R语言包_gbm

gbm效果和randomForest相近，但是占用内存更少，且支持多核crossValidation运算。

本文用到的处理二值数据的方法，有以下两种：

1. glm(generalized boosted models)
2. glmnet(generalized linear models)

glm使用了boosted trees，glmnet使用了regression

# load libraries
library(caret)
library(pROC)

#################################################
# data prep
#################################################

# load data
titanicDF <- read.csv('http://math.ucdenver.edu/RTutorial/titanic.txt',sep='\t')
titanicDF$Title <- ifelse(grepl('Mr ',titanicDF$Name),'Mr',ifelse(grepl('Mrs ',titanicDF$Name),'Mrs',ifelse(grepl('Miss',titanicDF$Name),'Miss','Nothing'))) 
titanicDF$Age[is.na(titanicDF$Age)] <- median(titanicDF$Age, na.rm=T)

# miso format
titanicDF <- titanicDF[c('PClass', 'Age',    'Sex',   'Title', 'Survived')]

# dummy variables for factors/characters
titanicDF$Title <- as.factor(titanicDF$Title)
titanicDummy <- dummyVars("~.",data=titanicDF, fullRank=F)
titanicDF <- as.data.frame(predict(titanicDummy,titanicDF))
print(names(titanicDF))


# what is the proportion of your outcome variable?
prop.table(table(titanicDF$Survived))

# save the outcome for the glmnet model
tempOutcome <- titanicDF$Survived  

# generalize outcome and predictor variables
outcomeName <- 'Survived'
predictorsNames <- names(titanicDF)[names(titanicDF) != outcomeName]

#################################################
# model it
#################################################
# get names of all caret supported models 
names(getModelInfo())

titanicDF$Survived <- ifelse(titanicDF$Survived==1,'yes','nope')

# pick model gbm and find out what type of model it is
getModelInfo()$gbm$type

# split data into training and testing chunks
set.seed(1234)
splitIndex <- createDataPartition(titanicDF[,outcomeName], p = .75, list = FALSE, times = 1)
trainDF <- titanicDF[ splitIndex,]
testDF  <- titanicDF[-splitIndex,]

# create caret trainControl object to control the number of cross-validations performed
objControl <- trainControl(method='cv', number=3, returnResamp='none', summaryFunction = twoClassSummary, classProbs = TRUE)


# run model
objModel <- train(trainDF[,predictorsNames], as.factor(trainDF[,outcomeName]), 
                  method='gbm', 
                  trControl=objControl,  
                  metric = "ROC",
                  preProc = c("center", "scale"))
)

# find out variable importance
summary(objModel)

# find out model details
objModel

#################################################
# evalute mdoel
#################################################
# get predictions on your testing data

# class prediction
predictions <- predict(object=objModel, testDF[,predictorsNames], type='raw')
head(predictions)
postResample(pred=predictions, obs=as.factor(testDF[,outcomeName]))

# probabilites 
predictions <- predict(object=objModel, testDF[,predictorsNames], type='prob')
head(predictions)
postResample(pred=predictions, obs=testDF[,outcomeName])

auc <- roc(ifelse(testDF[,outcomeName]=="yes",1,0), predictions[[2]])
print(auc$auc)


################################################
# glm model
################################################

# pick model gbm and find out what type of model it is
getModelInfo()$glmnet$type

# save the outcome for the glmnet model
titanicDF$Survived  <- tempOutcome

# split data into training and testing chunks
set.seed(1234)
splitIndex <- createDataPartition(titanicDF[,outcomeName], p = .75, list = FALSE, times = 1)
trainDF <- titanicDF[ splitIndex,]
testDF  <- titanicDF[-splitIndex,]

# create caret trainControl object to control the number of cross-validations performed
objControl <- trainControl(method='cv', number=3, returnResamp='none')

# run model
objModel <- train(trainDF[,predictorsNames], trainDF[,outcomeName], method='glmnet',  metric = "RMSE")

# get predictions on your testing data
predictions <- predict(object=objModel, testDF[,predictorsNames])

library(pROC)
auc <- roc(testDF[,outcomeName], predictions)
print(auc$auc)

postResample(pred=predictions, obs=testDF[,outcomeName])

# find out variable importance
summary(objModel)
plot(varImp(objModel,scale=F))

# find out model details
objModel

# display variable importance on a +/- scale 
vimp <- varImp(objModel, scale=F)
results <- data.frame(row.names(vimp$importance),vimp$importance$Overall)
results$VariableName <- rownames(vimp)
colnames(results) <- c('VariableName','Weight')
results <- results[order(results$Weight),]
results <- results[(results$Weight != 0),]

par(mar=c(5,15,4,2)) # increase y-axis margin. 
xx <- barplot(results$Weight, width = 0.85, 
              main = paste("Variable Importance -",outcomeName), horiz = T, 
              xlab = "< (-) importance >  < neutral >  < importance (+) >", axes = FALSE, 
              col = ifelse((results$Weight > 0), 'blue', 'red')) 
axis(2, at=xx, labels=results$VariableName, tick=FALSE, las=2, line=-0.3, cex.axis=0.6)  


################################################
# advanced stuff
################################################

# boosted tree model (gbm) adjust learning rate and and trees
gbmGrid <-  expand.grid(interaction.depth =  c(1, 5, 9),
                        n.trees = 50,
                        shrinkage = 0.01)

# run model
objModel <- train(trainDF[,predictorsNames], trainDF[,outcomeName], method='gbm', trControl=objControl, tuneGrid = gbmGrid, verbose=F)

# get predictions on your testing data
predictions <- predict(object=objModel, testDF[,predictorsNames])

library(pROC)
auc <- roc(testDF[,outcomeName], predictions)
print(auc$auc)

参考资料

DataExploration博客