f0064c07ab
* Refactor configuration [Part II].
* General changes:
** Remove `Init` methods to avoid ambiguity.
** Remove `Configure(std::map<>)` to avoid redundant copying and prepare for
parameter validation. (`std::vector` is returned from `InitAllowUnknown`).
** Add name to tree updaters for easier debugging.
* Learner changes:
** Make `LearnerImpl` the only source of configuration.
All configurations are stored and carried out by `LearnerImpl::Configure()`.
** Remove booster in C API.
Originally kept for "compatibility reason", but did not state why. So here
we just remove it.
** Add a `metric_names_` field in `LearnerImpl`.
** Remove `LazyInit`. Configuration will always be lazy.
** Run `Configure` before every iteration.
* Predictor changes:
** Allocate both cpu and gpu predictor.
** Remove cpu_predictor from gpu_predictor.
`GBTree` is now used to dispatch the predictor.
** Remove some GPU Predictor tests.
* IO
No IO changes. The binary model format stability is tested by comparing
hashing value of save models between two commits
55 lines
2.5 KiB
C++
55 lines
2.5 KiB
C++
#include <gtest/gtest.h>
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#include <xgboost/generic_parameters.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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using Arg = std::pair<std::string, std::string>;
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size_t constexpr kRows = 10;
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size_t constexpr kCols = 10;
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auto p_shared_ptr_dmat = CreateDMatrix(kRows, kCols, 0);
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auto p_dmat {(*p_shared_ptr_dmat).get()};
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GenericParameter generic_param;
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generic_param.InitAllowUnknown(std::vector<Arg>{Arg("n_gpus", "0")});
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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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std::map<std::string, std::string> args {Arg{"tree_method", "approx"}, Arg{"num_feature", n_feat}};
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gbtree.Configure({args.cbegin(), args.cend()});
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gbtree.ConfigureWithKnownData(args, p_dmat);
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auto const& tparam = gbtree.GetTrainParam();
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gbtree.ConfigureWithKnownData({Arg{"tree_method", "approx"}, Arg{"num_feature", n_feat}}, p_dmat);
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ASSERT_EQ(tparam.updater_seq, "grow_histmaker,prune");
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gbtree.ConfigureWithKnownData({Arg("tree_method", "exact"), Arg("num_feature", n_feat)}, p_dmat);
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ASSERT_EQ(tparam.updater_seq, "grow_colmaker,prune");
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gbtree.ConfigureWithKnownData({Arg("tree_method", "hist"), Arg("num_feature", n_feat)}, p_dmat);
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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.ConfigureWithKnownData({Arg{"booster", "dart"}, Arg{"tree_method", "hist"},
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Arg{"num_feature", n_feat}}, p_dmat);
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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.InitAllowUnknown(std::vector<Arg>{Arg{"n_gpus", "1"}});
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gbtree.ConfigureWithKnownData({Arg("tree_method", "gpu_exact"),
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Arg("num_feature", n_feat)}, p_dmat);
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ASSERT_EQ(tparam.updater_seq, "grow_gpu,prune");
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ASSERT_EQ(tparam.predictor, "gpu_predictor");
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gbtree.ConfigureWithKnownData({Arg("tree_method", "gpu_hist"), Arg("num_feature", n_feat)},
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p_dmat);
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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.ConfigureWithKnownData({Arg{"booster", "dart"}, Arg{"tree_method", "gpu_hist"},
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Arg{"num_feature", n_feat}}, p_dmat);
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ASSERT_EQ(tparam.updater_seq, "grow_gpu_hist");
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#endif
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delete p_shared_ptr_dmat;
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}
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} // namespace xgboost
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