e089e16e3d
* Pass pointer to model parameters. This PR de-duplicates most of the model parameters except the one in `tree_model.h`. One difficulty is `base_score` is a model property but can be changed at runtime by objective function. Hence when performing model IO, we need to save the one provided by users, instead of the one transformed by objective. Here we created an immutable version of `LearnerModelParam` that represents the value of model parameter after configuration.
102 lines
3.3 KiB
C++
102 lines
3.3 KiB
C++
#include <gtest/gtest.h>
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#include <dmlc/filesystem.h>
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#include <xgboost/generic_parameters.h>
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#include "xgboost/learner.h"
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#include "../helpers.h"
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#include "../../../src/gbm/gbtree.h"
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namespace xgboost {
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TEST(GBTree, SelectTreeMethod) {
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size_t constexpr kCols = 10;
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GenericParameter generic_param;
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generic_param.UpdateAllowUnknown(Args{});
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LearnerModelParam mparam;
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mparam.base_score = 0.5;
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mparam.num_feature = kCols;
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mparam.num_output_group = 1;
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std::vector<std::shared_ptr<DMatrix> > caches;
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std::unique_ptr<GradientBooster> p_gbm{
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GradientBooster::Create("gbtree", &generic_param, &mparam, caches)};
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auto& gbtree = dynamic_cast<gbm::GBTree&> (*p_gbm);
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// Test if `tree_method` can be set
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Args args {{"tree_method", "approx"}};
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gbtree.Configure({args.cbegin(), args.cend()});
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gbtree.Configure(args);
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auto const& tparam = gbtree.GetTrainParam();
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gbtree.Configure({{"tree_method", "approx"}});
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ASSERT_EQ(tparam.updater_seq, "grow_histmaker,prune");
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gbtree.Configure({{"tree_method", "exact"}});
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ASSERT_EQ(tparam.updater_seq, "grow_colmaker,prune");
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gbtree.Configure({{"tree_method", "hist"}});
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ASSERT_EQ(tparam.updater_seq, "grow_quantile_histmaker");
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gbtree.Configure({{"booster", "dart"}, {"tree_method", "hist"}});
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ASSERT_EQ(tparam.updater_seq, "grow_quantile_histmaker");
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#ifdef XGBOOST_USE_CUDA
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generic_param.UpdateAllowUnknown(Args{{"gpu_id", "0"}});
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gbtree.Configure({{"tree_method", "gpu_hist"}});
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ASSERT_EQ(tparam.updater_seq, "grow_gpu_hist");
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gbtree.Configure({{"booster", "dart"}, {"tree_method", "gpu_hist"}});
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ASSERT_EQ(tparam.updater_seq, "grow_gpu_hist");
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#endif // XGBOOST_USE_CUDA
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}
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#ifdef XGBOOST_USE_CUDA
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TEST(GBTree, ChoosePredictor) {
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size_t constexpr kRows = 17;
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size_t constexpr kCols = 15;
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auto pp_dmat = CreateDMatrix(kRows, kCols, 0);
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std::shared_ptr<DMatrix> p_dmat {*pp_dmat};
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auto& data = (*(p_dmat->GetBatches<SparsePage>().begin())).data;
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p_dmat->Info().labels_.Resize(kRows);
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auto learner = std::unique_ptr<Learner>(Learner::Create({p_dmat}));
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learner->SetParams(Args{{"tree_method", "gpu_hist"}, {"gpu_id", "0"}});
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for (size_t i = 0; i < 4; ++i) {
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learner->UpdateOneIter(i, p_dmat.get());
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}
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ASSERT_TRUE(data.HostCanWrite());
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dmlc::TemporaryDirectory tempdir;
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const std::string fname = tempdir.path + "/model_para.bst";
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{
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std::unique_ptr<dmlc::Stream> fo(dmlc::Stream::Create(fname.c_str(), "w"));
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learner->Save(fo.get());
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}
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// a new learner
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learner = std::unique_ptr<Learner>(Learner::Create({p_dmat}));
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{
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std::unique_ptr<dmlc::Stream> fi(dmlc::Stream::Create(fname.c_str(), "r"));
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learner->Load(fi.get());
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}
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learner->SetParams(Args{{"tree_method", "gpu_hist"}, {"gpu_id", "0"}});
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for (size_t i = 0; i < 4; ++i) {
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learner->UpdateOneIter(i, p_dmat.get());
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}
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ASSERT_TRUE(data.HostCanWrite());
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// pull data into device.
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data = HostDeviceVector<Entry>(data.HostVector(), 0);
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data.DeviceSpan();
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ASSERT_FALSE(data.HostCanWrite());
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// another new learner
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learner = std::unique_ptr<Learner>(Learner::Create({p_dmat}));
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learner->SetParams(Args{{"tree_method", "gpu_hist"}, {"gpu_id", "0"}});
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for (size_t i = 0; i < 4; ++i) {
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learner->UpdateOneIter(i, p_dmat.get());
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}
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// data is not pulled back into host
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ASSERT_FALSE(data.HostCanWrite());
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}
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#endif
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} // namespace xgboost
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