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/**
* Copyright 2023 by XGBoost Contributors
*/
#include <gtest/gtest.h>
#include <cstddef> // std::size_t
#include <string> // std::to_string
#include "../../../../src/tree/hist/sampler.h" // SampleGradient
#include "../../../../src/tree/param.h" // TrainParam
#include "xgboost/base.h" // GradientPair,bst_target_t
#include "xgboost/context.h" // Context
#include "xgboost/data.h" // MetaInfo
#include "xgboost/linalg.h" // Matrix,Constants
namespace xgboost {
namespace tree {
TEST(Sampler, Basic) {
std::size_t constexpr kRows = 1024;
double constexpr kSubsample = .2;
TrainParam param;
param.UpdateAllowUnknown(Args{{"subsample", std::to_string(kSubsample)}});
Context ctx;
auto run = [&](bst_target_t n_targets) {
auto init = GradientPair{1.0f, 1.0f};
linalg::Matrix<GradientPair> gpair = linalg::Constant(&ctx, init, kRows, n_targets);
auto h_gpair = gpair.HostView();
SampleGradient(&ctx, param, h_gpair);
std::size_t n_sampled{0};
for (std::size_t i = 0; i < kRows; ++i) {
bool sampled{false};
if (h_gpair(i, 0).GetGrad() - .0f != .0f) {
sampled = true;
n_sampled++;
}
for (bst_target_t t = 1; t < n_targets; ++t) {
if (sampled) {
ASSERT_EQ(h_gpair(i, t).GetGrad() - init.GetGrad(), .0f);
ASSERT_EQ(h_gpair(i, t).GetHess() - init.GetHess(), .0f);
} else {
ASSERT_EQ(h_gpair(i, t).GetGrad() - .0f, .0f);
ASSERT_EQ(h_gpair(i, t).GetHess() - .0f, .0f);
}
}
}
auto ratio = static_cast<double>(n_sampled) / static_cast<double>(kRows);
ASSERT_LT(ratio, kSubsample * 1.5);
ASSERT_GT(ratio, kSubsample * 0.5);
};
run(1);
run(3);
}
} // namespace tree
} // namespace xgboost
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