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