78 lines
2.7 KiB
C++
78 lines
2.7 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/common_row_partitioner.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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TEST(QuantileHist, Partitioner) {
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size_t n_samples = 1024, n_features = 1, base_rowid = 0;
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Context ctx;
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ctx.InitAllowUnknown(Args{});
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CommonRowPartitioner partitioner{&ctx, n_samples, base_rowid, false};
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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 cuts = common::SketchOnDMatrix(Xy.get(), 64, ctx.Threads());
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for (auto const& page : Xy->GetBatches<SparsePage>()) {
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GHistIndexMatrix gmat(page, {}, cuts, 64, true, 0.5, ctx.Threads());
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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.InitFromSparse(page, gmat, 0.5, ctx.Threads());
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{
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auto min_value = gmat.cut.MinValues()[split_ind];
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RegTree tree;
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CommonRowPartitioner partitioner{&ctx, n_samples, base_rowid, false};
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GetSplit(&tree, min_value, &candidates);
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partitioner.UpdatePosition<false, true>(&ctx, gmat, 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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CommonRowPartitioner partitioner{&ctx, n_samples, base_rowid, false};
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auto ptr = gmat.cut.Ptrs()[split_ind + 1];
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float split_value = gmat.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, gmat, 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 = gmat.cut.Values().at(gmat.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 = gmat.cut.Values().at(gmat.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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