74 lines
2.0 KiB
C++
74 lines
2.0 KiB
C++
/*!
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* Copyright 2022 by XGBoost Contributors
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*/
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#include <gtest/gtest.h>
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#include <xgboost/generic_parameters.h>
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#include "../../../src/common/stats.h"
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namespace xgboost {
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namespace common {
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TEST(Stats, Quantile) {
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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}, Context::kCpuId);
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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(0.40f, beg, end);
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ASSERT_EQ(q, 26.0);
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q = Quantile(0.20f, beg, end);
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ASSERT_EQ(q, 16.0);
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q = Quantile(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(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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linalg::Tensor<float, 1> arr({1.f, 2.f, 3.f, 4.f, 5.f}, {5}, Context::kCpuId);
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linalg::Tensor<float, 1> weight({1.f, 1.f, 1.f, 1.f, 1.f}, {5}, Context::kCpuId);
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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(0.50f, beg, end, w);
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ASSERT_EQ(q, 3);
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q = WeightedQuantile(0.0, beg, end, w);
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ASSERT_EQ(q, 1);
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q = WeightedQuantile(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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linalg::Tensor<float, 2> values{{.0f, .0f, 1.f, 2.f}, {4}, Context::kCpuId};
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Context ctx;
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HostDeviceVector<float> weights;
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auto m = Median(&ctx, values, weights);
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ASSERT_EQ(m, .5f);
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#if defined(XGBOOST_USE_CUDA)
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ctx.gpu_id = 0;
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ASSERT_FALSE(ctx.IsCPU());
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m = Median(&ctx, values, weights);
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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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} // namespace common
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} // namespace xgboost
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