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kalenhaha
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a20b1d1866
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a0dddaf224
@ -18,8 +18,6 @@ booster_type=1
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do_reboost=0
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bst:num_roots=0
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bst:num_feature=3
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learning_rate=0.01
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@ -12,267 +12,267 @@
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#include "../utils/xgboost_stream.h"
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namespace xgboost{
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namespace regression{
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/*! \brief class for gradient boosted regression */
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class RegBoostLearner{
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public:
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namespace regression{
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/*! \brief class for gradient boosted regression */
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class RegBoostLearner{
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public:
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RegBoostLearner(bool silent = false){
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this->silent = silent;
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}
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RegBoostLearner(bool silent = false){
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this->silent = silent;
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}
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/*!
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* \brief a regression booter associated with training and evaluating data
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* \param train pointer to the training data
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* \param evals array of evaluating data
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* \param evname name of evaluation data, used print statistics
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*/
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RegBoostLearner( const DMatrix *train,
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std::vector<const DMatrix *> evals,
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std::vector<std::string> evname, bool silent = false ){
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this->silent = silent;
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SetData(train,evals,evname);
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}
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/*!
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* \brief a regression booter associated with training and evaluating data
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* \param train pointer to the training data
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* \param evals array of evaluating data
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* \param evname name of evaluation data, used print statistics
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*/
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RegBoostLearner( const DMatrix *train,
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std::vector<const DMatrix *> evals,
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std::vector<std::string> evname, bool silent = false ){
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this->silent = silent;
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SetData(train,evals,evname);
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}
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/*!
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* \brief associate regression booster with training and evaluating data
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* \param train pointer to the training data
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* \param evals array of evaluating data
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* \param evname name of evaluation data, used print statistics
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*/
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inline void SetData(const DMatrix *train,
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std::vector<const DMatrix *> evals,
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std::vector<std::string> evname){
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this->train_ = train;
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this->evals_ = evals;
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this->evname_ = evname;
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//assign buffer index
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int buffer_size = (*train).size();
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for(int i = 0; i < evals.size(); i++){
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buffer_size += (*evals[i]).size();
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}
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char str[25];
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_itoa(buffer_size,str,10);
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base_model.SetParam("num_pbuffer",str);
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base_model.SetParam("num_pbuffer",str);
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}
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/*!
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* \brief associate regression booster with training and evaluating data
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* \param train pointer to the training data
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* \param evals array of evaluating data
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* \param evname name of evaluation data, used print statistics
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*/
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inline void SetData(const DMatrix *train,
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std::vector<const DMatrix *> evals,
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std::vector<std::string> evname){
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this->train_ = train;
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this->evals_ = evals;
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this->evname_ = evname;
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//assign buffer index
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int buffer_size = (*train).size();
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for(int i = 0; i < evals.size(); i++){
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buffer_size += (*evals[i]).size();
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}
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char str[25];
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_itoa(buffer_size,str,10);
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base_model.SetParam("num_pbuffer",str);
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base_model.SetParam("num_pbuffer",str);
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}
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/*!
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* \brief set parameters from outside
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* \param name name of the parameter
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* \param val value of the parameter
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*/
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inline void SetParam( const char *name, const char *val ){
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mparam.SetParam( name, val );
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base_model.SetParam( name, val );
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}
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/*!
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* \brief initialize solver before training, called before training
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* this function is reserved for solver to allocate necessary space and do other preparation
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*/
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inline void InitTrainer( void ){
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base_model.InitTrainer();
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InitModel();
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mparam.AdjustBase();
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}
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/*!
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* \brief set parameters from outside
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* \param name name of the parameter
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* \param val value of the parameter
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*/
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inline void SetParam( const char *name, const char *val ){
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mparam.SetParam( name, val );
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base_model.SetParam( name, val );
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}
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/*!
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* \brief initialize solver before training, called before training
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* this function is reserved for solver to allocate necessary space and do other preparation
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*/
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inline void InitTrainer( void ){
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base_model.InitTrainer();
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InitModel();
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mparam.AdjustBase();
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}
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/*!
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* \brief initialize the current data storage for model, if the model is used first time, call this function
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*/
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inline void InitModel( void ){
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base_model.InitModel();
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}
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/*!
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* \brief initialize the current data storage for model, if the model is used first time, call this function
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*/
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inline void InitModel( void ){
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base_model.InitModel();
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}
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/*!
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* \brief load model from stream
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* \param fi input stream
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*/
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inline void LoadModel( utils::IStream &fi ){
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utils::Assert( fi.Read( &mparam, sizeof(ModelParam) ) != 0 );
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base_model.LoadModel( fi );
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}
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/*!
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* \brief save model to stream
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* \param fo output stream
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*/
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inline void SaveModel( utils::IStream &fo ) const{
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fo.Write( &mparam, sizeof(ModelParam) );
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base_model.SaveModel( fo );
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}
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/*!
