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Fix early stopping with linear model. (#7554)

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This commit is contained in:
Jiaming Yuan
2022-01-13 21:53:06 +08:00
committed by GitHub
parent e5e47c3c99
commit e94b766310
2 changed files with 33 additions and 6 deletions
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11
src/gbm/gblinear.cc
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@@ -62,9 +62,8 @@ struct GBLinearTrainParam : public XGBoostParameter<GBLinearTrainParam> {
}
};
void LinearCheckLayer(unsigned layer_begin, unsigned layer_end) {
void LinearCheckLayer(unsigned layer_begin) {
CHECK_EQ(layer_begin, 0) << "Linear booster does not support prediction range.";
CHECK_EQ(layer_end, 0) << "Linear booster does not support prediction range.";
}
/*!
@@ -152,7 +151,7 @@ class GBLinear : public GradientBooster {
void PredictBatch(DMatrix *p_fmat, PredictionCacheEntry *predts,
bool training, unsigned layer_begin, unsigned layer_end) override {
monitor_.Start("PredictBatch");
LinearCheckLayer(layer_begin, layer_end);
LinearCheckLayer(layer_begin);
auto* out_preds = &predts->predictions;
this->PredictBatchInternal(p_fmat, &out_preds->HostVector());
monitor_.Stop("PredictBatch");
@@ -161,7 +160,7 @@ class GBLinear : public GradientBooster {
void PredictInstance(const SparsePage::Inst &inst,
std::vector<bst_float> *out_preds,
unsigned layer_begin, unsigned layer_end) override {
LinearCheckLayer(layer_begin, layer_end);
LinearCheckLayer(layer_begin);
const int ngroup = model_.learner_model_param->num_output_group;
for (int gid = 0; gid < ngroup; ++gid) {
this->Pred(inst, dmlc::BeginPtr(*out_preds), gid,
@@ -177,7 +176,7 @@ class GBLinear : public GradientBooster {
HostDeviceVector<bst_float>* out_contribs,
unsigned layer_begin, unsigned layer_end, bool, int, unsigned) override {
model_.LazyInitModel();
LinearCheckLayer(layer_begin, layer_end);
LinearCheckLayer(layer_begin);
auto base_margin = p_fmat->Info().base_margin_.View(GenericParameter::kCpuId);
const int ngroup = model_.learner_model_param->num_output_group;
const size_t ncolumns = model_.learner_model_param->num_feature + 1;
@@ -214,7 +213,7 @@ class GBLinear : public GradientBooster {
void PredictInteractionContributions(DMatrix* p_fmat,
HostDeviceVector<bst_float>* out_contribs,
unsigned layer_begin, unsigned layer_end, bool) override {
LinearCheckLayer(layer_begin, layer_end);
LinearCheckLayer(layer_begin);
std::vector<bst_float>& contribs = out_contribs->HostVector();
// linear models have no interaction effects