xgboost
objective.h
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1 
7 #ifndef XGBOOST_OBJECTIVE_H_
8 #define XGBOOST_OBJECTIVE_H_
9 
10 #include <dmlc/registry.h>
11 #include <xgboost/base.h>
12 #include <xgboost/data.h>
14 #include <xgboost/model.h>
15 #include <xgboost/task.h>
16 
17 #include <cstdint> // std::int32_t
18 #include <functional>
19 #include <string>
20 
21 namespace xgboost {
22 
23 class RegTree;
24 struct Context;
25 
27 class ObjFunction : public Configurable {
28  protected:
29  Context const* ctx_;
30 
31  public:
32  static constexpr float DefaultBaseScore() { return 0.5f; }
33 
34  public:
36  ~ObjFunction() override = default;
41  virtual void Configure(Args const& args) = 0;
50  virtual void GetGradient(HostDeviceVector<float> const& preds, MetaInfo const& info,
51  std::int32_t iter, linalg::Matrix<GradientPair>* out_gpair) = 0;
52 
54  virtual const char* DefaultEvalMetric() const = 0;
58  virtual Json DefaultMetricConfig() const { return Json{Null{}}; }
59 
60  // the following functions are optional, most of time default implementation is good enough
68  virtual void PredTransform(HostDeviceVector<float>*) const {}
76  virtual void EvalTransform(HostDeviceVector<float>* io_preds) { this->PredTransform(io_preds); }
85  [[nodiscard]] virtual float ProbToMargin(float base_score) const { return base_score; }
94  virtual void InitEstimation(MetaInfo const& info, linalg::Tensor<float, 1>* base_score) const;
98  [[nodiscard]] virtual struct ObjInfo Task() const = 0;
103  [[nodiscard]] virtual bst_target_t Targets(MetaInfo const& info) const {
104  if (info.labels.Shape(1) > 1) {
105  LOG(FATAL) << "multioutput is not supported by the current objective function";
106  }
107  return 1;
108  }
109 
125  virtual void UpdateTreeLeaf(HostDeviceVector<bst_node_t> const& /*position*/,
126  MetaInfo const& /*info*/, float /*learning_rate*/,
127  HostDeviceVector<float> const& /*prediction*/,
128  std::int32_t /*group_idx*/, RegTree* /*p_tree*/) const {}
129 
135  static ObjFunction* Create(const std::string& name, Context const* ctx);
136 };
137 
142  : public dmlc::FunctionRegEntryBase<ObjFunctionReg,
143  std::function<ObjFunction* ()> > {
144 };
145 
158 #define XGBOOST_REGISTER_OBJECTIVE(UniqueId, Name) \
159  static DMLC_ATTRIBUTE_UNUSED ::xgboost::ObjFunctionReg & \
160  __make_ ## ObjFunctionReg ## _ ## UniqueId ## __ = \
161  ::dmlc::Registry< ::xgboost::ObjFunctionReg>::Get()->__REGISTER__(Name)
162 } // namespace xgboost
163 #endif // XGBOOST_OBJECTIVE_H_
Defines configuration macros and basic types for xgboost.
Definition: json.h:319
Data structure representing JSON format.
Definition: json.h:378
Meta information about dataset, always sit in memory.
Definition: data.h:48
linalg::Tensor< float, 2 > labels
label of each instance
Definition: data.h:60
interface of objective function
Definition: objective.h:27
virtual void EvalTransform(HostDeviceVector< float > *io_preds)
Apply inverse link (activation) function to prediction values.
Definition: objective.h:76
static constexpr float DefaultBaseScore()
Definition: objective.h:32
virtual void GetGradient(HostDeviceVector< float > const &preds, MetaInfo const &info, std::int32_t iter, linalg::Matrix< GradientPair > *out_gpair)=0
Get gradient over each of predictions, given existing information.
virtual void Configure(Args const &args)=0
Configure the objective with the specified parameters.
Context const * ctx_
Definition: objective.h:29
virtual void PredTransform(HostDeviceVector< float > *) const
Apply inverse link (activation) function to prediction values.
Definition: objective.h:68
static ObjFunction * Create(const std::string &name, Context const *ctx)
Create an objective function according to name.
virtual void InitEstimation(MetaInfo const &info, linalg::Tensor< float, 1 > *base_score) const
Obtain the initial estimation of prediction.
virtual Json DefaultMetricConfig() const
Return the configuration for the default metric.
Definition: objective.h:58
virtual struct ObjInfo Task() const =0
Return task of this objective.
~ObjFunction() override=default
virtual destructor
virtual void UpdateTreeLeaf(HostDeviceVector< bst_node_t > const &, MetaInfo const &, float, HostDeviceVector< float > const &, std::int32_t, RegTree *) const
Update the leaf values after a tree is built. Needed for objectives with 0 hessian.
Definition: objective.h:125
virtual const char * DefaultEvalMetric() const =0
virtual bst_target_t Targets(MetaInfo const &info) const
Return number of targets for input matrix. Right now XGBoost supports only multi-target regression.
Definition: objective.h:103
virtual float ProbToMargin(float base_score) const
Apply link function to the intercept.
Definition: objective.h:85
define regression tree to be the most common tree model.
Definition: tree_model.h:157
A tensor storage. To use it for other functionality like slicing one needs to obtain a view first....
Definition: linalg.h:762
auto Shape() const
Definition: linalg.h:884
The input data structure of xgboost.
A device-and-host vector abstraction layer.
Defines the abstract interface for different components in XGBoost.
Core data structure for multi-target trees.
Definition: base.h:89
std::vector< std::pair< std::string, std::string > > Args
Definition: base.h:316
std::uint32_t bst_target_t
Type for indexing into output targets.
Definition: base.h:119
Definition: model.h:31
Runtime context for XGBoost. Contains information like threads and device.
Definition: context.h:133
Registry entry for objective factory functions.
Definition: objective.h:143
A struct returned by objective, which determines task at hand. The struct is not used by any algorith...
Definition: task.h:24