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rank pass toy

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This commit is contained in:
kalenhaha
2014-04-07 23:25:35 +08:00
parent 40c380e40a
commit a10f594644
32 changed files with 2237 additions and 2146 deletions
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92
booster/xgboost.h
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@@ -19,8 +19,8 @@
namespace xgboost{
/*! \brief namespace for boosters */
namespace booster{
/*!
* \brief interface of a gradient boosting learner
/*!
* \brief interface of a gradient boosting learner
* \tparam FMatrix the feature matrix format that the booster takes
*/
template<typename FMatrix>
@@ -35,101 +35,101 @@ namespace xgboost{
// call booster->LoadModel
// (3) booster->DoBoost to update the model
// (4) booster->Predict to get new prediction
/*!
* \brief set parameters from outside
/*!
* \brief set parameters from outside
* \param name name of the parameter
* \param val value of the parameter
*/
virtual void SetParam( const char *name, const char *val ) = 0;
/*!
virtual void SetParam(const char *name, const char *val) = 0;
/*!
* \brief load model from stream
* \param fi input stream
*/
virtual void LoadModel( utils::IStream &fi ) = 0;
/*!
virtual void LoadModel(utils::IStream &fi) = 0;
/*!
* \brief save model to stream
* \param fo output stream
*/
virtual void SaveModel( utils::IStream &fo ) const = 0;
virtual void SaveModel(utils::IStream &fo) const = 0;
/*!
* \brief initialize solver before training, called before training
* this function is reserved for solver to allocate necessary space and do other preparation
* this function is reserved for solver to allocate necessary space and do other preparation
*/
virtual void InitModel( void ) = 0;
virtual void InitModel(void) = 0;
public:
/*!
* \brief do gradient boost training for one step, using the information given,
/*!
* \brief do gradient boost training for one step, using the information given,
* Note: content of grad and hess can change after DoBoost
* \param grad first order gradient of each instance
* \param hess second order gradient of each instance
* \param feats features of each instance
* \param root_index pre-partitioned root index of each instance,
* \param root_index pre-partitioned root index of each instance,
* root_index.size() can be 0 which indicates that no pre-partition involved
*/
virtual void DoBoost( std::vector<float> &grad,
std::vector<float> &hess,
const FMatrix &feats,
const std::vector<unsigned> &root_index ) = 0;
/*!
virtual void DoBoost(std::vector<float> &grad,
std::vector<float> &hess,
const FMatrix &feats,
const std::vector<unsigned> &root_index) = 0;
/*!
* \brief predict the path ids along a trees, for given sparse feature vector. When booster is a tree
* \param path the result of path
* \param feats feature matrix
* \param row_index row index in the feature matrix
* \param root_index root id of current instance, default = 0
*/
virtual void PredPath( std::vector<int> &path, const FMatrix &feats,
bst_uint row_index, unsigned root_index = 0 ){
utils::Error( "not implemented" );
virtual void PredPath(std::vector<int> &path, const FMatrix &feats,
bst_uint row_index, unsigned root_index = 0){
utils::Error("not implemented");
}
/*!
/*!
* \brief predict values for given sparse feature vector
*
*
* NOTE: in tree implementation, Sparse Predict is OpenMP threadsafe, but not threadsafe in general,
* dense version of Predict to ensures threadsafety
* \param feats feature matrix
* \param row_index row index in the feature matrix
* \param root_index root id of current instance, default = 0
* \return prediction
*/
virtual float Predict( const FMatrix &feats, bst_uint row_index, unsigned root_index = 0 ){
utils::Error( "not implemented" );
* \return prediction
*/
virtual float Predict(const FMatrix &feats, bst_uint row_index, unsigned root_index = 0){
utils::Error("not implemented");
return 0.0f;
}
/*!
/*!
* \brief predict values for given dense feature vector
* \param feat feature vector in dense format
* \param funknown indicator that the feature is missing
* \param rid root id of current instance, default = 0
* \return prediction
*/
virtual float Predict( const std::vector<float> &feat,
const std::vector<bool> &funknown,
unsigned rid = 0 ){
utils::Error( "not implemented" );
virtual float Predict(const std::vector<float> &feat,
const std::vector<bool> &funknown,
unsigned rid = 0){
utils::Error("not implemented");
return 0.0f;
}
/*!
/*!
* \brief print information
* \param fo output stream
*/
virtual void PrintInfo( FILE *fo ){}
/*!
* \param fo output stream
*/
virtual void PrintInfo(FILE *fo){}
/*!
* \brief dump model into text file
* \param fo output stream
* \param fo output stream
* \param fmap feature map that may help give interpretations of feature
* \param with_stats whether print statistics
*/
virtual void DumpModel( FILE *fo, const utils::FeatMap& fmap, bool with_stats = false ){
utils::Error( "not implemented" );
virtual void DumpModel(FILE *fo, const utils::FeatMap& fmap, bool with_stats = false){
utils::Error("not implemented");
}
public:
/*! \brief virtual destructor */
virtual ~InterfaceBooster( void ){}
virtual ~InterfaceBooster(void){}
};
};
namespace booster{
/*!
* \brief this will is the most commonly used booster interface
/*!
* \brief this will is the most commonly used booster interface
* we try to make booster invariant of data structures, but most cases, FMatrixS is what we wnat
*/
typedef InterfaceBooster<FMatrixS> IBooster;
@@ -138,7 +138,7 @@ namespace xgboost{
namespace xgboost{
namespace booster{
/*!
/*!
* \brief create a gradient booster, given type of booster
* normally we use FMatrixS, by calling CreateBooster<FMatrixS>
* \param booster_type type of gradient booster, can be used to specify implements
@@ -146,7 +146,7 @@ namespace xgboost{
* \return the pointer to the gradient booster created
*/
template<typename FMatrix>
inline InterfaceBooster<FMatrix> *CreateBooster( int booster_type );
inline InterfaceBooster<FMatrix> *CreateBooster(int booster_type);
};
};