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>
13 #include <xgboost/model.h>
16 #include <xgboost/task.h>
17 
18 #include <vector>
19 #include <utility>
20 #include <string>
21 #include <functional>
22 
23 namespace xgboost {
24 
25 class RegTree;
26 
28 class ObjFunction : public Configurable {
29  protected:
30  Context const* ctx_;
31 
32  public:
33  static constexpr float DefaultBaseScore() { return 0.5f; }
34 
35  public:
37  ~ObjFunction() override = default;
42  virtual void Configure(const std::vector<std::pair<std::string, std::string> >& args) = 0;
50  virtual void GetGradient(const HostDeviceVector<bst_float>& preds,
51  const MetaInfo& info,
52  int iteration,
53  HostDeviceVector<GradientPair>* out_gpair) = 0;
54 
56  virtual const char* DefaultEvalMetric() const = 0;
57  // the following functions are optional, most of time default implementation is good enough
63 
69  virtual void EvalTransform(HostDeviceVector<bst_float> *io_preds) {
70  this->PredTransform(io_preds);
71  }
78  virtual bst_float ProbToMargin(bst_float base_score) const {
79  return base_score;
80  }
87  virtual void InitEstimation(MetaInfo const& info, linalg::Tensor<float, 1>* base_score) const;
91  virtual struct ObjInfo Task() const = 0;
96  virtual uint32_t Targets(MetaInfo const& info) const {
97  if (info.labels.Shape(1) > 1) {
98  LOG(FATAL) << "multioutput is not supported by current objective function";
99  }
100  return 1;
101  }
102 
116  virtual void UpdateTreeLeaf(HostDeviceVector<bst_node_t> const& /*position*/,
117  MetaInfo const& /*info*/,
118  HostDeviceVector<float> const& /*prediction*/,
119  RegTree* /*p_tree*/) const {}
120 
126  static ObjFunction* Create(const std::string& name, GenericParameter const* tparam);
127 };
128 
133  : public dmlc::FunctionRegEntryBase<ObjFunctionReg,
134  std::function<ObjFunction* ()> > {
135 };
136 
149 #define XGBOOST_REGISTER_OBJECTIVE(UniqueId, Name) \
150  static DMLC_ATTRIBUTE_UNUSED ::xgboost::ObjFunctionReg & \
151  __make_ ## ObjFunctionReg ## _ ## UniqueId ## __ = \
152  ::dmlc::Registry< ::xgboost::ObjFunctionReg>::Get()->__REGISTER__(Name)
153 } // namespace xgboost
154 #endif // XGBOOST_OBJECTIVE_H_
defines configuration macros of xgboost.
Meta information about dataset, always sit in memory.
Definition: data.h:46
linalg::Tensor< float, 2 > labels
label of each instance
Definition: data.h:58
interface of objective function
Definition: objective.h:28
virtual void PredTransform(HostDeviceVector< bst_float > *) const
transform prediction values, this is only called when Prediction is called
Definition: objective.h:62
virtual bst_float ProbToMargin(bst_float base_score) const
transform probability value back to margin this is used to transform user-set base_score back to marg...
Definition: objective.h:78
virtual void UpdateTreeLeaf(HostDeviceVector< bst_node_t > const &, MetaInfo const &, HostDeviceVector< float > const &, RegTree *) const
Update the leaf values after a tree is built. Needed for objectives with 0 hessian.
Definition: objective.h:116
virtual void GetGradient(const HostDeviceVector< bst_float > &preds, const MetaInfo &info, int iteration, HostDeviceVector< GradientPair > *out_gpair)=0
Get gradient over each of predictions, given existing information.
static constexpr float DefaultBaseScore()
Definition: objective.h:33
virtual void EvalTransform(HostDeviceVector< bst_float > *io_preds)
transform prediction values, this is only called when Eval is called, usually it redirect to PredTran...
Definition: objective.h:69
virtual uint32_t Targets(MetaInfo const &info) const
Return number of targets for input matrix. Right now XGBoost supports only multi-target regression.
Definition: objective.h:96
Context const * ctx_
Definition: objective.h:30
virtual void Configure(const std::vector< std::pair< std::string, std::string > > &args)=0
Configure the objective with the specified parameters.
virtual void InitEstimation(MetaInfo const &info, linalg::Tensor< float, 1 > *base_score) const
Make initialize estimation of prediction.
virtual struct ObjInfo Task() const =0
Return task of this objective.
~ObjFunction() override=default
virtual destructor
virtual const char * DefaultEvalMetric() const =0
static ObjFunction * Create(const std::string &name, GenericParameter const *tparam)
Create an objective function according to name.
define regression tree to be the most common tree model. This is the data structure used in xgboost's...
Definition: tree_model.h:131
auto Shape() const
Definition: linalg.h:775
The input data structure of xgboost.
A device-and-host vector abstraction layer.
Defines the abstract interface for different components in XGBoost.
namespace of xgboost
Definition: base.h:110
float bst_float
float type, used for storing statistics
Definition: base.h:119
Definition: model.h:31
Definition: generic_parameters.h:15
Registry entry for objective factory functions.
Definition: objective.h:134
A struct returned by objective, which determines task at hand. The struct is not used by any algorith...
Definition: task.h:24