ac457c56a2
* Use `UpdateAllowUnknown' for non-model related parameter. Model parameter can not pack an additional boolean value due to binary IO format. This commit deals only with non-model related parameter configuration. * Add tidy command line arg for use-dmlc-gtest.
113 lines
3.9 KiB
C++
113 lines
3.9 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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std::unique_ptr<GradientBooster> p_gbm{
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GradientBooster::Create("gbtree", &generic_param, {}, 0)};
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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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std::string n_feat = std::to_string(kCols);
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Args args {{"tree_method", "approx"}, {"num_feature", n_feat}};
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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"}, {"num_feature", n_feat}});
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ASSERT_EQ(tparam.updater_seq, "grow_histmaker,prune");
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gbtree.Configure({{"tree_method", "exact"}, {"num_feature", n_feat}});
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ASSERT_EQ(tparam.updater_seq, "grow_colmaker,prune");
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gbtree.Configure({{"tree_method", "hist"}, {"num_feature", n_feat}});
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ASSERT_EQ(tparam.updater_seq, "grow_quantile_histmaker");
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ASSERT_EQ(tparam.predictor, "cpu_predictor");
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gbtree.Configure({{"booster", "dart"}, {"tree_method", "hist"},
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{"num_feature", n_feat}});
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ASSERT_EQ(tparam.updater_seq, "grow_quantile_histmaker");
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ASSERT_EQ(tparam.predictor, "cpu_predictor");
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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"}, {"num_feature", n_feat}});
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ASSERT_EQ(tparam.updater_seq, "grow_gpu_hist");
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ASSERT_EQ(tparam.predictor, "gpu_predictor");
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gbtree.Configure({{"booster", "dart"}, {"tree_method", "gpu_hist"},
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{"num_feature", n_feat}});
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ASSERT_EQ(tparam.updater_seq, "grow_gpu_hist");
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ASSERT_EQ(tparam.predictor, "gpu_predictor");
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#endif
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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 kNumRows = 17;
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size_t constexpr kCols = 15;
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auto pp_mat = CreateDMatrix(kNumRows, kCols, 0);
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auto& p_mat = *pp_mat;
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std::vector<bst_float> labels (kNumRows);
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for (size_t i = 0; i < kNumRows; ++i) {
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labels[i] = i % 2;
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}
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p_mat->Info().SetInfo("label", labels.data(), DataType::kFloat32, kNumRows);
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std::vector<std::shared_ptr<xgboost::DMatrix>> mat = {p_mat};
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std::string n_feat = std::to_string(kCols);
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Args args {{"tree_method", "approx"}, {"num_feature", n_feat}};
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GenericParameter generic_param;
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generic_param.UpdateAllowUnknown(Args{{"gpu_id", "0"}});
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auto& data = (*(p_mat->GetBatches<SparsePage>().begin())).data;
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auto learner = std::unique_ptr<Learner>(Learner::Create(mat));
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learner->SetParams(Args{{"tree_method", "gpu_hist"}});
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for (size_t i = 0; i < 4; ++i) {
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learner->UpdateOneIter(i, p_mat.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(mat));
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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_mat.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(mat));
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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_mat.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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