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Unify logging facilities. (#3982)

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* 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.
This commit is contained in:
Jiaming Yuan
2018-12-14 19:29:58 +08:00
committed by GitHub
parent fd722d60cd
commit e0a279114e
28 changed files with 368 additions and 171 deletions
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4
R-package/demo/custom_objective.R
View File

@@ -33,7 +33,7 @@ evalerror <- function(preds, dtrain) {
return(list(metric = "error", value = err))
}
param <- list(max_depth=2, eta=1, nthread = 2, silent=1,
param <- list(max_depth=2, eta=1, nthread = 2, verbosity=0,
objective=logregobj, eval_metric=evalerror)
print ('start training with user customized objective')
# training with customized objective, we can also do step by step training
@@ -57,7 +57,7 @@ logregobjattr <- function(preds, dtrain) {
hess <- preds * (1 - preds)
return(list(grad = grad, hess = hess))
}
param <- list(max_depth=2, eta=1, nthread = 2, silent=1,
param <- list(max_depth=2, eta=1, nthread = 2, verbosity=0,
objective=logregobjattr, eval_metric=evalerror)
print ('start training with user customized objective, with additional attributes in DMatrix')
# training with customized objective, we can also do step by step training

6
R-package/demo/early_stopping.R
View File

@@ -7,7 +7,7 @@ dtest <- xgb.DMatrix(agaricus.test$data, label = agaricus.test$label)
# note: for customized objective function, we leave objective as default
# note: what we are getting is margin value in prediction
# you must know what you are doing
param <- list(max_depth=2, eta=1, nthread = 2, silent=1)
param <- list(max_depth=2, eta=1, nthread=2, verbosity=0)
watchlist <- list(eval = dtest)
num_round <- 20
# user define objective function, given prediction, return gradient and second order gradient
@@ -32,9 +32,9 @@ evalerror <- function(preds, dtrain) {
}
print ('start training with early Stopping setting')
bst <- xgb.train(param, dtrain, num_round, watchlist,
bst <- xgb.train(param, dtrain, num_round, watchlist,
objective = logregobj, eval_metric = evalerror, maximize = FALSE,
early_stopping_round = 3)
bst <- xgb.cv(param, dtrain, num_round, nfold = 5,
bst <- xgb.cv(param, dtrain, num_round, nfold = 5,
objective = logregobj, eval_metric = evalerror,
maximize = FALSE, early_stopping_rounds = 3)