R代码代修改、R编程辅导修改

# Give-Me-Some-Credit

# This code is using XGboost in R

library(xgboost)

require(xgboost)

require(methods)

train = read.csv('train.csv',header=TRUE,stringsAsFactors = F)

test = read.csv('test.csv',header=TRUE,stringsAsFactors = F)

train = train[,-1]

test = test[,-1]

y = train[,ncol(train)]

y = gsub('Class_','',y)

y = as.integer(y)-1 #xgboost take features in [0,numOfClass)

x = rbind(train[,-ncol(train)],test)

x = as.matrix(x)

x = matrix(as.numeric(x),nrow(x),ncol(x))

trind = 1:length(y)

teind = (nrow(train)+1):nrow(x)

# Set necessary parameters

param <- list("objective" = "binary:logistic",

             "eval_metric" = "auc",

             "num_class" = 2,

             "nthread" = 10)

# Run Cross Valication

cv.nround = 50

bst.cv = xgb.cv(param=param, data = x[trind,] , label = y,

               nfold = 10, nrounds=cv.nround)

# Train the model

nround = 100

bst = xgboost(param=param, data = x[trind,], label = y, nrounds=nround)

# Make prediction

pred = predict(bst,x[teind,])

pred = matrix(pred,2,length(pred)/2)

pred = t(pred)

# Output submission

pred = format(pred, digits=2,scientific=F) # shrink the size of submission

pred = data.frame(1:nrow(pred),pred)

write.csv(pred,file='submission.csv', quote=FALSE,row.names=FALSE)

period <- 120

FullList <- 1:120

x <- train$MonthlyIncome

# "randomly" make 15 of the points "missing"

MissingList <- sample(x,15)

x[MissingList] <- NA

# Create sine curve with noise

y <- sin(2*pi*x/period) + runif(length(x),-1,1)

# Plot points on noisy curve

plot(x,y, main="Sine Curve + 'Uniform' Noise")

mtext("Using loess smoothed fit to impute missing values")

y.loess <- loess(y ~ x, span=0.75, data.frame(x=x, y=y))

y.predict <- predict(y.loess, data.frame(x=FullList))

# Plot the loess smoothed curve showing gaps for missing data

lines(x,y.predict,col=i)

y.Missing <- predict(y.loess, data.frame(x=MissingList))

points(MissingList, y.Missing, pch=FILLED.CIRCLE<-19, col=i)