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