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# install xgboost package, see R-package in root folder
require(xgboost)
require(methods)
testsize <- 550000
dtrain <- read.csv("data/training.csv", header=TRUE)
dtrain[33] <- dtrain[33] == "s"
label <- as.numeric(dtrain[[33]])
data <- as.matrix(dtrain[2:31])
weight <- as.numeric(dtrain[[32]]) * testsize / length(label)
sumwpos <- sum(weight * (label==1.0))
sumwneg <- sum(weight * (label==0.0))
print(paste("weight statistics: wpos=", sumwpos, "wneg=", sumwneg, "ratio=", sumwneg / sumwpos))
xgmat <- xgb.DMatrix(data, label = label, weight = weight, missing = -999.0)
param <- list("objective" = "binary:logitraw",
"scale_pos_weight" = sumwneg / sumwpos,
"bst:eta" = 0.1,
"bst:max_depth" = 6,
"eval_metric" = "auc",
"eval_metric" = "ams@0.15",
"nthread" = 16)
watchlist <- list("train" = xgmat)
nrounds = 120
print ("loading data end, start to boost trees")
bst = xgb.train(param, xgmat, nrounds, watchlist );
# save out model
xgb.save(bst, "higgs.model")
print ('finish training')
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