Mape Regression Loss.
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#include <regression_objective.hpp>
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| RegressionMAPELOSS (const Config &config) |
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| RegressionMAPELOSS (const std::vector< std::string > &strs) |
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void | Init (const Metadata &metadata, data_size_t num_data) override |
| Initialize.
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void | GetGradients (const double *score, score_t *gradients, score_t *hessians) const override |
| calculating first order derivative of loss function
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double | BoostFromScore (int) const override |
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bool | IsRenewTreeOutput () const override |
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double | RenewTreeOutput (double, const double *pred, const data_size_t *index_mapper, const data_size_t *bagging_mapper, data_size_t num_data_in_leaf) const override |
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double | RenewTreeOutput (double, double pred, const data_size_t *index_mapper, const data_size_t *bagging_mapper, data_size_t num_data_in_leaf) const override |
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const char * | GetName () const override |
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bool | IsConstantHessian () const override |
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| RegressionL1loss (const Config &config) |
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| RegressionL1loss (const std::vector< std::string > &strs) |
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| RegressionL2loss (const Config &config) |
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| RegressionL2loss (const std::vector< std::string > &strs) |
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void | ConvertOutput (const double *input, double *output) const override |
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std::string | ToString () const override |
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virtual | ~ObjectiveFunction () |
| virtual destructor
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virtual bool | ClassNeedTrain (int) const |
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virtual bool | SkipEmptyClass () const |
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virtual int | NumModelPerIteration () const |
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virtual int | NumPredictOneRow () const |
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virtual bool | NeedAccuratePrediction () const |
| The prediction should be accurate or not. True will disable early stopping for prediction.
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ObjectiveFunction & | operator= (const ObjectiveFunction &)=delete |
| Disable copy.
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| ObjectiveFunction (const ObjectiveFunction &)=delete |
| Disable copy.
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◆ BoostFromScore()
double LightGBM::RegressionMAPELOSS::BoostFromScore |
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int |
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const |
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inlineoverridevirtual |
◆ GetGradients()
void LightGBM::RegressionMAPELOSS::GetGradients |
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const double * |
score, |
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score_t * |
gradients, |
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score_t * |
hessians |
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| const |
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inlineoverridevirtual |
calculating first order derivative of loss function
- Parameters
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score | prediction score in this round \gradients Output gradients \hessians Output hessians |
Reimplemented from LightGBM::RegressionL1loss.
◆ GetName()
const char * LightGBM::RegressionMAPELOSS::GetName |
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const |
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inlineoverridevirtual |
◆ Init()
◆ IsConstantHessian()
bool LightGBM::RegressionMAPELOSS::IsConstantHessian |
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const |
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inlineoverridevirtual |
◆ IsRenewTreeOutput()
bool LightGBM::RegressionMAPELOSS::IsRenewTreeOutput |
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const |
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inlineoverridevirtual |
◆ RenewTreeOutput() [1/2]
double LightGBM::RegressionMAPELOSS::RenewTreeOutput |
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double |
, |
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const double * |
pred, |
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const data_size_t * |
index_mapper, |
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const data_size_t * |
bagging_mapper, |
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data_size_t |
num_data_in_leaf |
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| const |
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inlineoverridevirtual |
◆ RenewTreeOutput() [2/2]
double LightGBM::RegressionMAPELOSS::RenewTreeOutput |
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double |
, |
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double |
pred, |
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const data_size_t * |
index_mapper, |
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const data_size_t * |
bagging_mapper, |
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data_size_t |
num_data_in_leaf |
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) |
| const |
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inlineoverridevirtual |
The documentation for this class was generated from the following file: