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[WIP] Extract prediction into separate interface (#2531)
* [WIP] Extract prediction into separate interface * Add copyright, fix linter errors * Add predictor to amalgamation * Fix documentation * Move prediction cache into predictor, add GBTreeModel * Updated predictor doc comments
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| /*! | ||
| * Copyright by Contributors | ||
| * \file predictor.h | ||
| * \brief Interface of predictor, | ||
| * performs predictions for a gradient booster. | ||
| */ | ||
| #pragma once | ||
| #include <xgboost/base.h> | ||
| #include <functional> | ||
| #include <memory> | ||
| #include <vector> | ||
| #include <string> | ||
| #include "../../src/gbm/gbtree_model.h" | ||
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| // Forward declarations | ||
| namespace xgboost { | ||
| class DMatrix; | ||
| class TreeUpdater; | ||
| } | ||
| namespace xgboost { | ||
| namespace gbm { | ||
| struct GBTreeModel; | ||
| } | ||
| } // namespace xgboost | ||
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| namespace xgboost { | ||
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| /** | ||
| * \class Predictor | ||
| * | ||
| * \brief Performs prediction on individual training instances or batches of instances for GBTree. | ||
| * The predictor also manages a prediction cache associated with input matrices. If possible, | ||
| * it will use previously calculated predictions instead of calculating new predictions. | ||
| * Prediction functions all take a GBTreeModel and a DMatrix as input and output a vector of | ||
| * predictions. The predictor does not modify any state of the model itself. | ||
| */ | ||
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| class Predictor { | ||
| public: | ||
| virtual ~Predictor() {} | ||
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| /** | ||
| * \fn void Predictor::InitCache(const std::vector<std::shared_ptr<DMatrix> > &cache); | ||
| * | ||
| * \brief Register input matrices in prediction cache. | ||
| * | ||
| * \param cache Vector of DMatrix's to be used in prediction. | ||
| */ | ||
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| void InitCache(const std::vector<std::shared_ptr<DMatrix> > &cache); | ||
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| /** | ||
| * \fn virtual void Predictor::PredictBatch( DMatrix* dmat, std::vector<bst_float>* out_preds, const gbm::GBTreeModel &model, int tree_begin, unsigned ntree_limit = 0) = 0; | ||
| * | ||
| * \brief Generate batch predictions for a given feature matrix. May use cached predictions if available instead of calculating from scratch. | ||
| * | ||
| * \param [in,out] dmat Feature matrix. | ||
| * \param [in,out] out_preds The output preds. | ||
| * \param model The model to predict from. | ||
| * \param tree_begin The tree begin index. | ||
| * \param ntree_limit (Optional) The ntree limit. 0 means do not limit trees. | ||
| */ | ||
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| virtual void PredictBatch( | ||
| DMatrix* dmat, std::vector<bst_float>* out_preds, const gbm::GBTreeModel &model, | ||
| int tree_begin, unsigned ntree_limit = 0) = 0; | ||
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| /** | ||
| * \fn virtual void Predictor::UpdatePredictionCache( const gbm::GBTreeModel &model, std::vector<std::unique_ptr<TreeUpdater> >* updaters, int num_new_trees) = 0; | ||
| * | ||
| * \brief Update the internal prediction cache using newly added trees. Will use the tree updater | ||
| * to do this if possible. Should be called as a part of the tree boosting process to facilitate the look up of predictions at a later time. | ||
| * | ||
| * \param model The model. | ||
| * \param [in,out] updaters The updater sequence for gradient boosting. | ||
| * \param num_new_trees Number of new trees. | ||
| */ | ||
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| virtual void UpdatePredictionCache( | ||
| const gbm::GBTreeModel &model, std::vector<std::unique_ptr<TreeUpdater> >* updaters, | ||
| int num_new_trees) = 0; | ||
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| /** | ||
| * \fn virtual void Predictor::PredictInstance( const SparseBatch::Inst& inst, std::vector<bst_float>* out_preds, const gbm::GBTreeModel& model, unsigned ntree_limit = 0, unsigned root_index = 0) = 0; | ||
