93 lines
3.2 KiB
C++
93 lines
3.2 KiB
C++
/**
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* Copyright 2022-2024, XGBoost Contributors
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*/
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#include <gtest/gtest.h>
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#include <xgboost/base.h> // for GradientPairInternal, GradientPairPrecise
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#include <xgboost/data.h> // for MetaInfo
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#include <xgboost/host_device_vector.h> // for HostDeviceVector
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#include <xgboost/span.h> // for operator!=, Span, SpanIterator
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#include <algorithm> // for max, max_element, next_permutation, copy
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#include <cmath> // for isnan
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#include <cstddef> // for size_t
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#include <cstdint> // for int32_t, uint64_t, uint32_t
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#include <limits> // for numeric_limits
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#include <vector> // for vector
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#include "../../../src/common/hist_util.h" // for HistogramCuts, HistCollection, GHistRow
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#include "../../../src/tree/hist/hist_cache.h" // for HistogramCollection
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#include "../../../src/tree/param.h" // for TrainParam, GradStats
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namespace xgboost::tree {
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/**
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* @brief Enumerate all possible partitions for categorical split.
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*/
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class TestPartitionBasedSplit : public ::testing::Test {
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protected:
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size_t n_bins_ = 6;
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std::vector<size_t> sorted_idx_;
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TrainParam param_;
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MetaInfo info_;
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float best_score_{-std::numeric_limits<float>::infinity()};
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common::HistogramCuts cuts_;
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BoundedHistCollection hist_;
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GradientPairPrecise total_gpair_;
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void SetUp() override;
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};
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inline auto MakeCutsForTest(std::vector<float> values, std::vector<uint32_t> ptrs,
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std::vector<float> min_values, DeviceOrd device) {
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common::HistogramCuts cuts;
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cuts.cut_values_.HostVector() = values;
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cuts.cut_ptrs_.HostVector() = ptrs;
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cuts.min_vals_.HostVector() = min_values;
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if (device.IsCUDA()) {
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cuts.cut_ptrs_.SetDevice(device);
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cuts.cut_values_.SetDevice(device);
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cuts.min_vals_.SetDevice(device);
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}
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return cuts;
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}
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class TestCategoricalSplitWithMissing : public testing::Test {
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protected:
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common::HistogramCuts cuts_;
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// Setup gradients and parent sum with missing values.
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GradientPairPrecise parent_sum_{1.0, 6.0};
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std::vector<GradientPairPrecise> feature_histogram_{
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{0.5, 0.5}, {0.5, 0.5}, {1.0, 1.0}, {1.0, 1.0}};
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TrainParam param_;
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void SetUp() override {
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cuts_ = MakeCutsForTest({0.0, 1.0, 2.0, 3.0}, {0, 4}, {0.0}, DeviceOrd::CPU());
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auto max_cat = *std::max_element(cuts_.cut_values_.HostVector().begin(),
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cuts_.cut_values_.HostVector().end());
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cuts_.SetCategorical(true, max_cat);
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param_.UpdateAllowUnknown(
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Args{{"min_child_weight", "0"}, {"reg_lambda", "0"}, {"max_cat_to_onehot", "1"}});
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}
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void CheckResult(float loss_chg, bst_feature_t split_ind, float fvalue, bool is_cat,
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bool dft_left, GradientPairPrecise left_sum, GradientPairPrecise right_sum) {
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// forward
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// it: 0, gain: 0.545455
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// it: 1, gain: 1.000000
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// it: 2, gain: 2.250000
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// backward
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// it: 3, gain: 1.000000
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// it: 2, gain: 2.250000
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// it: 1, gain: 3.142857
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ASSERT_NEAR(loss_chg, 2.97619, kRtEps);
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ASSERT_TRUE(is_cat);
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ASSERT_TRUE(std::isnan(fvalue));
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ASSERT_EQ(split_ind, 0);
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ASSERT_FALSE(dft_left);
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ASSERT_EQ(left_sum.GetHess(), 2.5);
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ASSERT_EQ(right_sum.GetHess(), parent_sum_.GetHess() - left_sum.GetHess());
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}
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};
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} // namespace xgboost::tree
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