吴裕雄--天生自然 R语言开发学习：分类（续二）

#-----------------------------------------------------------------------------#
# R in Action (2nd ed): Chapter 17                                            #
# Classification                                                              #
# requires packaged rpart, party, randomForest, kernlab, rattle               #
# install.packages(c("rpart", "party", "randomForest", "e1071", "rpart.plot") #
# install.packages(rattle, dependencies = c("Depends", "Suggests"))           #
#-----------------------------------------------------------------------------#

par(ask=TRUE)

# Listing 17.1 - Prepare the breast cancer data
loc <- "http://archive.ics.uci.edu/ml/machine-learning-databases/"
ds  <- "breast-cancer-wisconsin/breast-cancer-wisconsin.data"
url <- paste(loc, ds, sep="")

breast <- read.table(url, sep=",", header=FALSE, na.strings="?")
names(breast) <- c("ID", "clumpThickness", "sizeUniformity",
                   "shapeUniformity", "maginalAdhesion", 
                   "singleEpithelialCellSize", "bareNuclei", 
                   "blandChromatin", "normalNucleoli", "mitosis", "class")

df <- breast[-1]
df$class <- factor(df$class, levels=c(2,4), 
                   labels=c("benign", "malignant"))

set.seed(1234)
train <- sample(nrow(df), 0.7*nrow(df))
df.train <- df[train,]
df.validate <- df[-train,]
table(df.train$class)
table(df.validate$class)

# Listing 17.2 - Logistic regression with glm()
fit.logit <- glm(class~., data=df.train, family=binomial())
summary(fit.logit)
prob <- predict(fit.logit, df.validate, type="response")
logit.pred <- factor(prob > .5, levels=c(FALSE, TRUE), 
                     labels=c("benign", "malignant"))
logit.perf <- table(df.validate$class, logit.pred,
                    dnn=c("Actual", "Predicted"))
logit.perf


# Listing 17.3 - Creating a classical decision tree with rpart()
library(rpart)
set.seed(1234)
dtree <- rpart(class ~ ., data=df.train, method="class",      
               parms=list(split="information"))
dtree$cptable
plotcp(dtree)

dtree.pruned <- prune(dtree, cp=.0125) 

library(rpart.plot)
prp(dtree.pruned, type = 2, extra = 104,  
    fallen.leaves = TRUE, main="Decision Tree")

dtree.pred <- predict(dtree.pruned, df.validate, type="class")
dtree.perf <- table(df.validate$class, dtree.pred, 
                    dnn=c("Actual", "Predicted"))
dtree.perf


# Listing 17.4 - Creating a conditional inference tree with ctree()
library(party)
fit.ctree <- ctree(class~., data=df.train)
plot(fit.ctree, main="Conditional Inference Tree")

ctree.pred <- predict(fit.ctree, df.validate, type="response")
ctree.perf <- table(df.validate$class, ctree.pred, 
                    dnn=c("Actual", "Predicted"))
ctree.perf


# Listing 17.5 - Random forest
library(randomForest)
set.seed(1234)
fit.forest <- randomForest(class~., data=df.train,        
                           na.action=na.roughfix,
                           importance=TRUE)             
fit.forest
importance(fit.forest, type=2)                          
forest.pred <- predict(fit.forest, df.validate)         
forest.perf <- table(df.validate$class, forest.pred, 
                     dnn=c("Actual", "Predicted"))
forest.perf


# Listing 17.6 - A support vector machine
library(e1071)
set.seed(1234)
fit.svm <- svm(class~., data=df.train)
fit.svm
svm.pred <- predict(fit.svm, na.omit(df.validate))
svm.perf <- table(na.omit(df.validate)$class, 
                  svm.pred, dnn=c("Actual", "Predicted"))
svm.perf


# Listing 17.7 Tuning an RBF support vector machine (this can take a while)
set.seed(1234)
tuned <- tune.svm(class~., data=df.train,
                  gamma=10^(-6:1),
                  cost=10^(-10:10))
tuned
fit.svm <- svm(class~., data=df.train, gamma=.01, cost=1)
svm.pred <- predict(fit.svm, na.omit(df.validate))
svm.perf <- table(na.omit(df.validate)$class,
                  svm.pred, dnn=c("Actual", "Predicted"))
svm.perf


# Listing 17.8 Function for assessing binary classification accuracy
performance <- function(table, n=2){
  if(!all(dim(table) == c(2,2)))
    stop("Must be a 2 x 2 table")
  tn = table[1,1]
  fp = table[1,2]
  fn = table[2,1]
  tp = table[2,2]
  sensitivity = tp/(tp+fn)
  specificity = tn/(tn+fp)
  ppp = tp/(tp+fp)
  npp = tn/(tn+fn)
  hitrate = (tp+tn)/(tp+tn+fp+fn)
  result <- paste("Sensitivity = ", round(sensitivity, n) ,
                  "\nSpecificity = ", round(specificity, n),
                  "\nPositive Predictive Value = ", round(ppp, n),
                  "\nNegative Predictive Value = ", round(npp, n),
                  "\nAccuracy = ", round(hitrate, n), "\n", sep="")
  cat(result)
}


# Listing 17.9 - Performance of breast cancer data classifiers
performance(dtree.perf)
performance(ctree.perf)
performance(forest.perf)
performance(svm.perf)


# Using Rattle Package for data mining

loc <- "http://archive.ics.uci.edu/ml/machine-learning-databases/"
ds <- "pima-indians-diabetes/pima-indians-diabetes.data"
url <- paste(loc, ds, sep="")
diabetes <- read.table(url, sep=",", header=FALSE)
names(diabetes) <- c("npregant", "plasma", "bp", "triceps",
                     "insulin", "bmi", "pedigree", "age", "class")
diabetes$class <- factor(diabetes$class, levels=c(0,1),
                         labels=c("normal", "diabetic"))
library(rattle)
rattle()