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

[Breaking] Don't drop trees during DART prediction by default (#5115)

Browse Source 
* Simplify DropTrees calling logic

* Add `training` parameter for prediction method.

* [Breaking]: Add `training` to C API.

* Change for R and Python custom objective.

* Correct comment.

Co-authored-by: Philip Hyunsu Cho <chohyu01@cs.washington.edu>
Co-authored-by: Jiaming Yuan <jm.yuan@outlook.com>
This commit is contained in:
Kodi Arfer
2020-01-13 08:48:30 -05:00
committed by Jiaming Yuan
parent 7b65698187
commit f100b8d878
23 changed files with 214 additions and 140 deletions
Download Patch File Download Diff File Expand all files Collapse all files

11
src/learner.cc
View File

@@ -694,7 +694,7 @@ class LearnerImpl : public Learner {
this->ValidateDMatrix(train);
monitor_.Start("PredictRaw");
this->PredictRaw(train, &preds_[train]);
this->PredictRaw(train, &preds_[train], true);
monitor_.Stop("PredictRaw");
TrainingObserver::Instance().Observe(preds_[train], "Predictions");
@@ -735,7 +735,7 @@ class LearnerImpl : public Learner {
for (size_t i = 0; i < data_sets.size(); ++i) {
DMatrix * dmat = data_sets[i];
this->ValidateDMatrix(dmat);
this->PredictRaw(data_sets[i], &preds_[dmat]);
this->PredictRaw(data_sets[i], &preds_[dmat], false);
obj_->EvalTransform(&preds_[dmat]);
for (auto& ev : metrics_) {
os << '\t' << data_names[i] << '-' << ev->Name() << ':'
@@ -799,6 +799,7 @@ class LearnerImpl : public Learner {
void Predict(DMatrix* data, bool output_margin,
HostDeviceVector<bst_float>* out_preds, unsigned ntree_limit,
bool training,
bool pred_leaf, bool pred_contribs, bool approx_contribs,
bool pred_interactions) override {
int multiple_predictions = static_cast<int>(pred_leaf) +
@@ -814,7 +815,7 @@ class LearnerImpl : public Learner {
} else if (pred_leaf) {
gbm_->PredictLeaf(data, &out_preds->HostVector(), ntree_limit);
} else {
this->PredictRaw(data, out_preds, ntree_limit);
this->PredictRaw(data, out_preds, training, ntree_limit);
if (!output_margin) {
obj_->PredTransform(out_preds);
}
@@ -832,13 +833,15 @@ class LearnerImpl : public Learner {
* \param out_preds output vector that stores the prediction
* \param ntree_limit limit number of trees used for boosted tree
* predictor, when it equals 0, this means we are using all the trees
* \param training allow dropout when the DART booster is being used
*/
void PredictRaw(DMatrix* data, HostDeviceVector<bst_float>* out_preds,
bool training,
unsigned ntree_limit = 0) const {
CHECK(gbm_ != nullptr)
<< "Predict must happen after Load or configuration";
this->ValidateDMatrix(data);
gbm_->PredictBatch(data, out_preds, ntree_limit);
gbm_->PredictBatch(data, out_preds, training, ntree_limit);
}
void ConfigureObjective(LearnerTrainParam const& old, Args* p_args) {