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Remove silent from R demos. (#5675)

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* Remove silent from R demos.

* Vignettes.
This commit is contained in:
Jiaming Yuan 2020-05-19 18:20:46 +08:00 committed by GitHub
parent dd9aeb60ae
commit 7903286961
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GPG Key ID: 4AEE18F83AFDEB23
11 changed files with 45 additions and 56 deletions
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2
R-package/demo/boost_from_prediction.R
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@ -11,7 +11,7 @@ watchlist <- list(eval = dtest, train = dtrain)
#
print('start running example to start from a initial prediction')
# train xgboost for 1 round
param <- list(max_depth=2, eta=1, nthread = 2, silent=1, objective='binary:logistic')
param <- list(max_depth=2, eta=1, nthread = 2, objective='binary:logistic')
bst <- xgb.train(param, dtrain, 1, watchlist)
# Note: we need the margin value instead of transformed prediction in set_base_margin
# do predict with output_margin=TRUE, will always give you margin values before logistic transformation

4
R-package/demo/cross_validation.R
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@ -6,7 +6,7 @@ dtrain <- xgb.DMatrix(agaricus.train$data, label = agaricus.train$label)
dtest <- xgb.DMatrix(agaricus.test$data, label = agaricus.test$label)
nrounds <- 2
param <- list(max_depth=2, eta=1, silent=1, nthread=2, objective='binary:logistic')
param <- list(max_depth=2, eta=1, nthread=2, objective='binary:logistic')
cat('running cross validation\n')
# do cross validation, this will print result out as
@ -40,7 +40,7 @@ evalerror <- function(preds, dtrain) {
return(list(metric = "error", value = err))
}
param <- list(max_depth=2, eta=1, silent=1,
param <- list(max_depth=2, eta=1,
objective = logregobj, eval_metric = evalerror)
# train with customized objective
xgb.cv(params = param, data = dtrain, nrounds = nrounds, nfold = 5)

2
R-package/demo/predict_first_ntree.R
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@ -5,7 +5,7 @@ data(agaricus.test, package='xgboost')
dtrain <- xgb.DMatrix(agaricus.train$data, label = agaricus.train$label)
dtest <- xgb.DMatrix(agaricus.test$data, label = agaricus.test$label)
param <- list(max_depth=2, eta=1, silent=1, objective='binary:logistic')
param <- list(max_depth=2, eta=1, objective='binary:logistic')
watchlist <- list(eval = dtest, train = dtrain)
nrounds = 2

2
R-package/demo/predict_leaf_indices.R
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@ -10,7 +10,7 @@ data(agaricus.test, package='xgboost')
dtrain <- xgb.DMatrix(data = agaricus.train$data, label = agaricus.train$label)
dtest <- xgb.DMatrix(data = agaricus.test$data, label = agaricus.test$label)
param <- list(max_depth=2, eta=1, silent=1, objective='binary:logistic')
param <- list(max_depth=2, eta=1, objective='binary:logistic')
nrounds = 4
# training the model for two rounds

2
R-package/vignettes/xgboost.Rnw
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@ -163,7 +163,7 @@ evalerror <- function(preds, dtrain) {
dtest <- xgb.DMatrix(test$data, label = test$label)
watchlist <- list(eval = dtest, train = dtrain)
param <- list(max_depth = 2, eta = 1, silent = 1)
param <- list(max_depth = 2, eta = 1)
bst <- xgb.train(param, dtrain, nrounds = 2, watchlist, logregobj, evalerror, maximize = FALSE)
@

2
demo/kaggle-higgs/higgs-train.R
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@ -21,7 +21,6 @@ param <- list("objective" = "binary:logitraw",
"bst:max_depth" = 6,
"eval_metric" = "auc",
"eval_metric" = "ams@0.15",
"silent" = 1,
"nthread" = 16)
watchlist <- list("train" = xgmat)
nrounds = 120
@ -30,4 +29,3 @@ bst = xgb.train(param, xgmat, nrounds, watchlist );
# save out model
xgb.save(bst, "higgs.model")
print ('finish training')

1
demo/kaggle-higgs/speedtest.R
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@ -36,7 +36,6 @@ for (i in 1:length(threads)){
"bst:max_depth" = 6,
"eval_metric" = "auc",
"eval_metric" = "ams@0.15",
"silent" = 1,
"nthread" = thread)
watchlist <- list("train" = xgmat)
nrounds = 120

8
tests/cpp/test_logging.cc
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@ -41,19 +41,11 @@ TEST(Logging, Basic) {
output = testing::internal::GetCapturedStderr();
ASSERT_EQ(output.size(), 0);
args["silent"] = "True";
ConsoleLogger::Configure({args.cbegin(), args.cend()});
testing::internal::CaptureStderr();
LOG(INFO) << "Test silent parameter.";
output = testing::internal::GetCapturedStderr();
ASSERT_EQ(output.length(), 0);
testing::internal::CaptureStderr();
LOG(CONSOLE) << "Test Log Console"; // ignore global setting.
output = testing::internal::GetCapturedStderr();
ASSERT_NE(output.find("Test Log Console"), std::string::npos);
args["silent"] = "False";
args["verbosity"] = "2"; // restore
ConsoleLogger::Configure({args.cbegin(), args.cend()});
}

4
tests/distributed/test_basic.py
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@ -9,10 +9,10 @@ dtrain = xgb.DMatrix('../../demo/data/agaricus.txt.train')
dtest = xgb.DMatrix('../../demo/data/agaricus.txt.test')
# Specify parameters via map, definition are same as c++ version
param = {'max_depth': 2, 'eta': 1, 'silent': 1, 'objective': 'binary:logistic' }
param = {'max_depth': 2, 'eta': 1, 'objective': 'binary:logistic'}
# Specify validations set to watch performance
watchlist = [(dtest,'eval'), (dtrain,'train')]
watchlist = [(dtest, 'eval'), (dtrain, 'train')]
num_round = 20
# Run training, all the features in training API is available.