newtest <- test[,num_id]
newtestC <- testC[,cat_id]
newtrainC <- matrix((sapply(newtrainC, normalize)), nrow = nrow(newtrainC))
newtrain <- matrix((sapply(newtrain, normalize)), nrow = nrow(newtrain))
newtestC <- matrix(sapply(newtestC, normalize), nrow = nrow(newtestC))
newtest <- matrix(sapply(newtest, normalize), nrow = nrow(newtest))
# Setting the k value
k <- 10
# Make a function to perform knn model on combination data
knn.comb <- function(train, trainC, valid, validC, train_response, k, alpha = 1, beta = 0){
predict_response <- numeric(nrow(valid))
# Compute the distances from the each valid data to all train data
distancesC <- beta*(1 - validC %*% t(trainC))
distancesN <- alpha*(1 - valid %*% t(train))
distances <- distancesC + distancesN
# predict the new variable
for (m in 1:nrow(valid)) {
neighbors_response <- numeric(k)
ind <- order(distances[m,])
neighbors_response <- train_response[ind][1:k]
if (sum(neighbors_response) == (k/2)) {
predict_response[m] <- ifelse(runif(1,0,1)>0.5, 1, 0)
} else if (sum(neighbors_response) > (k/2)){
predict_response[m] <- 1
} else {
predict_response[m] <- 0
}
}
return(predict_response)
}
# Perform KNN on train data using different k, alpha, beta
alpha <- seq(0, 1, by = 0.1)
beta <- 1 - alpha
totalresults <- matrix(0, nrow = 0, ncol = 5)
# Use different combinations of weights for numerical data and categorical data (alpha, beta)
for (j in 1:length(alpha)){
# Use 10-fold cross validation
set.seed(1234)
folds_i <- sample(rep(1:10, length.out = nrow(train)))
# Store the results after applying models
resultsB <- matrix(0, nrow = k, ncol = 5)
colnames(resultsB) <- c("Accuracy", "Sensitivity", "Precision", "F-measure","MCC" )
rownames(resultsB) <- rownames(resultsB, do.NULL = FALSE, prefix = "k = ")
for (i in 1:k){
cvresult <- matrix(0, nrow = 10, ncol = 5)
for (n in 1:10) {
valid_i <- which(folds_i == n)
train_dataN <- newtrain[-valid_i, ]
train_dataC <- newtrainC[-valid_i, ]
trainlabel <- train_response[-valid_i]
valid_dataN <- newtrain[valid_i, ]
valid_dataC <- newtrainC[valid_i, ]
validlabel <- train_response[valid_i]
cat("alpha= ", alpha[j],"\n")
cat("k= ", i,"\n")
cat("fold= ", n,"\n")
model <- knn.comb(train_dataN, train_dataC, valid_dataN, valid_dataC, trainlabel, k=i, alpha = alpha[j], beta = beta[j])
confusion.matrix <- table(factor(validlabel), factor(model, levels = c(0,1)))
cvresult[n,] <- Assement(confusion.matrix[4], confusion.matrix[3], confusion.matrix[2], confusion.matrix[1])
resultsB[i,] <- colMeans(cvresult)
}
}
totalresults <- rbind(totalresults, resultsB)
}
# Apply the optimatized models on test data
# Perform KNN on train data using different k, alpha, beta
totalresultsT <- matrix(0, nrow = 0, ncol = 5)
# Store the results after applying models
k <- 5
# Use different combinations of weights for numerical data and categorical data (alpha, beta)
for (j in 1:length(alpha)){
# Store the results after applying models
resultsT <- matrix(0, nrow = k, ncol = 5)
colnames(resultsT) <- c("Accuracy", "Sensitivity", "Precision", "F-measure","MCC" )
rownames(resultsT) <- rownames(resultsT, do.NULL = FALSE, prefix = "k = ")
for (i in 1:k){
cat("alpha= ", alpha[j],"\n")
cat("k= ", i,"\n")
model <- knn.comb(newtrain, newtrainC, newtest, newtestC, train_response, k=i, alpha = alpha[j], beta = beta[j])
confusion.matrix <- table(factor(test_response), factor(model, levels = c(0,1)))
resultsT[i,] <- Assement(confusion.matrix[4], confusion.matrix[3], confusion.matrix[2], confusion.matrix[1])
}
totalresultsT <- rbind(totalresultsT, resultsT)
}
# Plot the validation parts to visualize the results
# Set the plots' parameters
par(mfrow=c(1,1))
opts = c("p","l","o","b","c")
colors <- rainbow(5)
linetype <- c(1:5)
plotchar <- seq(18,18+5,1)
