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/**
* Copyright 2018-2023, XGBoost Contributors
*/
#include <xgboost/objective.h>
#include <xgboost/context.h>
#include <limits>
#include "../helpers.h"
#include "test_hinge.h"
#include "../../../src/common/linalg_op.h"
namespace xgboost {
void TestHingeObj(const Context* ctx) {
std::unique_ptr<ObjFunction> obj{ObjFunction::Create("binary:hinge", ctx)};
float eps = std::numeric_limits<xgboost::bst_float>::min();
std::vector<float> predt{-1.0f, -0.5f, 0.5f, 1.0f, -1.0f, -0.5f, 0.5f, 1.0f};
std::vector<float> label{ 0.0f, 0.0f, 0.0f, 0.0f, 1.0f, 1.0f, 1.0f, 1.0f};
std::vector<float> grad{0.0f, 1.0f, 1.0f, 1.0f, -1.0f, -1.0f, -1.0f, 0.0f};
std::vector<float> hess{eps, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, eps};
CheckObjFunction(obj, predt, label, {1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f}, grad, hess);
CheckObjFunction(obj, predt, label, {/* Empty weight. */}, grad, hess);
ASSERT_EQ(obj->DefaultEvalMetric(), StringView{"error"});
MetaInfo info;
info.num_row_ = label.size();
info.labels.Reshape(info.num_row_, 3);
ASSERT_EQ(obj->Targets(info), 3);
auto h_labels = info.labels.HostView();
for (std::size_t j = 0; j < obj->Targets(info); ++j) {
for (std::size_t i = 0; i < info.num_row_; ++i) {
h_labels(i, j) = label[i];
}
}
linalg::Tensor<float, 2> t_predt{};
t_predt.Reshape(info.labels.Shape());
for (std::size_t j = 0; j < obj->Targets(info); ++j) {
for (std::size_t i = 0; i < info.num_row_; ++i) {
t_predt(i, j) = predt[i];
}
}
linalg::Matrix<GradientPair> out_gpair;
obj->GetGradient(*t_predt.Data(), info, 0, &out_gpair);
for (std::size_t j = 0; j < obj->Targets(info); ++j) {
auto gh = out_gpair.Slice(linalg::All(), j);
ASSERT_EQ(gh.Size(), info.num_row_);
for (std::size_t i = 0; i < gh.Size(); ++i) {
ASSERT_EQ(gh(i).GetGrad(), grad[i]);
ASSERT_EQ(gh(i).GetHess(), hess[i]);
}
}
}
} // namespace xgboost
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