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* \brief update the model for one iteration
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* \param iteration the number of updating iteration
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*/
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inline void UpdateOneIter( int iteration ){
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std::vector<float> grad,hess,preds;
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std::vector<unsigned> root_index;
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booster::FMatrixS::Image train_image((*train_).data);
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Predict(preds,*train_,0);
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Gradient(preds,(*train_).labels,grad,hess);
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base_model.DoBoost(grad,hess,train_image,root_index);
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int buffer_index_offset = (*train_).size();
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float loss = 0.0;
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for(int i = 0; i < evals_.size();i++){
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Predict(preds, *evals_[i], buffer_index_offset);
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loss = mparam.Loss(preds,(*evals_[i]).labels);
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if(!silent){
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printf("The loss of %s data set in %d the \
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iteration is %f",evname_[i].c_str(),&iteration,&loss);
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}
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buffer_index_offset += (*evals_[i]).size();
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}
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}
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/*!
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* \brief load model from stream
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* \param fi input stream
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*/
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inline void LoadModel( utils::IStream &fi ){
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utils::Assert( fi.Read( &mparam, sizeof(ModelParam) ) != 0 );
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base_model.LoadModel( fi );
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}
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/*!
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* \brief save model to stream
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* \param fo output stream
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*/
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inline void SaveModel( utils::IStream &fo ) const{
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fo.Write( &mparam, sizeof(ModelParam) );
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base_model.SaveModel( fo );
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}
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/*! \brief get the transformed predictions, given data */
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inline void Predict( std::vector<float> &preds, const DMatrix &data,int buffer_index_offset = 0 ){
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int data_size = data.size();
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preds.resize(data_size);
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for(int j = 0; j < data_size; j++){
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preds[j] = mparam.PredTransform(mparam.base_score +
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base_model.Predict(data.data[j],buffer_index_offset + j));
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}
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}
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/*!
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* \brief update the model for one iteration
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* \param iteration the number of updating iteration
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*/
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inline void UpdateOneIter( int iteration ){
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std::vector<float> grad,hess,preds;
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std::vector<unsigned> root_index;
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booster::FMatrixS::Image train_image((*train_).data);
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Predict(preds,*train_,0);
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Gradient(preds,(*train_).labels,grad,hess);
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base_model.DoBoost(grad,hess,train_image,root_index);
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int buffer_index_offset = (*train_).size();
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float loss = 0.0;
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for(int i = 0; i < evals_.size();i++){
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Predict(preds, *evals_[i], buffer_index_offset);
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loss = mparam.Loss(preds,(*evals_[i]).labels);
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if(!silent){
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printf("The loss of %s data set in %d the \
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iteration is %f",evname_[i].c_str(),&iteration,&loss);
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}
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buffer_index_offset += (*evals_[i]).size();
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}
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private:
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/*! \brief get the first order and second order gradient, given the transformed predictions and labels*/
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inline void Gradient(const std::vector<float> &preds, const std::vector<float> &labels, std::vector<float> &grad,
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std::vector<float> &hess){
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grad.clear();
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hess.clear();
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for(int j = 0; j < preds.size(); j++){
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grad.push_back(mparam.FirstOrderGradient(preds[j],labels[j]));
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hess.push_back(mparam.SecondOrderGradient(preds[j],labels[j]));
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}
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}
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}
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enum LOSS_TYPE_LIST{
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LINEAR_SQUARE,
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LOGISTIC_NEGLOGLIKELIHOOD,
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};
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/*! \brief get the transformed predictions, given data */
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inline void Predict( std::vector<float> &preds, const DMatrix &data,int buffer_index_offset = 0 ){
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int data_size = data.size();
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preds.resize(data_size);
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for(int j = 0; j < data_size; j++){
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preds[j] = mparam.PredTransform(mparam.base_score +
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base_model.Predict(data.data[j],buffer_index_offset + j));
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}
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}
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/*! \brief training parameter for regression */
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struct ModelParam{
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/* \brief global bias */
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float base_score;
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/* \brief type of loss function */
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int loss_type;
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ModelParam( void ){
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base_score = 0.5f;
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loss_type = 0;
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}
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/*!
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* \brief set parameters from outside
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* \param name name of the parameter
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* \param val value of the parameter
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*/
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inline void SetParam( const char *name, const char *val ){
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if( !strcmp("base_score", name ) ) base_score = (float)atof( val );
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if( !strcmp("loss_type", name ) ) loss_type = atoi( val );
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}
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/*!
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* \brief adjust base_score
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*/
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inline void AdjustBase( void ){
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if( loss_type == 1 ){
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utils::Assert( base_score > 0.0f && base_score < 1.0f, "sigmoid range constrain" );
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base_score = - logf( 1.0f / base_score - 1.0f );
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}
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}
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/*!
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* \brief calculate first order gradient of loss, given transformed prediction
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* \param predt transformed prediction
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* \param label true label
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* \return first order gradient
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*/
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inline float FirstOrderGradient( float predt, float label ) const{
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switch( loss_type ){
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case LINEAR_SQUARE: return predt - label;
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case 1: return predt - label;
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default: utils::Error("unknown loss_type"); return 0.0f;
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}
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}
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/*!
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* \brief calculate second order gradient of loss, given transformed prediction
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* \param predt transformed prediction
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* \param label true label
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* \return second order gradient
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*/
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inline float SecondOrderGradient( float predt, float label ) const{
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switch( loss_type ){
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case LINEAR_SQUARE: return 1.0f;
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case LOGISTIC_NEGLOGLIKELIHOOD: return predt * ( 1 - predt );
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default: utils::Error("unknown loss_type"); return 0.0f;
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}
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}
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private:
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/*! \brief get the first order and second order gradient, given the transformed predictions and labels*/
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inline void Gradient(const std::vector<float> &preds, const std::vector<float> &labels, std::vector<float> &grad,
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std::vector<float> &hess){
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grad.clear();
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hess.clear();
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for(int j = 0; j < preds.size(); j++){
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grad.push_back(mparam.FirstOrderGradient(preds[j],labels[j]));
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hess.push_back(mparam.SecondOrderGradient(preds[j],labels[j]));
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}
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}
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/*!
