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gbm.h
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gbm.h
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/*!
* Copyright 2014-2020 by Contributors
* \file gbm.h
* \brief Interface of gradient booster,
* that learns through gradient statistics.
* \author Tianqi Chen
*/
#ifndef XGBOOST_GBM_H_
#define XGBOOST_GBM_H_
#include <dmlc/registry.h>
#include <dmlc/any.h>
#include <xgboost/base.h>
#include <xgboost/data.h>
#include <xgboost/host_device_vector.h>
#include <xgboost/model.h>
#include <vector>
#include <utility>
#include <string>
#include <functional>
#include <unordered_map>
#include <memory>
namespace xgboost {
class Json;
class FeatureMap;
class ObjFunction;
struct GenericParameter;
struct LearnerModelParam;
struct PredictionCacheEntry;
class PredictionContainer;
/*!
* \brief interface of gradient boosting model.
*/
class GradientBooster : public Model, public Configurable {
protected:
GenericParameter const* generic_param_;
public:
/*! \brief virtual destructor */
~GradientBooster() override = default;
/*!
* \brief Set the configuration of gradient boosting.
* User must call configure once before InitModel and Training.
*
* \param cfg configurations on both training and model parameters.
*/
virtual void Configure(const std::vector<std::pair<std::string, std::string> >& cfg) = 0;
/*!
* \brief load model from stream
* \param fi input stream.
*/
virtual void Load(dmlc::Stream* fi) = 0;
/*!
* \brief save model to stream.
* \param fo output stream
*/
virtual void Save(dmlc::Stream* fo) const = 0;
/*!
* \brief Slice a model using boosting index. The slice m:n indicates taking all trees
* that were fit during the boosting rounds m, (m+1), (m+2), ..., (n-1).
* \param layer_begin Begining of boosted tree layer used for prediction.
* \param layer_end End of booster layer. 0 means do not limit trees.
* \param out Output gradient booster
*/
virtual void Slice(int32_t layer_begin, int32_t layer_end, int32_t step,
GradientBooster *out, bool* out_of_bound) const {
LOG(FATAL) << "Slice is not supported by current booster.";
}
/*!
* \brief whether the model allow lazy checkpoint
* return true if model is only updated in DoBoost
* after all Allreduce calls
*/
virtual bool AllowLazyCheckPoint() const {
return false;
}
/*! \brief Return number of boosted rounds.
*/
virtual int32_t BoostedRounds() const = 0;
/*!
* \brief perform update to the model(boosting)
* \param p_fmat feature matrix that provide access to features
* \param in_gpair address of the gradient pair statistics of the data
* \param prediction The output prediction cache entry that needs to be updated.
* the booster may change content of gpair
*/
virtual void DoBoost(DMatrix* p_fmat,
HostDeviceVector<GradientPair>* in_gpair,
PredictionCacheEntry*) = 0;
/*!
* \brief generate predictions for given feature matrix
* \param dmat feature matrix
* \param out_preds output vector to hold the predictions
* \param training Whether the prediction value is used for training. For dart booster
* drop out is performed during training.
* \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 PredictBatch(DMatrix* dmat,
PredictionCacheEntry* out_preds,
bool training,
unsigned ntree_limit = 0) = 0;
/*!
* \brief Inplace prediction.
*
* \param x A type erased data adapter.
* \param missing Missing value in the data.
* \param [in,out] out_preds The output preds.
* \param layer_begin (Optional) Begining of boosted tree layer used for prediction.
* \param layer_end (Optional) End of booster layer. 0 means do not limit trees.
*/
virtual void InplacePredict(dmlc::any const &, float,
PredictionCacheEntry*,
uint32_t,
uint32_t) const {
LOG(FATAL) << "Inplace predict is not supported by current booster.";
}
/*!
* \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 you want to predict
* \param out_preds output vector to hold the predictions
* \param ntree_limit limit the number of trees used in prediction
* \sa Predict
*/
virtual void PredictInstance(const SparsePage::Inst& inst,
std::vector<bst_float>* out_preds,
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 dmat feature matrix
* \param out_preds output vector to hold the predictions
* \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 PredictLeaf(DMatrix* dmat,
HostDeviceVector<bst_float>* out_preds,
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 dmat feature matrix
* \param out_contribs output vector to hold the contributions
* \param ntree_limit limit the number of trees used in prediction, when it equals 0, this means
* we do not limit number of trees
* \param approximate use a faster (inconsistent) approximation of SHAP values
* \param condition condition on the condition_feature (0=no, -1=cond off, 1=cond on).
* \param condition_feature feature to condition on (i.e. fix) during calculations
*/
virtual void PredictContribution(DMatrix* dmat,
HostDeviceVector<bst_float>* out_contribs,
unsigned ntree_limit = 0,
bool approximate = false, int condition = 0,
unsigned condition_feature = 0) = 0;
virtual void PredictInteractionContributions(DMatrix* dmat,
HostDeviceVector<bst_float>* out_contribs,
unsigned ntree_limit, bool approximate) = 0;
/*!
* \brief dump the model in the requested format
* \param fmap feature map that may help give interpretations of feature
* \param with_stats extra statistics while dumping model
* \param format the format to dump the model in
* \return a vector of dump for boosters.
*/
virtual std::vector<std::string> DumpModel(const FeatureMap& fmap,
bool with_stats,
std::string format) const = 0;
/*!
* \brief Whether the current booster uses GPU.
*/
virtual bool UseGPU() const = 0;
/*!
* \brief create a gradient booster from given name
* \param name name of gradient booster
* \param generic_param Pointer to runtime parameters
* \param learner_model_param pointer to global model parameters
* \return The created booster.
*/
static GradientBooster* Create(
const std::string& name,
GenericParameter const* generic_param,
LearnerModelParam const* learner_model_param);
};
/*!
* \brief Registry entry for tree updater.
*/
struct GradientBoosterReg
: public dmlc::FunctionRegEntryBase<
GradientBoosterReg,
std::function<GradientBooster* (LearnerModelParam const* learner_model_param)> > {
};
/*!
* \brief Macro to register gradient booster.
*
* \code
* // example of registering a objective ndcg@k
* XGBOOST_REGISTER_GBM(GBTree, "gbtree")
* .describe("Boosting tree ensembles.")
* .set_body([]() {
* return new GradientBooster<TStats>();
* });
* \endcode
*/
#define XGBOOST_REGISTER_GBM(UniqueId, Name) \
static DMLC_ATTRIBUTE_UNUSED ::xgboost::GradientBoosterReg & \
__make_ ## GradientBoosterReg ## _ ## UniqueId ## __ = \
::dmlc::Registry< ::xgboost::GradientBoosterReg>::Get()->__REGISTER__(Name)
} // namespace xgboost
#endif // XGBOOST_GBM_H_