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
65 lines
1.8 KiB
C++
65 lines
1.8 KiB
C++
/*!
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* Copyright 2018-2019 by Contributors
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*/
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#include "../helpers.h"
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#include "../../../src/common/host_device_vector.h"
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#include <xgboost/tree_updater.h>
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#include <gtest/gtest.h>
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#include <vector>
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#include <string>
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#include <memory>
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namespace xgboost {
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namespace tree {
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TEST(Updater, Prune) {
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int constexpr kNCols = 16;
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std::vector<std::pair<std::string, std::string>> cfg;
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cfg.emplace_back(std::pair<std::string, std::string>(
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"num_feature", std::to_string(kNCols)));
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cfg.emplace_back(std::pair<std::string, std::string>(
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"min_split_loss", "10"));
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cfg.emplace_back(std::pair<std::string, std::string>(
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"silent", "1"));
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// These data are just place holders.
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HostDeviceVector<GradientPair> gpair =
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{ {0.50f, 0.25f}, {0.50f, 0.25f}, {0.50f, 0.25f}, {0.50f, 0.25f},
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{0.25f, 0.24f}, {0.25f, 0.24f}, {0.25f, 0.24f}, {0.25f, 0.24f} };
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auto dmat = CreateDMatrix(32, 16, 0.4, 3);
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auto lparam = CreateEmptyGenericParam(0, 0);
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// prepare tree
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RegTree tree = RegTree();
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tree.param.InitAllowUnknown(cfg);
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std::vector<RegTree*> trees {&tree};
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// prepare pruner
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std::unique_ptr<TreeUpdater> pruner(TreeUpdater::Create("prune", &lparam));
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pruner->Configure(cfg);
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// loss_chg < min_split_loss;
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tree.ExpandNode(0, 0, 0, true, 0.0f, 0.3f, 0.4f, 0.0f, 0.0f);
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pruner->Update(&gpair, dmat->get(), trees);
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ASSERT_EQ(tree.NumExtraNodes(), 0);
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// loss_chg > min_split_loss;
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tree.ExpandNode(0, 0, 0, true, 0.0f, 0.3f, 0.4f, 11.0f, 0.0f);
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pruner->Update(&gpair, dmat->get(), trees);
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ASSERT_EQ(tree.NumExtraNodes(), 2);
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// loss_chg == min_split_loss;
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tree.Stat(0).loss_chg = 10;
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pruner->Update(&gpair, dmat->get(), trees);
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ASSERT_EQ(tree.NumExtraNodes(), 2);
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delete dmat;
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}
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} // namespace tree
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} // namespace xgboost
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