e089e16e3d
* Pass pointer to model parameters. This PR de-duplicates most of the model parameters except the one in `tree_model.h`. One difficulty is `base_score` is a model property but can be changed at runtime by objective function. Hence when performing model IO, we need to save the one provided by users, instead of the one transformed by objective. Here we created an immutable version of `LearnerModelParam` that represents the value of model parameter after configuration.
109 lines
3.6 KiB
C++
109 lines
3.6 KiB
C++
/*!
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* Copyright 2014-2019 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 <xgboost/base.h>
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#include <xgboost/data.h>
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#include <xgboost/model.h>
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#include <xgboost/generic_parameters.h>
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#include <xgboost/host_device_vector.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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namespace xgboost {
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/*! \brief interface of objective function */
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class ObjFunction : public Configurable {
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protected:
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GenericParameter const* tparam_;
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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 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(const 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 tparam Generic parameters.
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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, GenericParameter const* tparam);
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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:squarederror")
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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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