53fc17578f
- Use std::uint64_t instead of size_t to avoid implementation-defined type. - Rename to bst_idx_t, to account for other types of indexing. - Small cleanup to the base header.
60 lines
1.9 KiB
C++
60 lines
1.9 KiB
C++
/**
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* Copyright 2018-2023 by XGBoost Contributors
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*/
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#include <gtest/gtest.h>
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#include <xgboost/host_device_vector.h>
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#include <xgboost/task.h> // for ObjInfo
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#include <xgboost/tree_updater.h>
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#include <memory>
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#include <string>
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#include <vector>
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#include "../../../src/tree/param.h" // for TrainParam
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#include "../helpers.h"
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namespace xgboost::tree {
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TEST(Updater, Refresh) {
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bst_idx_t constexpr kRows = 8;
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bst_feature_t constexpr kCols = 16;
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Context ctx;
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linalg::Matrix<GradientPair> gpair
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{{ {0.23f, 0.24f}, {0.23f, 0.24f}, {0.23f, 0.24f}, {0.23f, 0.24f},
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{0.27f, 0.29f}, {0.27f, 0.29f}, {0.27f, 0.29f}, {0.27f, 0.29f} }, {8, 1}, ctx.Device()};
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std::shared_ptr<DMatrix> p_dmat{
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RandomDataGenerator{kRows, kCols, 0.4f}.Seed(3).GenerateDMatrix()};
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std::vector<std::pair<std::string, std::string>> cfg{
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{"reg_alpha", "0.0"},
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{"num_feature", std::to_string(kCols)},
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{"reg_lambda", "1"}};
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RegTree tree = RegTree{1u, kCols};
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std::vector<RegTree*> trees{&tree};
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ObjInfo task{ObjInfo::kRegression};
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std::unique_ptr<TreeUpdater> refresher(TreeUpdater::Create("refresh", &ctx, &task));
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tree.ExpandNode(0, 2, 0.2f, false, 0.0, 0.2f, 0.8f, 0.0f, 0.0f,
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/*left_sum=*/0.0f, /*right_sum=*/0.0f);
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int cleft = tree[0].LeftChild();
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int cright = tree[0].RightChild();
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tree.Stat(cleft).base_weight = 1.2;
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tree.Stat(cright).base_weight = 1.3;
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std::vector<HostDeviceVector<bst_node_t>> position;
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tree::TrainParam param;
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param.UpdateAllowUnknown(cfg);
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refresher->Update(¶m, &gpair, p_dmat.get(), position, trees);
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bst_float constexpr kEps = 1e-6;
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ASSERT_NEAR(-0.183392, tree[cright].LeafValue(), kEps);
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ASSERT_NEAR(-0.224489, tree.Stat(0).loss_chg, kEps);
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ASSERT_NEAR(0, tree.Stat(cleft).loss_chg, kEps);
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ASSERT_NEAR(0, tree.Stat(1).loss_chg, kEps);
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ASSERT_NEAR(0, tree.Stat(2).loss_chg, kEps);
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}
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} // namespace xgboost::tree
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