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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", | |
| "silent" = 1, | |
| "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') | |