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Fix #3485, #3540: Don't use dropout for predicting test sets (#3556)

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* Fix #3485, #3540: Don't use dropout for predicting test sets

Dropout (for DART) should only be used at training time.

* Add regression test
This commit is contained in:
Philip Hyunsu Cho
2018-08-05 10:17:21 -07:00
committed by GitHub
parent 109473dae2
commit 44811f2330
5 changed files with 21 additions and 4 deletions
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1
src/gbm/gblinear.cc
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@@ -103,6 +103,7 @@ class GBLinear : public GradientBooster {
void PredictBatch(DMatrix *p_fmat,
HostDeviceVector<bst_float> *out_preds,
bool dropout,
unsigned ntree_limit) override {
monitor_.Start("PredictBatch");
CHECK_EQ(ntree_limit, 0U)

6
src/gbm/gbtree.cc
View File

@@ -217,6 +217,7 @@ class GBTree : public GradientBooster {
void PredictBatch(DMatrix* p_fmat,
HostDeviceVector<bst_float>* out_preds,
bool dropout,
unsigned ntree_limit) override {
predictor_->PredictBatch(p_fmat, out_preds, model_, 0, ntree_limit);
}
@@ -356,8 +357,11 @@ class Dart : public GBTree {
// predict the leaf scores with dropout if ntree_limit = 0
void PredictBatch(DMatrix* p_fmat,
HostDeviceVector<bst_float>* out_preds,
bool dropout,
unsigned ntree_limit) override {
DropTrees(ntree_limit);
if (dropout) {
DropTrees(ntree_limit);
}
PredLoopInternal<Dart>(p_fmat, &out_preds->HostVector(), 0, ntree_limit, true);
}