数据分析整理，R，caret包（1）

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library(C50)

library(lattice)
library(ggplot2)
library(caret)

#多药耐药逆转剂

#528 obs. of  342 variables

#528个化合物，342个描述符

data(mdrr)

# 0 variance

newdata <- mdrrDescr[, -nearZeroVar(mdrrDescr)]
# high cor
descrCorr <- cor(newdata)
newdata2 <- newdata[, -findCorrelation(descrCorr)]


# 去掉共线性(如果存在)
comboInfo <- findLinearCombos(newdata2)
if(!is.null(comboInfo)){
  newdata3 <- newdata2[, -comboInfo$remove]
} 


# 如果有缺失值,使用bagImpute,knnImpute进行计算填补
if(nrow(newdata2[!complete.cases(newdata2),])!=0)
{
  process <- preProcess(newdata2, method="bagImpute")
  pre <- predict(process, newdata2)
}


# feather selection
# 产生检测属性个数的序列
subsets <- seq(2, ncol(newdata2), by=2)




# define sbfControl
sbfControls_rf <- sbfControl(  functions = rfSBF,  method = 'cv',  repeats = 5)

# sbf: feature selection
pro <- sbf(newdata2, mdrrClass, sizes = subsets, sbfControl=sbfControls_rf)
summary(pro)


# feature selected variables
pro$optVariables


# 训练模型
# 获取特征选择后的属性
newdata4 <- newdata2[, pro$optVariables]


# 训练数据和测试数据


index <- createDataPartition(mdrrClass, p=3/4, list=F)


trainx <- newdata4[index,]
trainy <- mdrrClass[index]


testx <- newdata4[-index,]
testy <- mdrrClass[-index]
# 设置模型训练参数并拟合模型
fitControl <- trainControl(method="repeatedcv", number=10, repeats=3, returnResamp="all")


gbmGrid <- expand.grid(interaction.depth=c(1,3), n.trees=seq(50,300,by=50), shrinkage=0.1,n.minobsinnode = 20)


gbmFit1 <- train(trainx, trainy, method="gbm", trControl=fitControl, tuneGrid= gbmGrid, verbose=F)
trainControl

png("foo.png",family="GB1")
plot(gbmFit1)


dev.off()

# 使用训练好的模型进行predict
predict(gbmFit1, newdata=testx)
# 混淆矩阵查看结果
table(testy, predict(gbmFit1, newdata=testx))

testy      Active Inactive
  Active       61       13
  Inactive     12       45

# 使用另外的模型(装袋法)
gbmFit2 <- train(trainx, trainy, method="treebag", trControl=fitControl)
table(testy, predict(gbmFit2, newdata=testx))

testy      Active Inactive
  Active       64       10
  Inactive     13       44

models <- list(gbmFit1, gbmFit2);


predValues <- extractPrediction(models, testX=testx, testY=testy)

# predValues <- extractPrediction(models, testX=testx)

      obs     pred model dataType  object
1   Active   Active   gbm Training Object1
2   Active   Active   gbm Training Object1
3 Inactive Inactive   gbm Training Object1
4   Active Inactive   gbm Training Object1
5   Active   Active   gbm Training Object1
6   Active   Active   gbm Training Object1

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