c589eff941
* Only define `gpu_id` and `n_gpus` in `LearnerTrainParam` * Pass LearnerTrainParam through XGBoost vid factory method. * Disable all GPU usage when GPU related parameters are not specified (fixes XGBoost choosing GPU over aggressively). * Test learner train param io. * Fix gpu pickling.
102 lines
3.3 KiB
C++
102 lines
3.3 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 <functional>
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#include <vector>
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#include <utility>
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#include <string>
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#include "../../src/common/host_device_vector.h"
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namespace xgboost {
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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 {
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protected:
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LearnerTrainParam 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 Init(const std::vector<std::pair<std::string, std::string> >& 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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/*!
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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, LearnerTrainParam 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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