131 lines
3.5 KiB
C++
131 lines
3.5 KiB
C++
/**
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* Copyright 2022-2023 by XGBoost Contributors
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*/
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#include <gtest/gtest.h>
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#include <xgboost/context.h>
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#include <xgboost/linalg.h> // Tensor,Vector
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#include "../../../src/common/stats.h"
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#include "../../../src/common/transform_iterator.h" // common::MakeIndexTransformIter
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#include "../helpers.h"
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namespace xgboost::common {
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TEST(Stats, Quantile) {
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Context ctx;
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{
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linalg::Tensor<float, 1> arr({20.f, 0.f, 15.f, 50.f, 40.f, 0.f, 35.f}, {7}, DeviceOrd::CPU());
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std::vector<size_t> index{0, 2, 3, 4, 6};
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auto h_arr = arr.HostView();
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auto beg = MakeIndexTransformIter([&](size_t i) { return h_arr(index[i]); });
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auto end = beg + index.size();
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auto q = Quantile(&ctx, 0.40f, beg, end);
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ASSERT_EQ(q, 26.0);
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q = Quantile(&ctx, 0.20f, beg, end);
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ASSERT_EQ(q, 16.0);
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q = Quantile(&ctx, 0.10f, beg, end);
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ASSERT_EQ(q, 15.0);
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}
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{
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std::vector<float> vec{1., 2., 3., 4., 5.};
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auto beg = MakeIndexTransformIter([&](size_t i) { return vec[i]; });
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auto end = beg + vec.size();
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auto q = Quantile(&ctx, 0.5f, beg, end);
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ASSERT_EQ(q, 3.);
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}
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}
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TEST(Stats, WeightedQuantile) {
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Context ctx;
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linalg::Tensor<float, 1> arr({1.f, 2.f, 3.f, 4.f, 5.f}, {5}, DeviceOrd::CPU());
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linalg::Tensor<float, 1> weight({1.f, 1.f, 1.f, 1.f, 1.f}, {5}, DeviceOrd::CPU());
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auto h_arr = arr.HostView();
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auto h_weight = weight.HostView();
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auto beg = MakeIndexTransformIter([&](size_t i) { return h_arr(i); });
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auto end = beg + arr.Size();
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auto w = MakeIndexTransformIter([&](size_t i) { return h_weight(i); });
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auto q = WeightedQuantile(&ctx, 0.50f, beg, end, w);
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ASSERT_EQ(q, 3);
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q = WeightedQuantile(&ctx, 0.0, beg, end, w);
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ASSERT_EQ(q, 1);
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q = WeightedQuantile(&ctx, 1.0, beg, end, w);
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ASSERT_EQ(q, 5);
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}
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TEST(Stats, Median) {
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Context ctx;
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{
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linalg::Tensor<float, 2> values{{.0f, .0f, 1.f, 2.f}, {4}, DeviceOrd::CPU()};
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HostDeviceVector<float> weights;
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linalg::Tensor<float, 1> out;
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Median(&ctx, values, weights, &out);
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auto m = out(0);
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ASSERT_EQ(m, .5f);
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#if defined(XGBOOST_USE_CUDA) || defined(XGBOOST_USE_HIP)
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ctx = ctx.MakeCUDA(0);
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ASSERT_FALSE(ctx.IsCPU());
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Median(&ctx, values, weights, &out);
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m = out(0);
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ASSERT_EQ(m, .5f);
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#endif // defined(XGBOOST_USE_CUDA)
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}
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{
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ctx = ctx.MakeCPU();
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// 4x2 matrix
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linalg::Tensor<float, 2> values{{0.f, 0.f, 0.f, 0.f, 1.f, 1.f, 2.f, 2.f}, {4, 2}, ctx.Device()};
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HostDeviceVector<float> weights;
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linalg::Tensor<float, 1> out;
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Median(&ctx, values, weights, &out);
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ASSERT_EQ(out(0), .5f);
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ASSERT_EQ(out(1), .5f);
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#if defined(XGBOOST_USE_CUDA) || defined(XGBOOST_USE_HIP)
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ctx = ctx.MakeCUDA(0);
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Median(&ctx, values, weights, &out);
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ASSERT_EQ(out(0), .5f);
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ASSERT_EQ(out(1), .5f);
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#endif // defined(XGBOOST_USE_CUDA)
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}
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}
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namespace {
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void TestMean(Context const* ctx) {
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std::size_t n{128};
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linalg::Vector<float> data({n}, ctx->Device());
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auto h_v = data.HostView().Values();
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std::iota(h_v.begin(), h_v.end(), .0f);
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auto nf = static_cast<float>(n);
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float mean = nf * (nf - 1) / 2 / n;
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linalg::Vector<float> res{{1}, ctx->Device()};
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Mean(ctx, data, &res);
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auto h_res = res.HostView();
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ASSERT_EQ(h_res.Size(), 1);
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ASSERT_EQ(mean, h_res(0));
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}
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} // anonymous namespace
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TEST(Stats, Mean) {
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Context ctx;
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TestMean(&ctx);
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}
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#if defined(XGBOOST_USE_CUDA) || defined(XGBOOST_USE_HIP)
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TEST(Stats, GPUMean) {
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auto ctx = MakeCUDACtx(0);
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TestMean(&ctx);
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}
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#endif // defined(XGBOOST_USE_CUDA)
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} // namespace xgboost::common
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