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.
213 lines
8.2 KiB
C++
213 lines
8.2 KiB
C++
/*!
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* Copyright 2018-2022 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/tree_updater.h>
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#include <algorithm>
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#include <string>
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#include <vector>
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#include "../../../src/tree/param.h"
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#include "../../../src/tree/split_evaluator.h"
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#include "../../../src/tree/updater_quantile_hist.h"
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#include "../helpers.h"
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#include "test_partitioner.h"
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#include "xgboost/data.h"
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namespace xgboost {
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namespace tree {
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class QuantileHistMock : public QuantileHistMaker {
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static double constexpr kEps = 1e-6;
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template <typename GradientSumT>
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struct BuilderMock : public QuantileHistMaker::Builder<GradientSumT> {
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using RealImpl = QuantileHistMaker::Builder<GradientSumT>;
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BuilderMock(const TrainParam ¶m, std::unique_ptr<TreeUpdater> pruner,
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DMatrix const *fmat, GenericParameter const* ctx)
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: RealImpl(1, param, std::move(pruner), fmat, ObjInfo{ObjInfo::kRegression}, ctx) {}
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public:
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void TestInitData(const GHistIndexMatrix& gmat,
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std::vector<GradientPair>* gpair,
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DMatrix* p_fmat,
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const RegTree& tree) {
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RealImpl::InitData(gmat, *p_fmat, tree, gpair);
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ASSERT_EQ(this->data_layout_, RealImpl::DataLayout::kSparseData);
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/* The creation of HistCutMatrix and GHistIndexMatrix are not technically
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* part of QuantileHist updater logic, but we include it here because
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* QuantileHist updater object currently stores GHistIndexMatrix
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* internally. According to https://github.com/dmlc/xgboost/pull/3803,
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* we should eventually move GHistIndexMatrix out of the QuantileHist
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* updater. */
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const size_t num_row = p_fmat->Info().num_row_;
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const size_t num_col = p_fmat->Info().num_col_;
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/* Validate HistCutMatrix */
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ASSERT_EQ(gmat.cut.Ptrs().size(), num_col + 1);
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for (size_t fid = 0; fid < num_col; ++fid) {
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const size_t ibegin = gmat.cut.Ptrs()[fid];
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const size_t iend = gmat.cut.Ptrs()[fid + 1];
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// Ordered, but empty feature is allowed.
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ASSERT_LE(ibegin, iend);
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for (size_t i = ibegin; i < iend - 1; ++i) {
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// Quantile points must be sorted in ascending order
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// No duplicates allowed
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ASSERT_LT(gmat.cut.Values()[i], gmat.cut.Values()[i + 1])
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<< "ibegin: " << ibegin << ", "
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<< "iend: " << iend;
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}
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}
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/* Validate GHistIndexMatrix */
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ASSERT_EQ(gmat.row_ptr.size(), num_row + 1);
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ASSERT_LT(*std::max_element(gmat.index.begin(), gmat.index.end()),
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gmat.cut.Ptrs().back());
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for (const auto& batch : p_fmat->GetBatches<xgboost::SparsePage>()) {
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auto page = batch.GetView();
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for (size_t i = 0; i < batch.Size(); ++i) {
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const size_t rid = batch.base_rowid + i;
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ASSERT_LT(rid, num_row);
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const size_t gmat_row_offset = gmat.row_ptr[rid];
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ASSERT_LT(gmat_row_offset, gmat.index.Size());
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SparsePage::Inst inst = page[i];
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ASSERT_EQ(gmat.row_ptr[rid] + inst.size(), gmat.row_ptr[rid + 1]);
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for (size_t j = 0; j < inst.size(); ++j) {
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// Each entry of GHistIndexMatrix represents a bin ID
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const size_t bin_id = gmat.index[gmat_row_offset + j];
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const size_t fid = inst[j].index;
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// The bin ID must correspond to correct feature
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ASSERT_GE(bin_id, gmat.cut.Ptrs()[fid]);
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ASSERT_LT(bin_id, gmat.cut.Ptrs()[fid + 1]);
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// The bin ID must correspond to a region between two
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// suitable quantile points
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ASSERT_LT(inst[j].fvalue, gmat.cut.Values()[bin_id]);
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if (bin_id > gmat.cut.Ptrs()[fid]) {
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ASSERT_GE(inst[j].fvalue, gmat.cut.Values()[bin_id - 1]);
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} else {
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ASSERT_GE(inst[j].fvalue, gmat.cut.MinValues()[fid]);
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}
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}
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}
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}
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}
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};
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int static constexpr kNRows = 8, kNCols = 16;
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std::shared_ptr<xgboost::DMatrix> dmat_;
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GenericParameter ctx_;
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const std::vector<std::pair<std::string, std::string> > cfg_;
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std::shared_ptr<BuilderMock<float> > float_builder_;
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std::shared_ptr<BuilderMock<double> > double_builder_;
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public:
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explicit QuantileHistMock(
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const std::vector<std::pair<std::string, std::string> >& args,
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const bool single_precision_histogram = false, bool batch = true) :
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QuantileHistMaker{ObjInfo{ObjInfo::kRegression}}, cfg_{args} {
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QuantileHistMaker::Configure(args);
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dmat_ = RandomDataGenerator(kNRows, kNCols, 0.8).Seed(3).GenerateDMatrix();
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ctx_.UpdateAllowUnknown(Args{});
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if (single_precision_histogram) {
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float_builder_.reset(new BuilderMock<float>(param_, std::move(pruner_), dmat_.get(), &ctx_));
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} else {
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double_builder_.reset(
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new BuilderMock<double>(param_, std::move(pruner_), dmat_.get(), &ctx_));
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}
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}
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~QuantileHistMock() override = default;
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static size_t GetNumColumns() { return kNCols; }
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void TestInitData() {
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int32_t constexpr kMaxBins = 4;
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GHistIndexMatrix gmat{dmat_.get(), kMaxBins, 0.0f, false, common::OmpGetNumThreads(0)};
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RegTree tree = RegTree();
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tree.param.UpdateAllowUnknown(cfg_);
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std::vector<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} };
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if (double_builder_) {
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double_builder_->TestInitData(gmat, &gpair, dmat_.get(), tree);
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} else {
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float_builder_->TestInitData(gmat, &gpair, dmat_.get(), tree);
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}
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}
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};
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TEST(QuantileHist, InitData) {
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std::vector<std::pair<std::string, std::string>> cfg
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{{"num_feature", std::to_string(QuantileHistMock::GetNumColumns())}};
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QuantileHistMock maker(cfg);
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maker.TestInitData();
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const bool single_precision_histogram = true;
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QuantileHistMock maker_float(cfg, single_precision_histogram);
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maker_float.TestInitData();
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}
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TEST(QuantileHist, Partitioner) {
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size_t n_samples = 1024, n_features = 1, base_rowid = 0;
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GenericParameter ctx;
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ctx.InitAllowUnknown(Args{});
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HistRowPartitioner partitioner{n_samples, base_rowid, ctx.Threads()};
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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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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, 0.5})) {
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bst_feature_t const split_ind = 0;
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common::ColumnMatrix column_indices;
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column_indices.Init(page, 0.5, ctx.Threads());
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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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HistRowPartitioner partitioner{n_samples, base_rowid, ctx.Threads()};
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GetSplit(&tree, min_value, &candidates);
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partitioner.UpdatePosition<false, true>(&ctx, page, column_indices, 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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HistRowPartitioner partitioner{n_samples, base_rowid, ctx.Threads()};
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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<false, true>(&ctx, page, column_indices, 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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