# Plot the results using different combination data
# alpha = 0.1, beta = 0.9
plot(0, 0, xlim = c(0, 20), ylim = c(0,1), type="n", xlab="K", ylab="Percentage" )
title(main = "Combination Data (alpha=0.1, beta=0.9)", col.lab="black", font.lab = 4, cex.lab= 1.2)
for (i in 1:5){
lines(1:20, totalresults[21:40,i], col=colors[i], lwd=2, lty=linetype[i], type="b", pch=plotchar[i])
}
legend("topright", legend = c("A", "S", "P", "F", "MCC"), cex=0.8, col=colors, pch=plotchar, lty=linetype)
# alpha = 0.5, beta = 0.5
plot(0, 0, xlim = c(0, 20), ylim = c(0,1), type="n", xlab="K", ylab="Percentage" )
title(main = "Combination Data (alpha=0.5, beta=0.5)", col.lab="black", font.lab = 4, cex.lab= 1.2)
for (i in 1:5){
lines(1:20, totalresults[101:120,i], col=colors[i], lwd=2, lty=linetype[i], type="b", pch=plotchar[i])
}
legend("topright", legend = c("A", "S", "P", "F", "MCC"), cex=0.8, col=colors, pch=plotchar, lty=linetype)
# alpha = 0.9, beta = 0.1
plot(0, 0, xlim = c(0, 20), ylim = c(0,1), type="n", xlab="K", ylab="Percentage" )
title(main = "Combination Data (alpha=0.9, beta=0.1)", col.lab="black", font.lab = 4, cex.lab= 1.2)
for (i in 1:5){
lines(1:20, totalresults[181:200,i], col=colors[i], lwd=2, lty=linetype[i], type="b", pch=plotchar[i])
}
legend("topright", legend = c("A", "S", "P", "F", "MCC"), cex=0.8, col=colors, pch=plotchar, lty=linetype)
# Plot the results using different combination data given k = 1
plot(0, 0, xlim = c(0, 1), ylim = c(0,0.2), type="n", xlab="alpha", ylab="Percentage" )
title(main = "Combination Data (K = 1)", col.lab="black", font.lab = 4, cex.lab= 1.2)
for (i in 1:4){
lines(seq(0,1,by=0.1), totalresults[1+20*(0:10),i+1], col=colors[i+1], lwd=2, lty=linetype[i+1], type="b", pch=plotchar[i+1])
}
legend("topright", legend = c("S", "P", "F", "MCC"), cex=0.8, col=colors[2:5], pch=plotchar[2:5], lty=linetype[2:5])
# Plot the results using different combination data given k = 2
plot(0, 0, xlim = c(0, 1), ylim = c(0,0.2), type="n", xlab="alpha", ylab="Percentage" )
title(main = "Combination Data (K = 2)", col.lab="black", font.lab = 4, cex.lab= 1.2)
for (i in 1:4){
lines(seq(0,1,by=0.1), totalresults[2+20*(0:10),i+1], col=colors[i+1], lwd=2, lty=linetype[i+1], type="b", pch=plotchar[i+1])
}
legend("topright", legend = c("S", "P", "F", "MCC"), cex=0.8, col=colors[2:5], pch=plotchar[2:5], lty=linetype[2:5])
# Plot the test parts to visualize the results
# Given k = 1, comparing the results in different combinations
plot(0, 0, xlim = c(0, 1), ylim = c(0,1), type="n", xlab="alpha", ylab="Percentage" )
title(main = "Combination Data (K = 1)", col.lab="black", font.lab = 4, cex.lab= 1.2)
for (i in 1:5){
lines(seq(0,1,by=0.1), totalresultsT[1+5*(0:10),i], col=colors[i], lwd=2, lty=linetype[i], type="b", pch=plotchar[i])
}
legend("topright", legend = c("A", "S", "P", "F", "MCC"), cex=0.8, col=colors, pch=plotchar, lty=linetype)
# Given k = 2, comparing the results in different combinations
plot(0, 0, xlim = c(0, 1), ylim = c(0,1), type="n", xlab="alpha", ylab="Percentage" )
title(main = "Combination Data (K = 2)", col.lab="black", font.lab = 4, cex.lab= 1.2)
for (i in 1:5){
lines(seq(0,1,by=0.1), totalresultsT[2+5*(0:10),i], col=colors[i], lwd=2, lty=linetype[i], type="b", pch=plotchar[i])
}
legend("topright", legend = c("A", "S", "P", "F", "MCC"), cex=0.8, col=colors, pch=plotchar, lty=linetype)
# Given k = 5, comparing the results in different combinations
plot(0, 0, xlim = c(0, 1), ylim = c(0,1), type="n", xlab="alpha", ylab="Percentage" )
title(main = "Combination Data (K = 5)", col.lab="black", font.lab = 4, cex.lab= 1.2)
for (i in 1:5){
lines(seq(0,1,by=0.1), totalresultsT[5+5*(0:10),i], col=colors[i], lwd=2, lty=linetype[i], type="b", pch=plotchar[i])
}
legend("topright", legend = c("A", "S", "P", "F", "MCC"), cex=0.8, col=colors, pch=plotchar, lty=linetype)