8 #ifndef XGBOOST_LEARNER_H_
9 #define XGBOOST_LEARNER_H_
31 class GradientBooster;
35 struct XGBAPIThreadLocalEntry;
37 class HostDeviceVector;
79 virtual void UpdateOneIter(std::int32_t iter, std::shared_ptr<DMatrix> train) = 0;
89 virtual void BoostOneIter(std::int32_t iter, std::shared_ptr<DMatrix> train,
99 const std::vector<std::shared_ptr<DMatrix>>& data_sets,
100 const std::vector<std::string>& data_names) = 0;
114 virtual void Predict(std::shared_ptr<DMatrix> data,
bool output_margin,
116 bst_layer_t layer_end,
bool training =
false,
bool pred_leaf =
false,
117 bool pred_contribs =
false,
bool approx_contribs =
false,
118 bool pred_interactions =
false) = 0;
139 std::vector<bst_feature_t>* features,
140 std::vector<float>* scores) = 0;
149 virtual std::uint32_t
Groups()
const = 0;
171 virtual void SetParam(
const std::string& key,
const std::string& value) = 0;
187 virtual void SetAttr(
const std::string& key,
const std::string& value) = 0;
195 virtual bool GetAttr(
const std::string& key, std::string* out)
const = 0;
201 virtual bool DelAttr(
const std::string& key) = 0;
239 bool* out_of_bound) = 0;
249 std::string format) = 0;
257 static Learner*
Create(
const std::vector<std::shared_ptr<DMatrix> >& cache_data);
270 std::unique_ptr<ObjFunction>
obj_;
272 std::unique_ptr<GradientBooster>
gbm_;
279 struct LearnerModelParamLegacy;
327 : base_score_{
std::move(base_score)},
Defines configuration macros and basic types for xgboost.
Feature map data structure to help text model dump. TODO(tqchen) consider make it even more lightweig...
Definition: feature_map.h:22
Data structure representing JSON format.
Definition: json.h:368
Learner class that does training and prediction. This is the user facing module of xgboost training....
Definition: learner.h:65
virtual std::vector< std::string > GetAttrNames() const =0
Get a vector of attribute names from the booster.
virtual void UpdateOneIter(std::int32_t iter, std::shared_ptr< DMatrix > train)=0
update the model for one iteration With the specified objective function.
virtual void InplacePredict(std::shared_ptr< DMatrix > p_m, PredictionType type, float missing, HostDeviceVector< float > **out_preds, bst_layer_t layer_begin, bst_layer_t layer_end)=0
Inplace prediction.
virtual void SetParam(const std::string &key, const std::string &value)=0
Set parameter for booster.
virtual void LoadModel(dmlc::Stream *fi)=0
std::vector< std::unique_ptr< Metric > > metrics_
The evaluation metrics used to evaluate the model.
Definition: learner.h:274
virtual void CalcFeatureScore(std::string const &importance_type, common::Span< int32_t const > trees, std::vector< bst_feature_t > *features, std::vector< float > *scores)=0
Calculate feature score. See doc in C API for outputs.
virtual std::vector< std::string > DumpModel(const FeatureMap &fmap, bool with_stats, std::string format)=0
dump the model in the requested format
virtual std::string EvalOneIter(int iter, const std::vector< std::shared_ptr< DMatrix >> &data_sets, const std::vector< std::string > &data_names)=0
evaluate the model for specific iteration using the configured metrics.
~Learner() override
virtual destructor
virtual XGBAPIThreadLocalEntry & GetThreadLocal() const =0
virtual const std::map< std::string, std::string > & GetConfigurationArguments() const =0
Get configuration arguments currently stored by the learner.
virtual void SetFeatureNames(std::vector< std::string > const &fn)=0
Set the feature names for current booster.
virtual void SaveModel(dmlc::Stream *fo) const =0
virtual Context const * Ctx() const =0
Return the context object of this Booster.
virtual void Configure()=0
Configure Learner based on set parameters.
virtual int32_t BoostedRounds() const =0
virtual bool DelAttr(const std::string &key)=0
Delete an attribute from the booster.
virtual bool GetAttr(const std::string &key, std::string *out) const =0
Get attribute from the booster. The property will be saved along the booster.
virtual void SetParams(Args const &args)=0
Set multiple parameters at once.
virtual void SetAttr(const std::string &key, const std::string &value)=0
Set additional attribute to the Booster.
std::unique_ptr< ObjFunction > obj_
objective function
Definition: learner.h:270
std::unique_ptr< GradientBooster > gbm_
The gradient booster used by the model.
