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