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@Lanbig Lanbig/Ensemble.R
Created May 13, 2016

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setwd("~/Dropbox/CSC529/Project")
require(caret)
require(ggplot2)
require(randomForest)
require(e1071)
require(pROC)
require("foreach")
require("doSNOW")
require(party)
myd = read.csv("Lung Cancer dataset/LIDC_Data_SEIDEL_01-05-2015.csv",header = T, sep = ',')
mycl = data.frame(matrix(NA, ncol = 1, nrow = nrow(myd)) )
names(mycl)[1]<-paste("class")
for(i in 1:nrow(myd))
{
row <- myd[i,]
avg <- (row[5]+row[6]+row[7]+row[8])/4
mycl[i,1] <- ifelse(avg >=4 , "Likely", "Inconclusive")
}
qplot(mycl$class, geom="bar",ylab = "count", main = "Class Distribution ", xlab = "Class", fill=as.factor(mycl$class))
###############################################################
outcome = as.factor(mycl$class)
levels(outcome) = make.names(levels(outcome))
##################################
set.seed(4567)
#trainIndex <- createDataPartition(outcome , p=0.66, list=FALSE)
#train_X <- myd[,9:72][ trainIndex,]
#train_Y <- as.data.frame(outcome)[trainIndex,]
#test_X <- myd[,9:72][-trainIndex,]
#test_Y <- as.data.frame(outcome)[-trainIndex,]
##################################
train_X = myd[,9:72]
train_Y = outcome
####################### SMOTE #######################################
######## Balance the data set, comment this part if you don't want ########
require(DMwR)
sdata = cbind(train_Y,train_X)
names(sdata)[1]<-paste("class")
Bal_data_train <- SMOTE(class ~ ., sdata, perc.over = 500,perc.under=110)
table(Bal_data_train$class)
qplot(Bal_data_train$class, geom="bar",ylab = "count", main = "Class Distribution ", xlab = "Class", fill=Bal_data_train$class )
train_X = Bal_data_train[,2:65]
train_Y = as.factor(Bal_data_train[,1])
################### Feature Extraction ##############################
####### Extract new feature, reduce feture dimention, ############
####### comment this part if you don't want #####################
require(clusterSim)
fdata = cbind(train_Y,train_X)
Norm_data_train = data.Normalization(fdata[2:65],type="n4",normalization="column");
#Apply PCA
data_train.pca <- princomp(Norm_data_train, cor = TRUE, center = TRUE, scale. = TRUE)
plot(data_train.pca)
summary(data_train.pca)
train_X = data_train.pca$scores[,1:22]
train_Y = as.factor(Bal_data_train[,1])
############################################################
fitControl <- trainControl(method = "repeatedcv",
repeats = 10,
number = 10,
#summaryFunction = multiClassSummary,
summaryFunction = twoClassSummary,
savePredictions =T,
verboseIter = TRUE,
## Estimate class probabilities
classProbs = TRUE)
#######################################################
fit.rf = train(y = train_Y, x = train_X, method= "rf" ,
tuneLength = 5,
metric="ROC",
trControl = fitControl)
plot(fit.rf)
fit.rf
pred <- predict(fit.rf, train_X)
xtab <- table(pred, train_Y)
confusionMatrix(fit.rf)
#########################################################
fit.ada = train(y = train_Y, x = train_X, method= "ada" ,
metric="ROC",
trControl = fitControl)
plot(fit.ada)
fit.ada
trellis.par.set(caretTheme())
plot(fit.ada, metric = "ROC", plotType = "level",
scales = list(x = list(rot = 90)))
############################################################
fit.treeBag = train(y = train_Y, x = train_X, method= "treebag",
metric="ROC",
trControl = fitControl)
plot(fit.treeBag)
fit.treeBag
###########################################################
fit.LogitBoost = train(y = train_Y, x = train_X, method= "LogitBoost",
metric="ROC",
tuneLength = 8,
trControl = fitControl)
plot(fit.LogitBoost)
fit.LogitBoost
###########################################################
fit.AdaM1 = train(y = train_Y, x = train_X, method= "AdaBoost.M1",
metric="ROC",
trControl = fitControl)
plot(fit.AdaM1)
fit.AdaM1
###########################################################
fit.gbm = train(y = train_Y, x = train_X, method= "gbm",
metric="ROC",
trControl = fitControl)
plot(fit.gbm)
fit.gbm
##########################################################
fit.bagLDA <- train(train_X, train_Y,
"bag",
B = 10,
trControl = fitControl,
bagControl = bagControl(fit = ldaBag$fit,
predict = ldaBag$pred,
aggregate = ldaBag$aggregate),
tuneGrid = data.frame(vars = c((1:10)*10 , ncol(train_X))))
plot(fit.bagLDA)
fit.bagLDA
################Perf #######################################
resamps <- resamples(list(RF = fit.rf,
BoostedTree = fit.ada,
BaggedCart = fit.treeBag,
LogitBoost= fit.LogitBoost,
BaggedLDA = fit.bagLDA,
GBM = fit.gbm
))
resamps
summary(resamps)
bwplot(resamps, layout = c(5, 1))
##################### Testing ########################
pred <- predict(fit.rf, test_X)
xtab <- table(pred, test_Y)
confusionMatrix(xtab)
pred <- predict(fit.ada, test_X)
xtab <- table(pred, test_Y)
confusionMatrix(xtab)
pred <- predict(fit.treeBag, test_X)
xtab <- table(pred, test_Y)
confusionMatrix(xtab)
pred <- predict(fit.LogitBoost, test_X)
xtab <- table(pred, test_Y)
confusionMatrix(xtab)
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