Definition: learner.h:272
virtual void GetFeatureNames(std::vector< std::string > *fn) const =0
Get the feature names for current booster.
void LoadModel(Json const &in) override=0
load the model from a JSON object
virtual void SetFeatureTypes(std::vector< std::string > const &ft)=0
Set the feature types for current booster.
void SaveModel(Json *out) const override=0
saves the model config to a JSON object
static Learner * Create(const std::vector< std::shared_ptr< DMatrix > > &cache_data)
Create a new instance of learner.
virtual Learner * Slice(bst_layer_t begin, bst_layer_t end, bst_layer_t step, bool *out_of_bound)=0
Slice the model.
Context ctx_
Training parameter.
Definition: learner.h:276
virtual std::uint32_t Groups() const =0
Get the number of output groups from the model.
virtual void BoostOneIter(std::int32_t iter, std::shared_ptr< DMatrix > train, linalg::Matrix< GradientPair > *in_gpair)=0
Do customized gradient boosting with in_gpair.
virtual void Predict(std::shared_ptr< DMatrix > data, bool output_margin, HostDeviceVector< bst_float > *out_preds, bst_layer_t layer_begin, bst_layer_t layer_end, bool training=false, bool pred_leaf=false, bool pred_contribs=false, bool approx_contribs=false, bool pred_interactions=false)=0
get prediction given the model.
virtual uint32_t GetNumFeature() const =0
Get the number of features of the booster.
virtual void GetFeatureTypes(std::vector< std::string > *ft) const =0
Get the feature types for current booster.
span class implementation, based on ISO++20 span<T>. The interface should be the same.
Definition: span.h:422
A tensor view with static type and dimension. It implements indexing and slicing.
Definition: linalg.h:294
A tensor storage. To use it for other functionality like slicing one needs to obtain a view first....
Definition: linalg.h:762
Linear algebra related utilities.
interface of evaluation metric function supported in xgboost.
Defines the abstract interface for different components in XGBoost.
Definition: intrusive_ptr.h:207
Core data structure for multi-target trees.
Definition: base.h:87
std::vector< std::pair< std::string, std::string > > Args
Definition: base.h:310
PredictionType
Definition: learner.h:39
std::uint32_t bst_target_t
Type for indexing into output targets.
Definition: base.h:113
std::int32_t bst_layer_t
Type for indexing boosted layers.
Definition: base.h:117
std::uint32_t bst_feature_t
Type for data column (feature) index.
Definition: base.h:97
MultiStrategy
Strategy for building multi-target models.
Definition: learner.h:284
Runtime context for XGBoost. Contains information like threads and device.
Definition: context.h:133
A type for device ordinal. The type is packed into 32-bit for efficient use in viewing types like lin...
Definition: context.h:34
Basic model parameters, used to describe the booster.
Definition: learner.h:292
std::uint32_t num_output_group
The number of classes or targets.
Definition: learner.h:308
LearnerModelParam()=default
LearnerModelParam(Context const *ctx, LearnerModelParamLegacy const &user_param, linalg::Tensor< float, 1 > base_margin, ObjInfo t, MultiStrategy multi_strategy)
bool Initialized() const
Definition: learner.h:345
LearnerModelParam(bst_feature_t n_features, linalg::Tensor< float, 1 > base_score, std::uint32_t n_groups, bst_target_t n_targets, MultiStrategy multi_strategy)
Definition: learner.h:325
MultiStrategy multi_strategy
Strategy for building multi-target models.
Definition: learner.h:316
linalg::TensorView< float const, 1 > BaseScore(DeviceOrd device) const
bst_target_t OutputLength() const noexcept
Definition: learner.h:339
LearnerModelParam(LearnerModelParamLegacy const &user_param, ObjInfo t, MultiStrategy multi_strategy)
void Copy(LearnerModelParam const &that)
linalg::TensorView< float const, 1 > BaseScore(Context const *ctx) const
bst_target_t LeafLength() const noexcept
Definition: learner.h:340
ObjInfo task
Current task, determined by objective.
Definition: learner.h:312
bool IsVectorLeaf() const noexcept
Definition: learner.h:336
bst_feature_t num_feature
The number of features.
Definition: learner.h:304
A struct returned by objective, which determines task at hand. The struct is not used by any algorith...
Definition: task.h:24
@ kRegression
Definition: task.h:27