Hendrik/xgboost
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

Unify logging facilities. (#3982)

Browse Source 
* 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
Download Patch File Download Diff File Expand all files Collapse all files

31
src/learner.cc
View File

@@ -122,8 +122,6 @@ struct LearnerTrainParam : public dmlc::Parameter<LearnerTrainParam> {
// number of threads to use if OpenMP is enabled
// if equals 0, use system default
int nthread;
// flag to print out detailed breakdown of runtime
int debug_verbose;
// flag to disable default metric
int disable_default_eval_metric;
// declare parameters
@@ -155,10 +153,6 @@ struct LearnerTrainParam : public dmlc::Parameter<LearnerTrainParam> {
"Internal test flag");
DMLC_DECLARE_FIELD(nthread).set_default(0).describe(
"Number of threads to use.");
DMLC_DECLARE_FIELD(debug_verbose)
.set_lower_bound(0)
.set_default(0)
.describe("flag to print out detailed breakdown of runtime");
DMLC_DECLARE_FIELD(disable_default_eval_metric)
.set_default(0)
.describe("flag to disable default metric. Set to >0 to disable");
@@ -196,7 +190,7 @@ class LearnerImpl : public Learner {
}
// `updater` parameter was manually specified
if (cfg_.count("updater") > 0) {
LOG(CONSOLE) << "DANGER AHEAD: You have manually specified `updater` "
LOG(WARNING) << "DANGER AHEAD: You have manually specified `updater` "
"parameter. The `tree_method` parameter will be ignored. "
"Incorrect sequence of updaters will produce undefined "
"behavior. For common uses, we recommend using "
@@ -217,8 +211,9 @@ class LearnerImpl : public Learner {
cfg_["updater"] = "grow_colmaker,prune";
break;
case TreeMethod::kHist:
LOG(CONSOLE) << "Tree method is selected to be 'hist', which uses a "
"single updater grow_quantile_histmaker.";
LOG(INFO) <<
"Tree method is selected to be 'hist', which uses a "
"single updater grow_quantile_histmaker.";
cfg_["updater"] = "grow_quantile_histmaker";
break;
case TreeMethod::kGPUExact:
@@ -245,8 +240,10 @@ class LearnerImpl : public Learner {
const std::vector<std::pair<std::string, std::string> >& args) override {
// add to configurations
tparam_.InitAllowUnknown(args);
monitor_.Init("Learner", tparam_.debug_verbose);
ConsoleLogger::Configure(args.cbegin(), args.cend());
monitor_.Init("Learner");
cfg_.clear();
for (const auto& kv : args) {
if (kv.first == "eval_metric") {
// check duplication
@@ -270,7 +267,6 @@ class LearnerImpl : public Learner {
if (tparam_.dsplit == DataSplitMode::kAuto && rabit::IsDistributed()) {
tparam_.dsplit = DataSplitMode::kRow;
}
if (cfg_.count("num_class") != 0) {
cfg_["num_output_group"] = cfg_["num_class"];
if (atoi(cfg_["num_class"].c_str()) > 1 && cfg_.count("objective") == 0) {
@@ -612,15 +608,16 @@ class LearnerImpl : public Learner {
}
switch (current_tree_method) {
case TreeMethod::kAuto:
LOG(CONSOLE) << "Tree method is automatically selected to be 'approx' "
"for distributed training.";
LOG(WARNING) <<
"Tree method is automatically selected to be 'approx' "
"for distributed training.";
break;
case TreeMethod::kApprox:
// things are okay, do nothing
break;
case TreeMethod::kExact:
case TreeMethod::kHist:
LOG(CONSOLE) << "Tree method was set to be '"
LOG(WARNING) << "Tree method was set to be '"
<< (current_tree_method == TreeMethod::kExact ?
"exact" : "hist")
<< "', but only 'approx' is available for distributed "
@@ -640,14 +637,14 @@ class LearnerImpl : public Learner {
/* Some tree methods are not available for external-memory DMatrix */
switch (current_tree_method) {
case TreeMethod::kAuto:
LOG(CONSOLE) << "Tree method is automatically set to 'approx' "
LOG(WARNING) << "Tree method is automatically set to 'approx' "
"since external-memory data matrix is used.";
break;
case TreeMethod::kApprox:
// things are okay, do nothing
break;
case TreeMethod::kExact:
LOG(CONSOLE) << "Tree method was set to be 'exact', "
LOG(WARNING) << "Tree method was set to be 'exact', "
"but currently we are only able to proceed with "
"approximate algorithm ('approx') because external-"
"memory data matrix is used.";
@@ -668,7 +665,7 @@ class LearnerImpl : public Learner {
} else if (p_train->Info().num_row_ >= (4UL << 20UL)
&& current_tree_method == TreeMethod::kAuto) {
/* Choose tree_method='approx' automatically for large data matrix */
LOG(CONSOLE) << "Tree method is automatically selected to be "
LOG(WARNING) << "Tree method is automatically selected to be "
"'approx' for faster speed. To use old behavior "
"(exact greedy algorithm on single machine), "
"set tree_method to 'exact'.";