ccf80703ef
* Clang-tidy static analysis * Modernise checks * Google coding standard checks * Identifier renaming according to Google style
118 lines
3.9 KiB
C++
118 lines
3.9 KiB
C++
/*!
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* Copyright 2014 by Contributors
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* \file objective.h
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* \brief interface of objective function used by xgboost.
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* \author Tianqi Chen, Kailong Chen
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*/
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#ifndef XGBOOST_OBJECTIVE_H_
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#define XGBOOST_OBJECTIVE_H_
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#include <dmlc/registry.h>
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#include <vector>
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#include <utility>
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#include <string>
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#include <functional>
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#include "./data.h"
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#include "./base.h"
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#include "../../src/common/host_device_vector.h"
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namespace xgboost {
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/*! \brief interface of objective function */
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class ObjFunction {
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public:
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/*! \brief virtual destructor */
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virtual ~ObjFunction() = default;
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/*!
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* \brief set configuration from pair iterators.
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* \param begin The beginning iterator.
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* \param end The end iterator.
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* \tparam PairIter iterator<std::pair<std::string, std::string> >
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*/
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template<typename PairIter>
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inline void Configure(PairIter begin, PairIter end);
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/*!
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* \brief Configure the objective with the specified parameters.
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* \param args arguments to the objective function.
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*/
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virtual void Configure(const std::vector<std::pair<std::string, std::string> >& args) = 0;
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/*!
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* \brief Get gradient over each of predictions, given existing information.
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* \param preds prediction of current round
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* \param info information about labels, weights, groups in rank
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* \param iteration current iteration number.
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* \param out_gpair output of get gradient, saves gradient and second order gradient in
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*/
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virtual void GetGradient(HostDeviceVector<bst_float>* preds,
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const MetaInfo& info,
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int iteration,
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HostDeviceVector<GradientPair>* out_gpair) = 0;
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/*! \return the default evaluation metric for the objective */
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virtual const char* DefaultEvalMetric() const = 0;
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// the following functions are optional, most of time default implementation is good enough
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/*!
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* \brief transform prediction values, this is only called when Prediction is called
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* \param io_preds prediction values, saves to this vector as well
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*/
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virtual void PredTransform(HostDeviceVector<bst_float> *io_preds) {}
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/*!
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* \brief transform prediction values, this is only called when Eval is called,
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* usually it redirect to PredTransform
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* \param io_preds prediction values, saves to this vector as well
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*/
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virtual void EvalTransform(HostDeviceVector<bst_float> *io_preds) {
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this->PredTransform(io_preds);
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}
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/*!
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* \brief transform probability value back to margin
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* this is used to transform user-set base_score back to margin
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* used by gradient boosting
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* \return transformed value
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*/
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virtual bst_float ProbToMargin(bst_float base_score) const {
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return base_score;
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}
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/*!
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* \brief Create an objective function according to name.
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* \param name Name of the objective.
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*/
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static ObjFunction* Create(const std::string& name);
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};
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// implementing configure.
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template<typename PairIter>
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inline void ObjFunction::Configure(PairIter begin, PairIter end) {
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std::vector<std::pair<std::string, std::string> > vec(begin, end);
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this->Configure(vec);
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}
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/*!
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* \brief Registry entry for objective factory functions.
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*/
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struct ObjFunctionReg
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: public dmlc::FunctionRegEntryBase<ObjFunctionReg,
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std::function<ObjFunction* ()> > {
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};
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/*!
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* \brief Macro to register objective function.
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*
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* \code
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* // example of registering a objective
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* XGBOOST_REGISTER_OBJECTIVE(LinearRegression, "reg:linear")
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* .describe("Linear regression objective")
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* .set_body([]() {
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* return new RegLossObj(LossType::kLinearSquare);
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* });
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* \endcode
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*/
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#define XGBOOST_REGISTER_OBJECTIVE(UniqueId, Name) \
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static DMLC_ATTRIBUTE_UNUSED ::xgboost::ObjFunctionReg & \
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__make_ ## ObjFunctionReg ## _ ## UniqueId ## __ = \
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::dmlc::Registry< ::xgboost::ObjFunctionReg>::Get()->__REGISTER__(Name)
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} // namespace xgboost
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#endif // XGBOOST_OBJECTIVE_H_
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