ccf80703ef
* Clang-tidy static analysis * Modernise checks * Google coding standard checks * Identifier renaming according to Google style
83 lines
2.8 KiB
C++
83 lines
2.8 KiB
C++
/*!
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* Copyright 2015 by 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 <dmlc/parameter.h>
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#include <xgboost/objective.h>
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namespace xgboost {
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namespace 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 dmlc::Parameter<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 std::vector<std::pair<std::string, std::string> >& args) override {
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param_.InitAllowUnknown(args);
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}
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void GetGradient(HostDeviceVector<bst_float> *preds,
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const MetaInfo &info,
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int iter,
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HostDeviceVector<GradientPair> *out_gpair) override {
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out_gpair->Resize(preds->Size());
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std::vector<bst_float>& preds_h = preds->HostVector();
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std::vector<GradientPair>& out_gpair_h = out_gpair->HostVector();
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for (size_t i = 0; i < preds_h.size(); ++i) {
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bst_float w = info.GetWeight(i);
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// scale the negative examples!
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if (info.labels_[i] == 0.0f) w *= param_.scale_neg_weight;
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// logistic transformation
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bst_float p = 1.0f / (1.0f + std::exp(-preds_h[i]));
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// this is the gradient
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bst_float grad = (p - info.labels_[i]) * w;
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// this is the second order gradient
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bst_float hess = p * (1.0f - p) * w;
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out_gpair_h.at(i) = GradientPair(grad, hess);
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}
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}
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const char* DefaultEvalMetric() const override {
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return "error";
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}
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void PredTransform(HostDeviceVector<bst_float> *io_preds) override {
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// transform margin value to probability.
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std::vector<bst_float> &preds = io_preds->HostVector();
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for (size_t i = 0; i < preds.size(); ++i) {
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preds[i] = 1.0f / (1.0f + std::exp(-preds[i]));
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}
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}
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bst_float ProbToMargin(bst_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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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 obj
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} // namespace xgboost
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