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* \brief calculating the loss, given the predictions, labels and the loss type
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* \param preds the given predictions
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* \param labels the given labels
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* \return the specified loss
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*/
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inline float Loss(const std::vector<float> &preds, const std::vector<float> &labels) const{
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switch( loss_type ){
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case LINEAR_SQUARE: return SquareLoss(preds,labels);
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case LOGISTIC_NEGLOGLIKELIHOOD: return NegLoglikelihoodLoss(preds,labels);
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default: utils::Error("unknown loss_type"); return 0.0f;
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}
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}
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enum LOSS_TYPE_LIST{
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LINEAR_SQUARE,
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LOGISTIC_NEGLOGLIKELIHOOD,
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};
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/*!
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* \brief calculating the square loss, given the predictions and labels
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* \param preds the given predictions
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* \param labels the given labels
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* \return the summation of square loss
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*/
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inline float SquareLoss(const std::vector<float> &preds, const std::vector<float> &labels) const{
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float ans = 0.0;
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for(int i = 0; i < preds.size(); i++)
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ans += pow(preds[i] - labels[i], 2);
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return ans;
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}
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/*! \brief training parameter for regression */
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struct ModelParam{
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/* \brief global bias */
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float base_score;
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/* \brief type of loss function */
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int loss_type;
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/*!
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* \brief calculating the square loss, given the predictions and labels
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* \param preds the given predictions
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* \param labels the given labels
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* \return the summation of square loss
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*/
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inline float NegLoglikelihoodLoss(const std::vector<float> &preds, const std::vector<float> &labels) const{
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float ans = 0.0;
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for(int i = 0; i < preds.size(); i++)
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ans -= labels[i] * log(preds[i]) + ( 1 - labels[i] ) * log(1 - preds[i]);
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return ans;
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}
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ModelParam( void ){
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base_score = 0.5f;
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loss_type = 0;
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}
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/*!
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* \brief set parameters from outside
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* \param name name of the parameter
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* \param val value of the parameter
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*/
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inline void SetParam( const char *name, const char *val ){
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if( !strcmp("base_score", name ) ) base_score = (float)atof( val );
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if( !strcmp("loss_type", name ) ) loss_type = atoi( val );
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}
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/*!
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* \brief adjust base_score
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*/
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inline void AdjustBase( void ){
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if( loss_type == 1 ){
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utils::Assert( base_score > 0.0f && base_score < 1.0f, "sigmoid range constrain" );
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base_score = - logf( 1.0f / base_score - 1.0f );
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}
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}
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/*!
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* \brief calculate first order gradient of loss, given transformed prediction
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* \param predt transformed prediction
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* \param label true label
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* \return first order gradient
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*/
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inline float FirstOrderGradient( float predt, float label ) const{
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switch( loss_type ){
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case LINEAR_SQUARE: return predt - label;
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case 1: return predt - label;
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default: utils::Error("unknown loss_type"); return 0.0f;
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}
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}
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/*!
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* \brief calculate second order gradient of loss, given transformed prediction
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* \param predt transformed prediction
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* \param label true label
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* \return second order gradient
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*/
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inline float SecondOrderGradient( float predt, float label ) const{
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switch( loss_type ){
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case LINEAR_SQUARE: return 1.0f;
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case LOGISTIC_NEGLOGLIKELIHOOD: return predt * ( 1 - predt );
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default: utils::Error("unknown loss_type"); return 0.0f;
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}
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}
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/*!
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* \brief transform the linear sum to prediction
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* \param x linear sum of boosting ensemble
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* \return transformed prediction
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*/
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inline float PredTransform( float x ){
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switch( loss_type ){
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case LINEAR_SQUARE: return x;
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case LOGISTIC_NEGLOGLIKELIHOOD: return 1.0f/(1.0f + expf(-x));
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default: utils::Error("unknown loss_type"); return 0.0f;
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}
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}
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/*!
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* \brief calculating the loss, given the predictions, labels and the loss type
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* \param preds the given predictions
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* \param labels the given labels
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* \return the specified loss
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*/
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inline float Loss(const std::vector<float> &preds, const std::vector<float> &labels) const{
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switch( loss_type ){
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case LINEAR_SQUARE: return SquareLoss(preds,labels);
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case LOGISTIC_NEGLOGLIKELIHOOD: return NegLoglikelihoodLoss(preds,labels);
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default: utils::Error("unknown loss_type"); return 0.0f;
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}
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}
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};
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private:
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booster::GBMBaseModel base_model;
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ModelParam mparam;
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const DMatrix *train_;
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std::vector<const DMatrix *> evals_;
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std::vector<std::string> evname_;
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bool silent;
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};
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}
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/*!
