bcc0277338
* Re-implement ROC-AUC. * Binary * MultiClass * LTR * Add documents. This PR resolves a few issues: - Define a value when the dataset is invalid, which can happen if there's an empty dataset, or when the dataset contains only positive or negative values. - Define ROC-AUC for multi-class classification. - Define weighted average value for distributed setting. - A correct implementation for learning to rank task. Previous implementation is just binary classification with averaging across groups, which doesn't measure ordered learning to rank.
67 lines
1.8 KiB
Plaintext
67 lines
1.8 KiB
Plaintext
#include <gtest/gtest.h>
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#include "../../../src/common/ranking_utils.cuh"
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#include "../../../src/common/device_helpers.cuh"
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namespace xgboost {
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namespace common {
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TEST(SegmentedTrapezoidThreads, Basic) {
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size_t constexpr kElements = 24, kGroups = 3;
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dh::device_vector<size_t> offset_ptr(kGroups + 1, 0);
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offset_ptr[0] = 0;
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offset_ptr[1] = 8;
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offset_ptr[2] = 16;
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offset_ptr[kGroups] = kElements;
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size_t h = 1;
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dh::device_vector<size_t> thread_ptr(kGroups + 1, 0);
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size_t total = SegmentedTrapezoidThreads(dh::ToSpan(offset_ptr), dh::ToSpan(thread_ptr), h);
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ASSERT_EQ(total, kElements - kGroups);
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h = 2;
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SegmentedTrapezoidThreads(dh::ToSpan(offset_ptr), dh::ToSpan(thread_ptr), h);
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std::vector<size_t> h_thread_ptr(thread_ptr.size());
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thrust::copy(thread_ptr.cbegin(), thread_ptr.cend(), h_thread_ptr.begin());
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for (size_t i = 1; i < h_thread_ptr.size(); ++i) {
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ASSERT_EQ(h_thread_ptr[i] - h_thread_ptr[i - 1], 13);
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}
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h = 7;
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SegmentedTrapezoidThreads(dh::ToSpan(offset_ptr), dh::ToSpan(thread_ptr), h);
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thrust::copy(thread_ptr.cbegin(), thread_ptr.cend(), h_thread_ptr.begin());
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for (size_t i = 1; i < h_thread_ptr.size(); ++i) {
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ASSERT_EQ(h_thread_ptr[i] - h_thread_ptr[i - 1], 28);
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}
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}
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TEST(SegmentedTrapezoidThreads, Unravel) {
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size_t i = 0, j = 0;
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size_t constexpr kN = 8;
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UnravelTrapeziodIdx(6, kN, &i, &j);
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ASSERT_EQ(i, 0);
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ASSERT_EQ(j, 7);
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UnravelTrapeziodIdx(12, kN, &i, &j);
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ASSERT_EQ(i, 1);
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ASSERT_EQ(j, 7);
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UnravelTrapeziodIdx(15, kN, &i, &j);
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ASSERT_EQ(i, 2);
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ASSERT_EQ(j, 5);
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UnravelTrapeziodIdx(21, kN, &i, &j);
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ASSERT_EQ(i, 3);
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ASSERT_EQ(j, 7);
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UnravelTrapeziodIdx(25, kN, &i, &j);
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ASSERT_EQ(i, 5);
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ASSERT_EQ(j, 6);
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UnravelTrapeziodIdx(27, kN, &i, &j);
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ASSERT_EQ(i, 6);
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ASSERT_EQ(j, 7);
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}
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} // namespace common
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} // namespace xgboost
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