001503186c
This PR rewrites the approx tree method to use codebase from hist for better performance and code sharing. The rewrite has many benefits: - Support for both `max_leaves` and `max_depth`. - Support for `grow_policy`. - Support for mono constraint. - Support for feature weights. - Support for easier bin configuration (`max_bin`). - Support for categorical data. - Faster performance for most of the datasets. (many times faster) - Support for prediction cache. - Significantly better performance for external memory. - Unites the code base between approx and hist.
81 lines
2.2 KiB
C++
81 lines
2.2 KiB
C++
/*!
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* Copyright 2021 XGBoost contributors
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*/
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#include <gtest/gtest.h>
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#include <xgboost/base.h>
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#include <xgboost/tree_model.h>
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#include "../helpers.h"
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namespace xgboost {
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class TestGrowPolicy : public ::testing::Test {
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protected:
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std::shared_ptr<DMatrix> Xy_;
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size_t n_samples_ = 4096, n_features_ = 13;
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float sparsity_ = 0.5;
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protected:
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void SetUp() override {
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Xy_ =
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RandomDataGenerator{n_samples_, n_features_, sparsity_}.GenerateDMatrix(
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true);
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}
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void TestTreeGrowPolicy(std::string tree_method, std::string policy) {
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{
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std::unique_ptr<Learner> learner{Learner::Create({this->Xy_})};
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learner->SetParam("tree_method", tree_method);
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learner->SetParam("max_leaves", "16");
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learner->SetParam("grow_policy", policy);
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learner->Configure();
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learner->UpdateOneIter(0, Xy_);
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Json model{Object{}};
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learner->SaveModel(&model);
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auto j_tree = model["learner"]["gradient_booster"]["model"]["trees"][0];
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RegTree tree;
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tree.LoadModel(j_tree);
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ASSERT_EQ(tree.GetNumLeaves(), 16);
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}
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{
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std::unique_ptr<Learner> learner{Learner::Create({this->Xy_})};
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learner->SetParam("tree_method", tree_method);
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learner->SetParam("max_depth", "3");
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learner->SetParam("grow_policy", policy);
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learner->Configure();
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learner->UpdateOneIter(0, Xy_);
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Json model{Object{}};
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learner->SaveModel(&model);
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auto j_tree = model["learner"]["gradient_booster"]["model"]["trees"][0];
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RegTree tree;
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tree.LoadModel(j_tree);
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bst_node_t depth = 0;
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tree.WalkTree([&](bst_node_t nidx) {
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depth = std::max(tree.GetDepth(nidx), depth);
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return true;
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});
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ASSERT_EQ(depth, 3);
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}
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}
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};
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TEST_F(TestGrowPolicy, Approx) {
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this->TestTreeGrowPolicy("approx", "depthwise");
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this->TestTreeGrowPolicy("approx", "lossguide");
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}
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TEST_F(TestGrowPolicy, Hist) {
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this->TestTreeGrowPolicy("hist", "depthwise");
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this->TestTreeGrowPolicy("hist", "lossguide");
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}
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#if defined(XGBOOST_USE_CUDA)
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TEST_F(TestGrowPolicy, GpuHist) {
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this->TestTreeGrowPolicy("gpu_hist", "depthwise");
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this->TestTreeGrowPolicy("gpu_hist", "lossguide");
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}
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#endif // defined(XGBOOST_USE_CUDA)
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} // namespace xgboost
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