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* \brief calculating the square loss, given the predictions and labels
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* \param preds the given predictions
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* \param labels the given labels
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* \return the summation of square loss
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*/
|
||||
inline float SquareLoss(const std::vector<float> &preds, const std::vector<float> &labels) const{
|
||||
float ans = 0.0;
|
||||
for(int i = 0; i < preds.size(); i++)
|
||||
ans += pow(preds[i] - labels[i], 2);
|
||||
return ans;
|
||||
}
|
||||
|
||||
/*!
|
||||
* \brief calculating the square loss, given the predictions and labels
|
||||
* \param preds the given predictions
|
||||
* \param labels the given labels
|
||||
* \return the summation of square loss
|
||||
*/
|
||||
inline float NegLoglikelihoodLoss(const std::vector<float> &preds, const std::vector<float> &labels) const{
|
||||
float ans = 0.0;
|
||||
for(int i = 0; i < preds.size(); i++)
|
||||
ans -= labels[i] * log(preds[i]) + ( 1 - labels[i] ) * log(1 - preds[i]);
|
||||
return ans;
|
||||
}
|
||||
|
||||
|
||||
/*!
|
||||
* \brief transform the linear sum to prediction
|
||||
* \param x linear sum of boosting ensemble
|
||||
* \return transformed prediction
|
||||
*/
|
||||
inline float PredTransform( float x ){
|
||||
switch( loss_type ){
|
||||
case LINEAR_SQUARE: return x;
|
||||
case LOGISTIC_NEGLOGLIKELIHOOD: return 1.0f/(1.0f + expf(-x));
|
||||
default: utils::Error("unknown loss_type"); return 0.0f;
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
};
|
||||
private:
|
||||
booster::GBMBaseModel base_model;
|
||||
ModelParam mparam;
|
||||
const DMatrix *train_;
|
||||
std::vector<const DMatrix *> evals_;
|
||||
std::vector<std::string> evname_;
|
||||
bool silent;
|
||||
};
|
||||
}
|
||||
};
|
||||
|
||||
#endif
|
||||
|
||||
@ -3,13 +3,13 @@
|
||||
using namespace xgboost::regression;
|
||||
|
||||
int main(int argc, char *argv[]){
|
||||
// char* config_path = argv[1];
|
||||
// bool silent = ( atoi(argv[2]) == 1 );
|
||||
char* config_path = "c:\\cygwin64\\home\\chen\\github\\xgboost\\demo\\regression\\reg.conf";
|
||||
bool silent = false;
|
||||
RegBoostTrain train;
|
||||
train.train(config_path,false);
|
||||
//char* config_path = argv[1];
|
||||
//bool silent = ( atoi(argv[2]) == 1 );
|
||||
char* config_path = "c:\\cygwin64\\home\\chen\\github\\xgboost\\demo\\regression\\reg.conf";
|
||||
bool silent = false;
|
||||
RegBoostTrain train;
|
||||
train.train(config_path,false);
|
||||
|
||||
RegBoostTest test;
|
||||
test.test(config_path,false);
|
||||
RegBoostTest test;
|
||||
test.test(config_path,false);
|
||||
}
|
||||
@ -11,89 +11,89 @@
|
||||
|
||||
using namespace xgboost::utils;
|
||||
namespace xgboost{
|
||||
namespace regression{
|
||||
/*!
|
||||
* \brief wrapping the testing process of the gradient
|
||||
boosting regression model,given the configuation
|
||||
* \author Kailong Chen: chenkl198812@gmail.com
|
||||
*/
|
||||
class RegBoostTest{
|
||||
public:
|
||||
/*!
|
||||
* \brief to start the testing process of gradient boosting regression
|
||||
* model given the configuation, and finally save the prediction
|
||||
* results to the specified paths.
|
||||
* \param config_path the location of the configuration
|
||||
* \param silent whether to print feedback messages
|
||||
*/
|
||||
void test(char* config_path,bool silent = false){
|
||||
reg_boost_learner = new xgboost::regression::RegBoostLearner(silent);
|
||||
ConfigIterator config_itr(config_path);
|
||||
//Get the training data and validation data paths, config the Learner
|
||||
while (config_itr.Next()){
|
||||
reg_boost_learner->SetParam(config_itr.name(),config_itr.val());
|
||||
test_param.SetParam(config_itr.name(),config_itr.val());
|
||||
}
|
||||
namespace regression{
|
||||
/*!
|
||||
* \brief wrapping the testing process of the gradient
|
||||
boosting regression model,given the configuation
|
||||
* \author Kailong Chen: chenkl198812@gmail.com
|
||||
*/
|
||||
class RegBoostTest{
|
||||
public:
|
||||
/*!
|
||||
* \brief to start the testing process of gradient boosting regression
|
||||
* model given the configuation, and finally save the prediction
|
||||
* results to the specified paths.
