83a66b4994
* Extract partitioner from hist. * Implement categorical data support by passing the gradient index directly into the partitioner. * Organize/update document. * Remove code for negative hessian.
68 lines
2.4 KiB
C++
68 lines
2.4 KiB
C++
/*!
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* Copyright 2021-2022, XGBoost contributors.
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*/
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#include <gtest/gtest.h>
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#include "../../../src/tree/updater_approx.h"
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#include "../helpers.h"
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#include "test_partitioner.h"
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namespace xgboost {
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namespace tree {
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TEST(Approx, Partitioner) {
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size_t n_samples = 1024, n_features = 1, base_rowid = 0;
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ApproxRowPartitioner partitioner{n_samples, base_rowid};
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ASSERT_EQ(partitioner.base_rowid, base_rowid);
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ASSERT_EQ(partitioner.Size(), 1);
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ASSERT_EQ(partitioner.Partitions()[0].Size(), n_samples);
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auto Xy = RandomDataGenerator{n_samples, n_features, 0}.GenerateDMatrix(true);
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GenericParameter ctx;
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ctx.InitAllowUnknown(Args{});
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std::vector<CPUExpandEntry> candidates{{0, 0, 0.4}};
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auto grad = GenerateRandomGradients(n_samples);
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std::vector<float> hess(grad.Size());
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std::transform(grad.HostVector().cbegin(), grad.HostVector().cend(), hess.begin(),
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[](auto gpair) { return gpair.GetHess(); });
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for (auto const &page : Xy->GetBatches<GHistIndexMatrix>({64, hess, true})) {
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bst_feature_t const split_ind = 0;
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{
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auto min_value = page.cut.MinValues()[split_ind];
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RegTree tree;
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ApproxRowPartitioner partitioner{n_samples, base_rowid};
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GetSplit(&tree, min_value, &candidates);
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partitioner.UpdatePosition(&ctx, page, candidates, &tree);
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ASSERT_EQ(partitioner.Size(), 3);
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ASSERT_EQ(partitioner[1].Size(), 0);
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ASSERT_EQ(partitioner[2].Size(), n_samples);
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}
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{
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ApproxRowPartitioner partitioner{n_samples, base_rowid};
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auto ptr = page.cut.Ptrs()[split_ind + 1];
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float split_value = page.cut.Values().at(ptr / 2);
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RegTree tree;
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GetSplit(&tree, split_value, &candidates);
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auto left_nidx = tree[RegTree::kRoot].LeftChild();
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partitioner.UpdatePosition(&ctx, page, candidates, &tree);
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auto elem = partitioner[left_nidx];
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ASSERT_LT(elem.Size(), n_samples);
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ASSERT_GT(elem.Size(), 1);
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for (auto it = elem.begin; it != elem.end; ++it) {
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auto value = page.cut.Values().at(page.index[*it]);
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ASSERT_LE(value, split_value);
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}
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auto right_nidx = tree[RegTree::kRoot].RightChild();
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elem = partitioner[right_nidx];
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for (auto it = elem.begin; it != elem.end; ++it) {
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auto value = page.cut.Values().at(page.index[*it]);
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ASSERT_GT(value, split_value) << *it;
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}
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}
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}
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}
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} // namespace tree
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} // namespace xgboost
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