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