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Check number of trees in inplace predict. (#7409) (#7424)

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Jiaming Yuan 2021-11-12 19:31:31 +08:00 committed by GitHub
parent 4d2ea0d4ef
commit a013942649
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1
src/gbm/gbtree.h
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@ -273,6 +273,7 @@ class GBTree : public GradientBooster {
uint32_t tree_begin, tree_end; uint32_t tree_begin, tree_end;
std::tie(tree_begin, tree_end) = std::tie(tree_begin, tree_end) =
detail::LayerToTree(model_, tparam_, layer_begin, layer_end); detail::LayerToTree(model_, tparam_, layer_begin, layer_end);
CHECK_LE(tree_end, model_.trees.size()) << "Invalid number of trees.";
std::vector<Predictor const *> predictors{ std::vector<Predictor const *> predictors{
cpu_predictor_.get(), cpu_predictor_.get(),
#if defined(XGBOOST_USE_CUDA) #if defined(XGBOOST_USE_CUDA)

43
tests/cpp/gbm/test_gbtree.cc
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@ -452,4 +452,47 @@ TEST(GBTree, FeatureScore) {
test_eq("gain"); test_eq("gain");
test_eq("cover"); test_eq("cover");
} }
TEST(GBTree, PredictRange) {
size_t n_samples = 1000, n_features = 10, n_classes = 4;
auto m = RandomDataGenerator{n_samples, n_features, 0.5}.GenerateDMatrix(true, false, n_classes);
std::unique_ptr<Learner> learner{Learner::Create({m})};
learner->SetParam("num_class", std::to_string(n_classes));
learner->Configure();
for (size_t i = 0; i < 2; ++i) {
learner->UpdateOneIter(i, m);
}
HostDeviceVector<float> out_predt;
ASSERT_THROW(learner->Predict(m, false, &out_predt, 0, 3), dmlc::Error);
auto m_1 =
RandomDataGenerator{n_samples, n_features, 0.5}.GenerateDMatrix(true, false, n_classes);
HostDeviceVector<float> out_predt_full;
learner->Predict(m_1, false, &out_predt_full, 0, 0);
ASSERT_TRUE(std::equal(out_predt.HostVector().begin(), out_predt.HostVector().end(),
out_predt_full.HostVector().begin()));
{
// inplace predict
HostDeviceVector<float> raw_storage;
auto raw = RandomDataGenerator{n_samples, n_features, 0.5}.GenerateArrayInterface(&raw_storage);
std::shared_ptr<data::ArrayAdapter> x{new data::ArrayAdapter{StringView{raw}}};
HostDeviceVector<float>* out_predt;
learner->InplacePredict(x, nullptr, PredictionType::kValue,
std::numeric_limits<float>::quiet_NaN(), &out_predt, 0, 2);
auto h_out_predt = out_predt->HostVector();
learner->InplacePredict(x, nullptr, PredictionType::kValue,
std::numeric_limits<float>::quiet_NaN(), &out_predt, 0, 0);
auto h_out_predt_full = out_predt->HostVector();
ASSERT_TRUE(std::equal(h_out_predt.begin(), h_out_predt.end(), h_out_predt_full.begin()));
ASSERT_THROW(learner->InplacePredict(x, nullptr, PredictionType::kValue,
std::numeric_limits<float>::quiet_NaN(), &out_predt, 0, 3),
dmlc::Error);
}
}
} // namespace xgboost } // namespace xgboost