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/**
* Copyright 2015-2023, XGBoost Contributors
* \file custom_metric.cc
* \brief This is an example to define plugin of xgboost.
* This plugin defines the additional metric function.
*/
#include <xgboost/base.h>
#include <xgboost/parameter.h>
#include <xgboost/objective.h>
#include <xgboost/json.h>
namespace xgboost::obj {
// This is a helpful data structure to define parameters
// You do not have to use it.
// see http://dmlc-core.readthedocs.org/en/latest/parameter.html
// for introduction of this module.
struct MyLogisticParam : public XGBoostParameter<MyLogisticParam> {
float scale_neg_weight;
// declare parameters
DMLC_DECLARE_PARAMETER(MyLogisticParam) {
DMLC_DECLARE_FIELD(scale_neg_weight).set_default(1.0f).set_lower_bound(0.0f)
.describe("Scale the weight of negative examples by this factor");
}
};
DMLC_REGISTER_PARAMETER(MyLogisticParam);
// Define a customized logistic regression objective in C++.
// Implement the interface.
class MyLogistic : public ObjFunction {
public:
void Configure(const Args& args) override { param_.UpdateAllowUnknown(args); }
[[nodiscard]] ObjInfo Task() const override { return ObjInfo::kRegression; }
void GetGradient(const HostDeviceVector<float>& preds, MetaInfo const& info,
std::int32_t /*iter*/, linalg::Matrix<GradientPair>* out_gpair) override {
out_gpair->Reshape(info.num_row_, 1);
const std::vector<float>& preds_h = preds.HostVector();
auto out_gpair_h = out_gpair->HostView();
auto const labels_h = info.labels.HostView();
for (size_t i = 0; i < preds_h.size(); ++i) {
float w = info.GetWeight(i);
// scale the negative examples!
if (labels_h(i) == 0.0f) w *= param_.scale_neg_weight;
// logistic transformation
float p = 1.0f / (1.0f + std::exp(-preds_h[i]));
// this is the gradient
float grad = (p - labels_h(i)) * w;
// this is the second order gradient
float hess = p * (1.0f - p) * w;
out_gpair_h(i) = GradientPair(grad, hess);
}
}
[[nodiscard]] const char* DefaultEvalMetric() const override {
return "logloss";
}
void PredTransform(HostDeviceVector<float> *io_preds) const override {
// transform margin value to probability.
std::vector<float> &preds = io_preds->HostVector();
for (auto& pred : preds) {
pred = 1.0f / (1.0f + std::exp(-pred));
}
}
[[nodiscard]] float ProbToMargin(float base_score) const override {
// transform probability to margin value
return -std::log(1.0f / base_score - 1.0f);
}
void SaveConfig(Json* p_out) const override {
auto& out = *p_out;
out["name"] = String("mylogistic");
out["my_logistic_param"] = ToJson(param_);
}
void LoadConfig(Json const& in) override {
FromJson(in["my_logistic_param"], ¶m_);
}
private:
MyLogisticParam param_;
};
// Finally register the objective function.
// After it succeeds you can try use xgboost with objective=mylogistic
XGBOOST_REGISTER_OBJECTIVE(MyLogistic, "mylogistic")
.describe("User defined logistic regression plugin")
.set_body([]() { return new MyLogistic(); });
} // namespace xgboost::obj
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