R代码代修改、R编程辅导修改
辅导Python编程|讲解Python程序
# 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)