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[WIP] Extract prediction into separate interface (#2531)

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* [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
This commit is contained in:
Rory Mitchell
2017-07-29 12:01:03 +12:00
committed by Tianqi Chen
parent 00eda28b3c
commit 0e06d1805d
11 changed files with 835 additions and 419 deletions
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4
include/xgboost/gbm.h
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@@ -77,7 +77,7 @@ class GradientBooster {
* \param ntree_limit limit the number of trees used in prediction, when it equals 0, this means
* we do not limit number of trees, this parameter is only valid for gbtree, but not for gblinear
*/
virtual void Predict(DMatrix* dmat,
virtual void PredictBatch(DMatrix* dmat,
std::vector<bst_float>* out_preds,
unsigned ntree_limit = 0) = 0;
/*!
@@ -92,7 +92,7 @@ class GradientBooster {
* \param root_index the root index
* \sa Predict
*/
virtual void Predict(const SparseBatch::Inst& inst,
virtual void PredictInstance(const SparseBatch::Inst& inst,
std::vector<bst_float>* out_preds,
unsigned ntree_limit = 0,
unsigned root_index = 0) = 0;

2
include/xgboost/learner.h
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@@ -189,7 +189,7 @@ inline void Learner::Predict(const SparseBatch::Inst& inst,
bool output_margin,
std::vector<bst_float>* out_preds,
unsigned ntree_limit) const {
gbm_->Predict(inst, out_preds, ntree_limit);
gbm_->PredictInstance(inst, out_preds, ntree_limit);
if (!output_margin) {
obj_->PredTransform(out_preds);
}

172
include/xgboost/predictor.h Normal file
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@@ -0,0 +1,172 @@
/*!
* 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"
// Forward declarations
namespace xgboost {
class DMatrix;
class TreeUpdater;
}
namespace xgboost {
namespace gbm {
struct GBTreeModel;
}
} // namespace xgboost
namespace xgboost {
/**
* \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.
*/
class Predictor {
public:
virtual ~Predictor() {}
/**
* \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.
*/
void InitCache(const std::vector<std::shared_ptr<DMatrix> > &cache);
/**
* \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.
*/
virtual void PredictBatch(
DMatrix* dmat, std::vector<bst_float>* out_preds, const gbm::GBTreeModel &model,
int tree_begin, unsigned ntree_limit = 0) = 0;
/**
* \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.
*/
virtual void UpdatePredictionCache(
const gbm::GBTreeModel &model, std::vector<std::unique_ptr<TreeUpdater> >* updaters,
int num_new_trees) = 0;
/**
* \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.
*/
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;
/**
* \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.
*/
virtual void PredictLeaf(DMatrix* dmat, std::vector<bst_float>* out_preds,
const gbm::GBTreeModel& model, unsigned ntree_limit = 0) = 0;
/**
* \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.
*/
virtual void PredictContribution(
DMatrix* dmat, std::vector<bst_float>* out_contribs,
const gbm::GBTreeModel& model, unsigned ntree_limit = 0) = 0;
/**
* \fn static Predictor* Predictor::Create(std::string name);
*
* \brief Creates a new Predictor*.
*
*/
static Predictor* Create(std::string name);
protected:
/**
* \struct PredictionCacheEntry
*
* \brief Contains pointer to input matrix and associated cached predictions.
*/
struct PredictionCacheEntry {
std::shared_ptr<DMatrix> data;
std::vector<bst_float> predictions;
};
/**
* \brief Map of matrices and associated cached predictions to facilitate storing and looking up
* predictions.
*/
std::unordered_map<DMatrix*, PredictionCacheEntry> cache_;
};
/*!
* \brief Registry entry for predictor.
*/
struct PredictorReg
: public dmlc::FunctionRegEntryBase<PredictorReg,
std::function<Predictor*()>> {};
#define XGBOOST_REGISTER_PREDICTOR(UniqueId, Name) \
static DMLC_ATTRIBUTE_UNUSED ::xgboost::PredictorReg& \
__make_##PredictorReg##_##UniqueId##__ = \
::dmlc::Registry<::xgboost::PredictorReg>::Get()->__REGISTER__(Name)
} // namespace xgboost