|
||||
* \param config_path the location of the configuration
|
||||
* \param silent whether to print feedback messages
|
||||
*/
|
||||
void test(char* config_path,bool silent = false){
|
||||
reg_boost_learner = new xgboost::regression::RegBoostLearner(silent);
|
||||
ConfigIterator config_itr(config_path);
|
||||
//Get the training data and validation data paths, config the Learner
|
||||
while (config_itr.Next()){
|
||||
reg_boost_learner->SetParam(config_itr.name(),config_itr.val());
|
||||
test_param.SetParam(config_itr.name(),config_itr.val());
|
||||
}
|
||||
|
||||
Assert(test_param.test_paths.size() == test_param.test_names.size(),
|
||||
"The number of test data set paths is not the same as the number of test data set data set names");
|
||||
Assert(test_param.test_paths.size() == test_param.test_names.size(),
|
||||
"The number of test data set paths is not the same as the number of test data set data set names");
|
||||
|
||||
//begin testing
|
||||
reg_boost_learner->InitModel();
|
||||
char model_path[256];
|
||||
std::vector<float> preds;
|
||||
for(int i = 0; i < test_param.test_paths.size(); i++){
|
||||
xgboost::regression::DMatrix test_data;
|
||||
test_data.LoadText(test_param.test_paths[i].c_str());
|
||||
sprintf(model_path,"%s/final.model",test_param.model_dir_path);
|
||||
FileStream fin(fopen(model_path,"r"));
|
||||
reg_boost_learner->LoadModel(fin);
|
||||
fin.Close();
|
||||
reg_boost_learner->Predict(preds,test_data);
|
||||
}
|
||||
}
|
||||
//begin testing
|
||||
reg_boost_learner->InitModel();
|
||||
char model_path[256];
|
||||
std::vector<float> preds;
|
||||
for(int i = 0; i < test_param.test_paths.size(); i++){
|
||||
xgboost::regression::DMatrix test_data;
|
||||
test_data.LoadText(test_param.test_paths[i].c_str());
|
||||
sprintf(model_path,"%s/final.model",test_param.model_dir_path);
|
||||
FileStream fin(fopen(model_path,"r"));
|
||||
reg_boost_learner->LoadModel(fin);
|
||||
fin.Close();
|
||||
reg_boost_learner->Predict(preds,test_data);
|
||||
}
|
||||
}
|
||||
|
||||
private:
|
||||
struct TestParam{
|
||||
/* \brief upperbound of the number of boosters */
|
||||
int boost_iterations;
|
||||
private:
|
||||
struct TestParam{
|
||||
/* \brief upperbound of the number of boosters */
|
||||
int boost_iterations;
|
||||
|
||||
/* \brief the period to save the model, -1 means only save the final round model */
|
||||
int save_period;
|
||||
/* \brief the period to save the model, -1 means only save the final round model */
|
||||
int save_period;
|
||||
|
||||
/* \brief the path of directory containing the saved models */
|
||||
char model_dir_path[256];
|
||||
/* \brief the path of directory containing the saved models */
|
||||
char model_dir_path[256];
|
||||
|
||||
/* \brief the path of directory containing the output prediction results */
|
||||
char pred_dir_path[256];
|
||||
/* \brief the path of directory containing the output prediction results */
|
||||
char pred_dir_path[256];
|
||||
|
||||
/* \brief the paths of test data sets */
|
||||
std::vector<std::string> test_paths;
|
||||
/* \brief the paths of test data sets */
|
||||
std::vector<std::string> test_paths;
|
||||
|
||||
/* \brief the names of the test data sets */
|
||||
std::vector<std::string> test_names;
|
||||
/* \brief the names of the test data sets */
|
||||
std::vector<std::string> test_names;
|
||||
|
||||
/*!
|
||||
* \brief set parameters from outside
|
||||
* \param name name of the parameter
|
||||
* \param val value of the parameter
|
||||
*/
|
||||
inline void SetParam(const char *name,const char *val ){
|
||||
if( !strcmp("model_dir_path", name ) ) strcpy(model_dir_path,val);
|
||||
if( !strcmp("pred_dir_path", name ) ) strcpy(pred_dir_path,val);
|
||||
if( !strcmp("test_paths", name) ) {
|
||||
test_paths = StringProcessing::split(val,';');
|
||||
}
|
||||
if( !strcmp("test_names", name) ) {
|
||||
test_names = StringProcessing::split(val,';');
|
||||
}
|
||||
}
|
||||
};
|
||||
/*!
|
||||
* \brief set parameters from outside
|
||||
* \param name name of the parameter
|
||||
* \param val value of the parameter
|
||||
*/
|
||||
inline void SetParam(const char *name,const char *val ){
|
||||
if( !strcmp("model_dir_path", name ) ) strcpy(model_dir_path,val);
|
||||
if( !strcmp("pred_dir_path", name ) ) strcpy(pred_dir_path,val);
|
||||
if( !strcmp("test_paths", name) ) {
|
||||
test_paths = StringProcessing::split(val,';');
|
||||
}
|
||||
if( !strcmp("test_names", name) ) {
|
||||
test_names = StringProcessing::split(val,';');
|
||||
}
|
||||
}
|
||||
};
|
||||
|
||||
TestParam test_param;
|
||||
xgboost::regression::RegBoostLearner* reg_boost_learner;
|
||||
};
|
||||
}
|
||||
TestParam test_param;
|
||||
xgboost::regression::RegBoostLearner* reg_boost_learner;
|
||||
};
|
||||
}
|
||||
}
|
||||
|
||||
#endif
|
||||
|
||||
@ -12,120 +12,120 @@
|
||||
using namespace xgboost::utils;
|
||||
|
||||
namespace xgboost{
|
||||
namespace regression{
|
||||
/*!
|
||||
* \brief wrapping the training process of the gradient
|
||||
boosting regression model,given the configuation
|
||||
* \author Kailong Chen: chenkl198812@gmail.com
|
||||
*/
|
||||
class RegBoostTrain{
|
||||
public:
|
||||
/*!
