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Add option to disable default metric (#3606)

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Philip Hyunsu Cho 2018-08-18 11:39:20 -07:00 committed by GitHub
parent 993e62b9e7
commit 983cb0b374
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3 changed files with 12 additions and 3 deletions
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4
demo/guide-python/custom_objective.py
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@ -33,9 +33,9 @@ def logregobj(preds, dtrain):
# Take this in mind when you use the customization, and maybe you need write customized evaluation function # Take this in mind when you use the customization, and maybe you need write customized evaluation function
def evalerror(preds, dtrain): def evalerror(preds, dtrain):
labels = dtrain.get_label() labels = dtrain.get_label()
# return a pair metric_name, result. The metric name must not contain a colon (:) # return a pair metric_name, result. The metric name must not contain a colon (:) or a space
# since preds are margin(before logistic transformation, cutoff at 0) # since preds are margin(before logistic transformation, cutoff at 0)
return 'error', float(sum(labels != (preds > 0.0))) / len(labels) return 'my-error', float(sum(labels != (preds > 0.0))) / len(labels)
# training with customized objective, we can also do step by step training # training with customized objective, we can also do step by step training
# simply look at xgboost.py's implementation of train # simply look at xgboost.py's implementation of train

4
doc/parameter.rst
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@ -31,6 +31,10 @@ General Parameters
- Number of parallel threads used to run XGBoost - Number of parallel threads used to run XGBoost
* ``disable_default_eval_metric`` [default=0]
- Flag to disable default metric. Set to >0 to disable.
* ``num_pbuffer`` [set automatically by XGBoost, no need to be set by user] * ``num_pbuffer`` [set automatically by XGBoost, no need to be set by user]
- Size of prediction buffer, normally set to number of training instances. The buffers are used to save the prediction results of last boosting step. - Size of prediction buffer, normally set to number of training instances. The buffers are used to save the prediction results of last boosting step.

7
src/learner.cc
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@ -92,6 +92,8 @@ struct LearnerTrainParam : public dmlc::Parameter<LearnerTrainParam> {
int nthread; int nthread;
// flag to print out detailed breakdown of runtime // flag to print out detailed breakdown of runtime
int debug_verbose; int debug_verbose;
// flag to disable default metric
int disable_default_eval_metric;
// declare parameters // declare parameters
DMLC_DECLARE_PARAMETER(LearnerTrainParam) { DMLC_DECLARE_PARAMETER(LearnerTrainParam) {
DMLC_DECLARE_FIELD(seed).set_default(0).describe( DMLC_DECLARE_FIELD(seed).set_default(0).describe(
@ -128,6 +130,9 @@ struct LearnerTrainParam : public dmlc::Parameter<LearnerTrainParam> {
.set_lower_bound(0) .set_lower_bound(0)
.set_default(0) .set_default(0)
.describe("flag to print out detailed breakdown of runtime"); .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");
} }
}; };
@ -403,7 +408,7 @@ class LearnerImpl : public Learner {
monitor_.Start("EvalOneIter"); monitor_.Start("EvalOneIter");
std::ostringstream os; std::ostringstream os;
os << '[' << iter << ']' << std::setiosflags(std::ios::fixed); os << '[' << iter << ']' << std::setiosflags(std::ios::fixed);
if (metrics_.size() == 0) { if (metrics_.size() == 0 && tparam_.disable_default_eval_metric <= 0) {
metrics_.emplace_back(Metric::Create(obj_->DefaultEvalMetric())); metrics_.emplace_back(Metric::Create(obj_->DefaultEvalMetric()));
} }
for (size_t i = 0; i < data_sets.size(); ++i) { for (size_t i = 0; i < data_sets.size(); ++i) {