| * | ||
| * \brief online prediction function, predict score for one instance at a time NOTE: use the batch | ||
| * prediction interface if possible, batch prediction is usually more efficient than online | ||
| * prediction This function is NOT threadsafe, make sure you only call from one thread. | ||
| * | ||
| * \param inst The instance to predict. | ||
| * \param [in,out] out_preds The output preds. | ||
| * \param model The model to predict from | ||
| * \param ntree_limit (Optional) The ntree limit. | ||
| * \param root_index (Optional) Zero-based index of the root. | ||
| */ | ||
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| virtual void PredictInstance( | ||
| const SparseBatch::Inst& inst, std::vector<bst_float>* out_preds, | ||
| const gbm::GBTreeModel& model, unsigned ntree_limit = 0, unsigned root_index = 0) = 0; | ||
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| /** | ||
| * \fn virtual void Predictor::PredictLeaf(DMatrix* dmat, std::vector<bst_float>* out_preds, const gbm::GBTreeModel& model, unsigned ntree_limit = 0) = 0; | ||
| * | ||
| * \brief predict the leaf index of each tree, the output will be nsample * ntree vector this is | ||
| * only valid in gbtree predictor. | ||
| * | ||
| * \param [in,out] dmat The input feature matrix. | ||
| * \param [in,out] out_preds The output preds. | ||
| * \param model Model to make predictions from. | ||
| * \param ntree_limit (Optional) The ntree limit. | ||
| */ | ||
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| virtual void PredictLeaf(DMatrix* dmat, std::vector<bst_float>* out_preds, | ||
| const gbm::GBTreeModel& model, unsigned ntree_limit = 0) = 0; | ||
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| /** | ||
| * \fn virtual void Predictor::PredictContribution( DMatrix* dmat, std::vector<bst_float>* out_contribs, const gbm::GBTreeModel& model, unsigned ntree_limit = 0) = 0; | ||
| * | ||
| * \brief feature contributions to individual predictions; the output will be a vector of length | ||
| * (nfeats + 1) * num_output_group * nsample, arranged in that order. | ||
| * | ||
| * \param [in,out] dmat The input feature matrix. | ||
| * \param [in,out] out_contribs The output feature contribs. | ||
| * \param model Model to make predictions from. | ||
| * \param ntree_limit (Optional) The ntree limit. | ||
| */ | ||
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| virtual void PredictContribution( | ||
| DMatrix* dmat, std::vector<bst_float>* out_contribs, | ||
| const gbm::GBTreeModel& model, unsigned ntree_limit = 0) = 0; | ||
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| /** | ||
| * \fn static Predictor* Predictor::Create(std::string name); | ||
| * | ||
| * \brief Creates a new Predictor*. | ||
| * | ||
| */ | ||
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| static Predictor* Create(std::string name); | ||
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| protected: | ||
| /** | ||
| * \struct PredictionCacheEntry | ||
| * | ||
| * \brief Contains pointer to input matrix and associated cached predictions. | ||
| */ | ||
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| struct PredictionCacheEntry { | ||
| std::shared_ptr<DMatrix> data; | ||
| std::vector<bst_float> predictions; | ||
| }; | ||
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| /** | ||
| * \brief Map of matrices and associated cached predictions to facilitate storing and looking up | ||
| * predictions. | ||
| */ | ||
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| std::unordered_map<DMatrix*, PredictionCacheEntry> cache_; | ||
| }; | ||
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| /*! | ||
| * \brief Registry entry for predictor. | ||
| */ | ||
| struct PredictorReg | ||
| : public dmlc::FunctionRegEntryBase<PredictorReg, | ||
| std::function<Predictor*()>> {}; | ||
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| #define XGBOOST_REGISTER_PREDICTOR(UniqueId, Name) \ | ||
| static DMLC_ATTRIBUTE_UNUSED ::xgboost::PredictorReg& \ | ||
| __make_##PredictorReg##_##UniqueId##__ = \ | ||
| ::dmlc::Registry<::xgboost::PredictorReg>::Get()->__REGISTER__(Name) | ||
| } // namespace xgboost |
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