|
||||
* \brief to start the training process of gradient boosting regression
|
||||
* model given the configuation, and finally saved the models
|
||||
* to the specified model directory
|
||||
* \param config_path the location of the configuration
|
||||
* \param silent whether to print feedback messages
|
||||
*/
|
||||
void train(char* config_path,bool silent = false){
|
||||
reg_boost_learner = new xgboost::regression::RegBoostLearner(silent);
|
||||
ConfigIterator config_itr(config_path);
|
||||
//Get the training data and validation data paths, config the Learner
|
||||
while (config_itr.Next()){
|
||||
printf("%s %s\n",config_itr.name(),config_itr.val());
|
||||
reg_boost_learner->SetParam(config_itr.name(),config_itr.val());
|
||||
train_param.SetParam(config_itr.name(),config_itr.val());
|
||||
}
|
||||
namespace regression{
|
||||
/*!
|
||||
* \brief wrapping the training process of the gradient
|
||||
boosting regression model,given the configuation
|
||||
* \author Kailong Chen: chenkl198812@gmail.com
|
||||
*/
|
||||
class RegBoostTrain{
|
||||
public:
|
||||
/*!
|
||||
* \brief to start the training process of gradient boosting regression
|
||||
* model given the configuation, and finally saved the models
|
||||
* to the specified model directory
|
||||
* \param config_path the location of the configuration
|
||||
* \param silent whether to print feedback messages
|
||||
*/
|
||||
void train(char* config_path,bool silent = false){
|
||||
reg_boost_learner = new xgboost::regression::RegBoostLearner(silent);
|
||||
ConfigIterator config_itr(config_path);
|
||||
//Get the training data and validation data paths, config the Learner
|
||||
while (config_itr.Next()){
|
||||
printf("%s %s\n",config_itr.name(),config_itr.val());
|
||||
reg_boost_learner->SetParam(config_itr.name(),config_itr.val());
|
||||
train_param.SetParam(config_itr.name(),config_itr.val());
|
||||
}
|
||||
|
||||
Assert(train_param.validation_data_paths.size() == train_param.validation_data_names.size(),
|
||||
"The number of validation paths is not the same as the number of validation data set names");
|
||||
Assert(train_param.validation_data_paths.size() == train_param.validation_data_names.size(),
|
||||
"The number of validation paths is not the same as the number of validation data set names");
|
||||
|
||||
//Load Data
|
||||
xgboost::regression::DMatrix train;
|
||||
printf("%s",train_param.train_path);
|
||||
train.LoadText(train_param.train_path);
|
||||
std::vector<const xgboost::regression::DMatrix*> evals;
|
||||
for(int i = 0; i < train_param.validation_data_paths.size(); i++){
|
||||
xgboost::regression::DMatrix eval;
|
||||
eval.LoadText(train_param.validation_data_paths[i].c_str());
|
||||
evals.push_back(&eval);
|
||||
}
|
||||
reg_boost_learner->SetData(&train,evals,train_param.validation_data_names);
|
||||
//Load Data
|
||||
xgboost::regression::DMatrix train;
|
||||
printf("%s",train_param.train_path);
|
||||
train.LoadText(train_param.train_path);
|
||||
std::vector<const xgboost::regression::DMatrix*> evals;
|
||||
for(int i = 0; i < train_param.validation_data_paths.size(); i++){
|
||||
xgboost::regression::DMatrix eval;
|
||||
eval.LoadText(train_param.validation_data_paths[i].c_str());
|
||||
evals.push_back(&eval);
|
||||
}
|
||||
reg_boost_learner->SetData(&train,evals,train_param.validation_data_names);
|
||||
|
||||
//begin training
|
||||
reg_boost_learner->InitTrainer();
|
||||
char suffix[256];
|
||||
for(int i = 1; i <= train_param.boost_iterations; i++){
|
||||
reg_boost_learner->UpdateOneIter(i);
|
||||
if(train_param.save_period != 0 && i % train_param.save_period == 0){
|
||||
sscanf(suffix,"%d.model",i);
|
||||
SaveModel(suffix);
|
||||
}
|
||||
}
|
||||
//begin training
|
||||
reg_boost_learner->InitTrainer();
|
||||
char suffix[256];
|
||||
for(int i = 1; i <= train_param.boost_iterations; i++){
|
||||
reg_boost_learner->UpdateOneIter(i);
|
||||
if(train_param.save_period != 0 && i % train_param.save_period == 0){
|
||||
sscanf(suffix,"%d.model",i);
|
||||
SaveModel(suffix);
|
||||
}
|
||||
}
|
||||
|
||||
//save the final round model
|
||||
SaveModel("final.model");
|
||||
}
|
||||
//save the final round model
|
||||
SaveModel("final.model");
|
||||
}
|
||||
|
||||
private:
|
||||
/*! \brief save model in the model directory with specified suffix*/
|
||||
void SaveModel(const char* suffix){
|
||||
char model_path[256];
|
||||
//save the final round model
|
||||
sprintf(model_path,"%s/%s",train_param.model_dir_path,suffix);
|
||||
FILE* file = fopen(model_path,"w");
|
||||
FileStream fin(file);
|
||||
reg_boost_learner->SaveModel(fin);
|
||||
fin.Close();
|
||||
}
|
||||
private:
|
||||
