41 lines
1.9 KiB
R
41 lines
1.9 KiB
R
require(xgboost)
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# load in the agaricus dataset
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data(agaricus.train, package = 'xgboost')
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data(agaricus.test, package = 'xgboost')
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dtrain <- xgb.DMatrix(agaricus.train$data, label = agaricus.train$label)
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dtest <- xgb.DMatrix(agaricus.test$data, label = agaricus.test$label)
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# note: for customized objective function, we leave objective as default
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# note: what we are getting is margin value in prediction
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# you must know what you are doing
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param <- list(max_depth = 2, eta = 1, nthread = 2, verbosity = 0)
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evals <- list(eval = dtest)
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num_round <- 20
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# user define objective function, given prediction, return gradient and second order gradient
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# this is log likelihood loss
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logregobj <- function(preds, dtrain) {
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labels <- getinfo(dtrain, "label")
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preds <- 1 / (1 + exp(-preds))
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grad <- preds - labels
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hess <- preds * (1 - preds)
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return(list(grad = grad, hess = hess))
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}
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# user defined evaluation function, return a pair metric_name, result
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# NOTE: when you do customized loss function, the default prediction value is margin
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# this may make builtin evaluation metric not function properly
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# for example, we are doing logistic loss, the prediction is score before logistic transformation
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# the builtin evaluation error assumes input is after logistic transformation
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# Take this in mind when you use the customization, and maybe you need write customized evaluation function
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evalerror <- function(preds, dtrain) {
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labels <- getinfo(dtrain, "label")
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err <- as.numeric(sum(labels != (preds > 0))) / length(labels)
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return(list(metric = "error", value = err))
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}
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print('start training with early Stopping setting')
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bst <- xgb.train(param, dtrain, num_round, evals,
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objective = logregobj, eval_metric = evalerror, maximize = FALSE,
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early_stopping_round = 3)
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bst <- xgb.cv(param, dtrain, num_round, nfold = 5,
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objective = logregobj, eval_metric = evalerror,
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maximize = FALSE, early_stopping_rounds = 3)
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