972730cde0
- Use the `linalg::Matrix` for storing gradients. - New API for the custom objective. - Custom objective for multi-class/multi-target is now required to return the correct shape. - Custom objective for Python can accept arrays with any strides. (row-major, column-major)
90 lines
2.9 KiB
C++
90 lines
2.9 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/data.h>
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#include <xgboost/host_device_vector.h>
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#include <xgboost/learner.h>
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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, Prune) {
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int constexpr kCols = 16;
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std::vector<std::pair<std::string, std::string>> cfg;
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cfg.emplace_back("num_feature", std::to_string(kCols));
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cfg.emplace_back("min_split_loss", "10");
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Context ctx;
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// These data are just place holders.
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linalg::Matrix<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} }, {8, 1}, ctx.Device()};
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std::shared_ptr<DMatrix> p_dmat{RandomDataGenerator{32, 10, 0}.GenerateDMatrix()};
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// prepare tree
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RegTree tree = RegTree{1u, kCols};
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std::vector<RegTree*> trees {&tree};
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// prepare pruner
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TrainParam param;
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param.UpdateAllowUnknown(cfg);
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ObjInfo task{ObjInfo::kRegression};
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std::unique_ptr<TreeUpdater> pruner(TreeUpdater::Create("prune", &ctx, &task));
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// loss_chg < min_split_loss;
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std::vector<HostDeviceVector<bst_node_t>> position(trees.size());
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tree.ExpandNode(0, 0, 0, true, 0.0f, 0.3f, 0.4f, 0.0f, 0.0f,
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/*left_sum=*/0.0f, /*right_sum=*/0.0f);
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pruner->Update(¶m, &gpair, p_dmat.get(), position, 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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/*left_sum=*/0.0f, /*right_sum=*/0.0f);
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pruner->Update(¶m, &gpair, p_dmat.get(), position, 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(¶m, &gpair, p_dmat.get(), position, trees);
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ASSERT_EQ(tree.NumExtraNodes(), 2);
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// Test depth
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// loss_chg > min_split_loss
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tree.ExpandNode(tree[0].LeftChild(),
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0, 0.5f, true, 0.3, 0.4, 0.5,
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/*loss_chg=*/18.0f, 0.0f,
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/*left_sum=*/0.0f, /*right_sum=*/0.0f);
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tree.ExpandNode(tree[0].RightChild(),
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0, 0.5f, true, 0.3, 0.4, 0.5,
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/*loss_chg=*/19.0f, 0.0f,
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/*left_sum=*/0.0f, /*right_sum=*/0.0f);
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cfg.emplace_back("max_depth", "1");
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param.UpdateAllowUnknown(cfg);
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pruner->Update(¶m, &gpair, p_dmat.get(), position, trees);
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ASSERT_EQ(tree.NumExtraNodes(), 2);
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tree.ExpandNode(tree[0].LeftChild(),
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0, 0.5f, true, 0.3, 0.4, 0.5,
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/*loss_chg=*/18.0f, 0.0f,
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/*left_sum=*/0.0f, /*right_sum=*/0.0f);
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cfg.emplace_back("min_split_loss", "0");
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param.UpdateAllowUnknown(cfg);
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pruner->Update(¶m, &gpair, p_dmat.get(), position, trees);
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ASSERT_EQ(tree.NumExtraNodes(), 2);
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}
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} // namespace xgboost::tree
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