/*! \brief save model in the model directory with specified suffix*/
|
||||
void SaveModel(const char* suffix){
|
||||
char model_path[256];
|
||||
//save the final round model
|
||||
sprintf(model_path,"%s/%s",train_param.model_dir_path,suffix);
|
||||
FILE* file = fopen(model_path,"w");
|
||||
FileStream fin(file);
|
||||
reg_boost_learner->SaveModel(fin);
|
||||
fin.Close();
|
||||
}
|
||||
|
||||
struct TrainParam{
|
||||
/* \brief upperbound of the number of boosters */
|
||||
int boost_iterations;
|
||||
struct TrainParam{
|
||||
/* \brief upperbound of the number of boosters */
|
||||
int boost_iterations;
|
||||
|
||||
/* \brief the period to save the model, -1 means only save the final round model */
|
||||
int save_period;
|
||||
/* \brief the period to save the model, -1 means only save the final round model */
|
||||
int save_period;
|
||||
|
||||
/* \brief the path of training data set */
|
||||
char train_path[256];
|
||||
/* \brief the path of training data set */
|
||||
char train_path[256];
|
||||
|
||||
/* \brief the path of directory containing the saved models */
|
||||
char model_dir_path[256];
|
||||
/* \brief the path of directory containing the saved models */
|
||||
char model_dir_path[256];
|
||||
|
||||
/* \brief the paths of validation data sets */
|
||||
std::vector<std::string> validation_data_paths;
|
||||
/* \brief the paths of validation data sets */
|
||||
std::vector<std::string> validation_data_paths;
|
||||
|
||||
/* \brief the names of the validation data sets */
|
||||
std::vector<std::string> validation_data_names;
|
||||
/* \brief the names of the validation data sets */
|
||||
std::vector<std::string> validation_data_names;
|
||||
|
||||
/*!
|
||||
* \brief set parameters from outside
|
||||
* \param name name of the parameter
|
||||
* \param val value of the parameter
|
||||
*/
|
||||
inline void SetParam(const char *name,const char *val ){
|
||||
if( !strcmp("boost_iterations", name ) ) boost_iterations = atoi( val );
|
||||
if( !strcmp("save_period", name ) ) save_period = atoi( val );
|
||||
if( !strcmp("train_path", name ) ) strcpy(train_path,val);
|
||||
if( !strcmp("model_dir_path", name ) ) {
|
||||
strcpy(model_dir_path,val);
|
||||
}
|
||||
if( !strcmp("validation_paths", name) ) {
|
||||
validation_data_paths = StringProcessing::split(val,';');
|
||||
}
|
||||
if( !strcmp("validation_names", name) ) {
|
||||
validation_data_names = StringProcessing::split(val,';');
|
||||
}
|
||||
}
|
||||
};
|
||||
/*!
|
||||
* \brief set parameters from outside
|
||||
* \param name name of the parameter
|
||||
* \param val value of the parameter
|
||||
*/
|
||||
inline void SetParam(const char *name,const char *val ){
|
||||
if( !strcmp("boost_iterations", name ) ) boost_iterations = atoi( val );
|
||||
if( !strcmp("save_period", name ) ) save_period = atoi( val );
|
||||
if( !strcmp("train_path", name ) ) strcpy(train_path,val);
|
||||
if( !strcmp("model_dir_path", name ) ) {
|
||||
strcpy(model_dir_path,val);
|
||||
}
|
||||
if( !strcmp("validation_paths", name) ) {
|
||||
validation_data_paths = StringProcessing::split(val,';');
|
||||
}
|
||||
if( !strcmp("validation_names", name) ) {
|
||||
validation_data_names = StringProcessing::split(val,';');
|
||||
}
|
||||
}
|
||||
};
|
||||
|
||||
/*! \brief the parameters of the training process*/
|
||||
TrainParam train_param;
|
||||
|
||||
/*! \brief the gradient boosting regression tree model*/
|
||||
xgboost::regression::RegBoostLearner* reg_boost_learner;
|
||||
};
|
||||
}
|
||||
/*! \brief the parameters of the training process*/
|
||||
TrainParam train_param;
|
||||
|
||||
/*! \brief the gradient boosting regression tree model*/
|
||||
xgboost::regression::RegBoostLearner* reg_boost_learner;
|
||||
};
|
||||
}
|
||||
}
|
||||
|
||||
#endif
|
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|
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@ -2,14 +2,14 @@
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#define _XGBOOST_REGDATA_H_
|
||||
|
||||
/*!
|
||||
* \file xgboost_regdata.h
|
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* \brief input data structure for regression and binary classification task.
|
||||
* Format:
|
||||
* The data should contain each data instance in each line.
|
||||
* The format of line data is as below:
|
||||
* label <nonzero feature dimension> [feature index:feature value]+
|
||||
* \author Kailong Chen: chenkl198812@gmail.com, Tianqi Chen: tianqi.tchen@gmail.com
|
||||
*/
|
||||
* \file xgboost_regdata.h
|
||||
* \brief input data structure for regression and binary classification task.
|
||||
* Format:
|
||||
* The data should contain each data instance in each line.
