11 #include <dmlc/registry.h> 22 #include <unordered_map> 31 struct GenericParameter;
32 struct LearnerModelParam;
33 struct PredictionCacheEntry;
34 class PredictionContainer;
52 virtual void Configure(
const std::vector<std::pair<std::string, std::string> >& cfg) = 0;
57 virtual void Load(dmlc::Stream* fi) = 0;
62 virtual void Save(dmlc::Stream* fo)
const = 0;
95 unsigned ntree_limit = 0) = 0;
108 uint32_t layer_begin = 0,
109 uint32_t layer_end = 0)
const {
110 LOG(FATAL) <<
"Inplace predict is not supported by current booster.";
124 std::vector<bst_float>* out_preds,
125 unsigned ntree_limit = 0) = 0;
135 std::vector<bst_float>* out_preds,
136 unsigned ntree_limit = 0) = 0;
150 std::vector<bst_float>* out_contribs,
151 unsigned ntree_limit = 0,
152 bool approximate =
false,
int condition = 0,
153 unsigned condition_feature = 0) = 0;
156 std::vector<bst_float>* out_contribs,
157 unsigned ntree_limit,
bool approximate) = 0;
168 std::string format)
const = 0;
172 virtual bool UseGPU()
const = 0;
181 const std::string& name,
190 :
public dmlc::FunctionRegEntryBase<
192 std::function<GradientBooster* (LearnerModelParam const* learner_model_param)> > {
207 #define XGBOOST_REGISTER_GBM(UniqueId, Name) \ 208 static DMLC_ATTRIBUTE_UNUSED ::xgboost::GradientBoosterReg & \ 209 __make_ ## GradientBoosterReg ## _ ## UniqueId ## __ = \ 210 ::dmlc::Registry< ::xgboost::GradientBoosterReg>::Get()->__REGISTER__(Name) 213 #endif // XGBOOST_GBM_H_ static GradientBooster * Create(const std::string &name, GenericParameter const *generic_param, LearnerModelParam const *learner_model_param)
create a gradient booster from given name
virtual void InplacePredict(dmlc::any const &x, float missing, PredictionCacheEntry *out_preds, uint32_t layer_begin=0, uint32_t layer_end=0) const
Inplace prediction.
Definition: gbm.h:106
Definition: learner.h:241
virtual std::vector< std::string > DumpModel(const FeatureMap &fmap, bool with_stats, std::string format) const =0
dump the model in the requested format
virtual void PredictContribution(DMatrix *dmat, std::vector< bst_float > *out_contribs, unsigned ntree_limit=0, bool approximate=false, int condition=0, unsigned condition_feature=0)=0
feature contributions to individual predictions; the output will be a vector of length (nfeats + 1) *...
Definition: host_device_vector.h:86
virtual void PredictInteractionContributions(DMatrix *dmat, std::vector< bst_float > *out_contribs, unsigned ntree_limit, bool approximate)=0
The input data structure of xgboost.
virtual void PredictBatch(DMatrix *dmat, PredictionCacheEntry *out_preds, bool training, unsigned ntree_limit=0)=0
generate predictions for given feature matrix
Definition: generic_parameters.h:14
Defines the abstract interface for different components in XGBoost.
Internal data structured used by XGBoost during training.
Definition: data.h:464
A device-and-host vector abstraction layer.
Feature map data structure to help text model dump. TODO(tqchen) consider make it even more lightweig...
Definition: feature_map.h:22
span class implementation, based on ISO++20 span<T>. The interface should be the same.
Definition: span.h:126
Registry entry for tree updater.
Definition: gbm.h:189
virtual void Configure(const std::vector< std::pair< std::string, std::string > > &cfg)=0
Set the configuration of gradient boosting. User must call configure once before InitModel and Traini...
~GradientBooster() override=default
virtual destructor
virtual bool AllowLazyCheckPoint() const
whether the model allow lazy checkpoint return true if model is only updated in DoBoost after all All...
Definition: gbm.h:68
virtual void PredictLeaf(DMatrix *dmat, std::vector< bst_float > *out_preds, unsigned ntree_limit=0)=0
predict the leaf index of each tree, the output will be nsample * ntree vector this is only valid in ...
virtual void Load(dmlc::Stream *fi)=0
load model from stream
namespace of xgboost
Definition: base.h:102
defines configuration macros of xgboost.
virtual bool UseGPU() const =0
Whether the current booster uses GPU.
virtual void Save(dmlc::Stream *fo) const =0
save model to stream.
virtual void PredictInstance(const SparsePage::Inst &inst, std::vector< bst_float > *out_preds, unsigned ntree_limit=0)=0
online prediction function, predict score for one instance at a time NOTE: use the batch prediction i...
virtual void DoBoost(DMatrix *p_fmat, HostDeviceVector< GradientPair > *in_gpair, PredictionCacheEntry *prediction)=0
perform update to the model(boosting)
interface of gradient boosting model.
Definition: gbm.h:39
Contains pointer to input matrix and associated cached predictions.
Definition: predictor.h:35
GenericParameter const * generic_param_
Definition: gbm.h:41