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.
107 lines
3.4 KiB
C++
107 lines
3.4 KiB
C++
/*!
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* Copyright 2014-2019 by Contributors
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* \file tree_updater.h
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* \brief General primitive for tree learning,
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* Updating a collection of trees given the information.
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* \author Tianqi Chen
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*/
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#ifndef XGBOOST_TREE_UPDATER_H_
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#define XGBOOST_TREE_UPDATER_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/tree_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 <xgboost/model.h>
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#include <functional>
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#include <vector>
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#include <utility>
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#include <string>
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namespace xgboost {
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class Json;
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/*!
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* \brief interface of tree update module, that performs update of a tree.
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*/
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class TreeUpdater : 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 ~TreeUpdater() = default;
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/*!
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* \brief Initialize the updater with given arguments.
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* \param args arguments to the objective function.
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*/
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virtual void Configure(const Args& args) = 0;
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/*!
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* \brief perform update to the tree models
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* \param gpair the gradient pair statistics of the data
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* \param data The data matrix passed to the updater.
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* \param trees references the trees to be updated, updater will change the content of trees
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* note: all the trees in the vector are updated, with the same statistics,
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* but maybe different random seeds, usually one tree is passed in at a time,
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* there can be multiple trees when we train random forest style model
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*/
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virtual void Update(HostDeviceVector<GradientPair>* gpair,
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DMatrix* data,
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const std::vector<RegTree*>& trees) = 0;
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/*!
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* \brief determines whether updater has enough knowledge about a given dataset
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* to quickly update prediction cache its training data and performs the
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* update if possible.
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* \param data: data matrix
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* \param out_preds: prediction cache to be updated
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* \return boolean indicating whether updater has capability to update
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* the prediction cache. If true, the prediction cache will have been
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* updated by the time this function returns.
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*/
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virtual bool UpdatePredictionCache(const DMatrix* data,
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HostDeviceVector<bst_float>* out_preds) {
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return false;
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}
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virtual char const* Name() const = 0;
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/*!
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* \brief Create a tree updater given name
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* \param name Name of the tree updater.
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*/
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static TreeUpdater* Create(const std::string& name, GenericParameter const* tparam);
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};
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/*!
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* \brief Registry entry for tree updater.
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*/
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struct TreeUpdaterReg
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: public dmlc::FunctionRegEntryBase<TreeUpdaterReg,
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std::function<TreeUpdater* ()> > {
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};
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/*!
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* \brief Macro to register tree updater.
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*
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* \code
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* // example of registering a objective ndcg@k
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* XGBOOST_REGISTER_TREE_UPDATER(ColMaker, "colmaker")
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* .describe("Column based tree maker.")
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* .set_body([]() {
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* return new ColMaker<TStats>();
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* });
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* \endcode
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*/
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#define XGBOOST_REGISTER_TREE_UPDATER(UniqueId, Name) \
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static DMLC_ATTRIBUTE_UNUSED ::xgboost::TreeUpdaterReg& \
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__make_ ## TreeUpdaterReg ## _ ## UniqueId ## __ = \
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::dmlc::Registry< ::xgboost::TreeUpdaterReg>::Get()->__REGISTER__(Name)
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} // namespace xgboost
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#endif // XGBOOST_TREE_UPDATER_H_
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