|
||||
* The format of line data is as below:
|
||||
* label <nonzero feature dimension> [feature index:feature value]+
|
||||
* \author Kailong Chen: chenkl198812@gmail.com, Tianqi Chen: tianqi.tchen@gmail.com
|
||||
*/
|
||||
#include <cstdio>
|
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#include <vector>
|
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#include "../booster/xgboost_data.h"
|
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@ -31,17 +31,17 @@ namespace xgboost{
|
||||
/*! \brief default constructor */
|
||||
DMatrix( void ){}
|
||||
|
||||
|
||||
/*! \brief get the number of instances */
|
||||
inline int size() const{
|
||||
return labels.size();
|
||||
}
|
||||
|
||||
/*! \brief get the number of instances */
|
||||
inline int size() const{
|
||||
return labels.size();
|
||||
}
|
||||
|
||||
/*!
|
||||
* \brief load from text file
|
||||
* \param fname name of text data
|
||||
* \param silent whether print information or not
|
||||
*/
|
||||
* \brief load from text file
|
||||
* \param fname name of text data
|
||||
* \param silent whether print information or not
|
||||
*/
|
||||
inline void LoadText( const char* fname, bool silent = false ){
|
||||
data.Clear();
|
||||
FILE* file = utils::FopenCheck( fname, "r" );
|
||||
@ -49,7 +49,7 @@ namespace xgboost{
|
||||
char tmp[ 1024 ];
|
||||
std::vector<booster::bst_uint> findex;
|
||||
std::vector<booster::bst_float> fvalue;
|
||||
|
||||
|
||||
while( fscanf( file, "%s", tmp ) == 1 ){
|
||||
unsigned index; float value;
|
||||
if( sscanf( tmp, "%u:%f", &index, &value ) == 2 ){
|
||||
@ -64,23 +64,23 @@ namespace xgboost{
|
||||
init = false;
|
||||
}
|
||||
}
|
||||
|
||||
labels.push_back( label );
|
||||
|
||||
labels.push_back( label );
|
||||
data.AddRow( findex, fvalue );
|
||||
|
||||
|
||||
this->UpdateInfo();
|
||||
if( !silent ){
|
||||
printf("%ux%u matrix with %lu entries is loaded from %s\n",
|
||||
(unsigned)labels.size(), num_feature, (unsigned long)data.NumEntry(), fname );
|
||||
(unsigned)labels.size(), num_feature, (unsigned long)data.NumEntry(), fname );
|
||||
}
|
||||
fclose(file);
|
||||
}
|
||||
/*!
|
||||
* \brief load from binary file
|
||||
* \param fname name of binary data
|
||||
* \param silent whether print information or not
|
||||
* \return whether loading is success
|
||||
*/
|
||||
* \brief load from binary file
|
||||
* \param fname name of binary data
|
||||
* \param silent whether print information or not
|
||||
* \return whether loading is success
|
||||
*/
|
||||
inline bool LoadBinary( const char* fname, bool silent = false ){
|
||||
FILE *fp = fopen64( fname, "rb" );
|
||||
if( fp == NULL ) return false;
|
||||
@ -92,15 +92,15 @@ namespace xgboost{
|
||||
this->UpdateInfo();
|
||||
if( !silent ){
|
||||
printf("%ux%u matrix with %lu entries is loaded from %s\n",
|
||||
(unsigned)labels.size(), num_feature, (unsigned long)data.NumEntry(), fname );
|
||||
(unsigned)labels.size(), num_feature, (unsigned long)data.NumEntry(), fname );
|
||||
}
|
||||
return true;
|
||||
}
|
||||
/*!
|
||||
* \brief save to binary file
|
||||
* \param fname name of binary data
|
||||
* \param silent whether print information or not
|
||||
*/
|
||||
* \brief save to binary file
|
||||
* \param fname name of binary data
|
||||
* \param silent whether print information or not
|
||||
*/
|
||||
inline void SaveBinary( const char* fname, bool silent = false ){
|
||||
utils::FileStream fs( utils::FopenCheck( fname, "wb" ) );
|
||||
data.SaveBinary( fs );
|
||||
@ -108,17 +108,17 @@ namespace xgboost{
|
||||
fs.Close();
|
||||
if( !silent ){
|
||||
printf("%ux%u matrix with %lu entries is saved to %s\n",
|
||||
(unsigned)labels.size(), num_feature, (unsigned long)data.NumEntry(), fname );
|
||||
(unsigned)labels.size(), num_feature, (unsigned long)data.NumEntry(), fname );
|
||||
}
|
||||
}
|
||||
/*!
|
||||
* \brief cache load data given a file name, the function will first check if fname + '.xgbuffer' exists,
|
||||
* if binary buffer exists, it will reads from binary buffer, otherwise, it will load from text file,
|
||||
* and try to create a buffer file
|
||||
* \param fname name of binary data
|
||||
* \param silent whether print information or not
|
||||
* \return whether loading is success
|
||||
*/
|
||||
* \brief cache load data given a file name, the function will first check if fname + '.xgbuffer' exists,
|
||||
* if binary buffer exists, it will reads from binary buffer, otherwise, it will load from text file,
|
||||
* and try to create a buffer file
|
||||
* \param fname name of binary data
|
||||
* \param silent whether print information or not
|
||||
* \return whether loading is success
|
||||
*/
|
||||
inline void CacheLoad( const char *fname, bool silent = false ){
|
||||
char bname[ 1024 ];
|
||||
sprintf( bname, "%s.buffer", fname );
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user