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.
15 lines
372 B
C++
15 lines
372 B
C++
#include <gtest/gtest.h>
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#include <xgboost/span.h>
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#include "../../../src/common/common.h"
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namespace xgboost {
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namespace common {
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TEST(ArgSort, Basic) {
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std::vector<float> inputs {3.0, 2.0, 1.0};
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auto ret = ArgSort<bst_feature_t>(Span<float>{inputs});
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std::vector<bst_feature_t> sol{2, 1, 0};
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ASSERT_EQ(ret, sol);
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}
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} // namespace common
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} // namespace xgboost
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