e0a279114e
* Unify logging facilities. * Enhance `ConsoleLogger` to handle different verbosity. * Override macros from `dmlc`. * Don't use specialized gamma when building with GPU. * Remove verbosity cache in monitor. * Test monitor. * Deprecate `silent`. * Fix doc and messages. * Fix python test. * Fix silent tests.
66 lines
2.9 KiB
R
66 lines
2.9 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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watchlist <- list(eval = dtest, train = dtrain)
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num_round <- 2
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# user define objective function, given prediction, return gradient and second order gradient
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# this is loglikelihood 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 buildin evalution 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 buildin 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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param <- list(max_depth=2, eta=1, nthread = 2, verbosity=0,
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objective=logregobj, eval_metric=evalerror)
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print ('start training with user customized objective')
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# training with customized objective, we can also do step by step training
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# simply look at xgboost.py's implementation of train
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bst <- xgb.train(param, dtrain, num_round, watchlist)
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#
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# there can be cases where you want additional information
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# being considered besides the property of DMatrix you can get by getinfo
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# you can set additional information as attributes if DMatrix
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# set label attribute of dtrain to be label, we use label as an example, it can be anything
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attr(dtrain, 'label') <- getinfo(dtrain, 'label')
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# this is new customized objective, where you can access things you set
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# same thing applies to customized evaluation function
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logregobjattr <- function(preds, dtrain) {
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# now you can access the attribute in customized function
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labels <- attr(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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param <- list(max_depth=2, eta=1, nthread = 2, verbosity=0,
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objective=logregobjattr, eval_metric=evalerror)
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print ('start training with user customized objective, with additional attributes in DMatrix')
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# training with customized objective, we can also do step by step training
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# simply look at xgboost.py's implementation of train
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bst <- xgb.train(param, dtrain, num_round, watchlist)
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