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