#################################################
# C05: 整合學習                                 #
# 吳漢銘 國立政治大學統計學系                   #
# https://hmwu.idv.tw/                          #
#################################################


# 4/29

prediction.accuracy.rate <- function(no.classifier=1, accuracy.rate=0.5){
   c(no.classifiers=no.classifier, 
     at.least.one.accuracy=1-(1-accuracy.rate)^no.classifier)
}
prediction.accuracy.rate()
t(sapply(1:10, prediction.accuracy.rate))



# 6/29
# install.packages("bootstrap")
library(bootstrap)
jackknife


# 7/29
set.seed(12345)
x <- runif(30) 
n <- length(x)
theta <- CV(x)
CV <- function(x) sqrt(var(x))/mean(x)
theta.i <- sapply(1:n, function(i) CV(x[-i]))
theta.i
theta.jack <- n*theta - (n-1)*mean(theta.i)
theta.jack
plot(theta.i)


# 8/29
library(bootstrap)
set.seed(12345)
n <- 50; p <- 5
mydata <- as.data.frame(matrix(rnorm(n*p), ncol=p))
head(mydata, 3)
model.lm <- formula(V1 ~ V2 + V3 + V4)             
theta <- function(x, xdata, coefficient){
    coef(lm(model.lm, data=xdata[x, ]))[coefficient] 
 }
results <- jackknife(1:n, theta, xdata=mydata, coefficient="(Intercept)")
results


# 11/29
set.seed(12345)
x <- runif(30) 
CV <- function(x) sqrt(var(x))/mean(x)
CV(x)
CV(sample(x, replace=T)) 
boot <- replicate(n=100, expr=CV(sample(x, replace=T)))
boot
mean(boot)
var(boot)
hist(boot)


# 12/29
library(bootstrap)
set.seed(12345)
x <- rnorm(20)                
mean(x)
x.bootstrap.mean <- bootstrap(x, 100, theta=mean)
x.bootstrap.mean
mean(x.bootstrap.mean$thetastar)

mu.hat <- mean(x)
n <- length(x) 
ja <- jackknife(x, mean)
mu.hat.jack <- n*mu.hat - (n-1)*mean(ja$jack.values)
mu.hat.jack <- mu.hat - ja$jack.bias


# 15/29
library(rpart); library(mlbench); library(adabag)
data(Vehicle)
dim(Vehicle)
head(Vehicle)
table(Vehicle$Class)

n <- nrow(Vehicle)
sub <- sample(1:n, 2*n/3)
Vehicle.train <- Vehicle[sub, ]
Vehicle.test <- Vehicle[-sub, ]

mfinal <- 10 #Defaults to mfinal=100 iterations
maxdepth <- 5
# apply rpart
Vehicle.rpart <- rpart(Class ~ ., data = Vehicle.train, maxdepth = maxdepth)
Vehicle.rpart.pred <- predict(Vehicle.rpart, newdata = Vehicle.test, type = "class")
(tb <- table(Vehicle.rpart.pred, Observed.Class=Vehicle.test$Class))
(error.rpart <- 1 - (sum(diag(tb)) / sum(tb)))


# 16/29
library(adabag)
Vehicle.adaboost <- boosting(Class ~., data = Vehicle.train, mfinal = mfinal, 
                              control = rpart.control(maxdepth=maxdepth))
Vehicle.adaboost.pred <- predict.boosting(Vehicle.adaboost, newdata = Vehicle.test)
Vehicle.adaboost.pred$confusion
Vehicle.adaboost.pred$error
importanceplot(Vehicle.adaboost)
evol.train <- errorevol(Vehicle.adaboost, newdata = Vehicle.train)
evol.test <- errorevol(Vehicle.adaboost, newdata = Vehicle.test)
plot.errorevol(evol.test, evol.train)

sort(Vehicle.adaboost$importance, dec=T)[1:5]


# 17/29
Vehicle.boost.cv <- boosting.cv(Class ~., data = Vehicle, v = 10, mfinal = 5, 
                                control = rpart.control(maxdepth=maxdepth))
Vehicle.boost.cv$confusion
Vehicle.boost.cv$error


Vehicle.bag.cv <- bagging.cv(Class ~., data = Vehicle, v = 10, mfinal = 5, 
                                control = rpart.control(maxdepth=maxdepth))
Vehicle.bag.cv$confusion
Vehicle.bag.cv$error


# 23/29
library(unbalanced)

data(ubIonosphere)
dim(ubIonosphere)
head(ubIonosphere)
table(ubIonosphere$Class)

p <- ncol(ubIonosphere)
y <- ubIonosphere$Class
x <- ubIonosphere[ ,-p]
data <- ubBalance(X=x, Y=y, type="ubOver", k=0)
overData <- data.frame(data$X, Class=data$Y)
table(overData$Class)
data <- ubBalance(X=x, Y=y, type="ubUnder", perc=50, method="percPos")
underData <- data.frame(data$X, Class=data$Y)
table(underData$Class)
bdata <- ubBalance(X= x, Y=y, type="ubSMOTE", percOver=300, percUnder=150, verbose=TRUE)
str(bdata)
table(bdata$Y)




# 24/29
set.seed(12345)
n <- nrow(ubIonosphere) # 351
no.train <- floor(0.5*n) # 175
id <- sample(1:n, no.train)
x.train  <- x[id, ]  # 175 x 32
y.train <- y[id]
x.test <- x[-id, ] # 176  32
y.test <- y[-id]

library(e1071)
model1 <- svm(x.train, y.train) 
y.pred1 <- predict(model1, x.test)
table(y.pred1, y.test)

balancedData <- ubBalance(X=x.train, Y=y.train, type="ubSMOTE", percOver=200, percUnder=150)
table(balancedData$Y)

model2 <- svm(balancedData$X, balancedData$Y)
y.pred2 <- predict(model2, x.test)
table(y.pred2, y.test)


# 25/29
set.seed(1234)
load("creditcard.Rdata")
str(creditcard)
table(creditcard$Class)

ubConf <- list(percOver=200, percUnder=200, k=2, perc=50, method="percPos", w=NULL)
results <- ubRacing(Class ~., creditcard, "randomForest", 
                    positive=1, metric="auc", ubConf=ubConf, ntree=5)
results 

# 27/29

results <- ubRacing(Class ~., creditcard, "randomForest", positive=1, metric="auc", ubConf=ubConf, ncore=4)
library(e1071)
results <- ubRacing(Class ~., creditcard, "svm", positive=1, ubConf=ubConf)
library(rpart)
results <- ubRacing(Class ~., creditcard, "rpart", positive=1, ubConf=ubConf)
results