284 lines
11 KiB
C++
284 lines
11 KiB
C++
#ifndef XGBOOST_LEARNER_H
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#define XGBOOST_LEARNER_H
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/*!
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* \file xgboost_learner.h
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* \brief class for gradient boosting learner
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* \author Kailong Chen: chenkl198812@gmail.com, Tianqi Chen: tianqi.tchen@gmail.com
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*/
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#include <cmath>
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#include <cstdlib>
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#include <cstring>
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#include "xgboost_data_instance.h"
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#include "../utils/xgboost_omp.h"
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#include "../booster/xgboost_gbmbase.h"
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#include "../utils/xgboost_utils.h"
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#include "../utils/xgboost_stream.h"
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namespace xgboost {
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namespace base {
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/*! \brief class for gradient boosting learner */
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class BoostLearner {
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public:
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/*! \brief constructor */
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BoostLearner(void) {
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silent = 0;
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}
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/*!
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* \brief booster 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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BoostLearner(const DMatrix *train,
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const std::vector<DMatrix *> &evals,
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const std::vector<std::string> &evname) {
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silent = 0;
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this->SetData(train, evals, evname);
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}
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/*!
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* \brief associate 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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const std::vector<DMatrix *> &evals,
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const 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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// estimate feature bound
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int num_feature = (int)(train->data.NumCol());
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// assign buffer index
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unsigned buffer_size = static_cast<unsigned>(train->Size());
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for (size_t i = 0; i < evals.size(); ++i) {
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buffer_size += static_cast<unsigned>(evals[i]->Size());
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num_feature = std::max(num_feature, (int)(evals[i]->data.NumCol()));
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}
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char str_temp[25];
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if (num_feature > mparam.num_feature) {
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mparam.num_feature = num_feature;
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sprintf(str_temp, "%d", num_feature);
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base_gbm.SetParam("bst:num_feature", str_temp);
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}
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sprintf(str_temp, "%u", buffer_size);
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base_gbm.SetParam("num_pbuffer", str_temp);
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if (!silent) {
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printf("buffer_size=%u\n", buffer_size);
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}
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// set eval_preds tmp sapce
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this->eval_preds_.resize(evals.size(), std::vector<float>());
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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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virtual inline void SetParam(const char *name, const char *val) {
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if (!strcmp(name, "silent")) silent = atoi(val);
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mparam.SetParam(name, val);
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base_gbm.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_gbm.InitTrainer();
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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_gbm.InitModel();
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if(!silent) printf("BoostLearner:InitModel Done!\n");
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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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base_gbm.LoadModel(fi);
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utils::Assert(fi.Read(&mparam, sizeof(ModelParam)) != 0);
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}
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/*!
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* \brief DumpModel
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* \param fo text file
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* \param fmap feature map that may help give interpretations of feature
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* \param with_stats whether print statistics as well
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*/
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inline void DumpModel(FILE *fo, const utils::FeatMap& fmap, bool with_stats) {
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base_gbm.DumpModel(fo, fmap, with_stats);
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}
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/*!
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* \brief Dump path of all trees
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* \param fo text file
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* \param data input data
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*/
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inline void DumpPath(FILE *fo, const DMatrix &data) {
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base_gbm.DumpPath(fo, data.data);
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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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base_gbm.SaveModel(fo);
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fo.Write(&mparam, sizeof(ModelParam));
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}
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virtual void EvalOneIter(int iter, FILE *fo = stderr) {}
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/*!
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* \brief update the model for one iteration
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* \param iteration iteration number
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*/
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inline void UpdateOneIter(int iter) {
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this->PredictBuffer(preds_, *train_, 0);
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this->GetGradient(preds_, train_->labels, train_->group_index, grad_, hess_);
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std::vector<unsigned> root_index;
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base_gbm.DoBoost(grad_, hess_, train_->data, root_index);
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// printf("xgboost_learner.h:UpdateOneIter\n");
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// const unsigned ndata = static_cast<unsigned>(train_->Size());
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// #pragma omp parallel for schedule( static )
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// for (unsigned j = 0; j < ndata; ++j) {
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// printf("haha:%d %f\n",j,base_gbm.Predict(train_->data, j, j));
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// }
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}
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/*! \brief get intransformed prediction, without buffering */
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inline void Predict(std::vector<float> &preds, const DMatrix &data) {
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preds.resize(data.Size());
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const unsigned ndata = static_cast<unsigned>(data.Size());
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#pragma omp parallel for schedule( static )
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for (unsigned j = 0; j < ndata; ++j) {
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preds[j] = base_gbm.Predict(data.data, j, -1);
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}
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}
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public:
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/*!
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* \brief update the model for one iteration
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* \param iteration iteration number
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*/
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virtual inline void UpdateInteract(std::string action){
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this->InteractPredict(preds_, *train_, 0);
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int buffer_offset = static_cast<int>(train_->Size());
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for (size_t i = 0; i < evals_.size(); ++i) {
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std::vector<float> &preds = this->eval_preds_[i];
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this->InteractPredict(preds, *evals_[i], buffer_offset);
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buffer_offset += static_cast<int>(evals_[i]->Size());
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}
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if (action == "remove") {
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base_gbm.DelteBooster();
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return;
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}
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this->GetGradient(preds_, train_->labels, train_->group_index, grad_, hess_);
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std::vector<unsigned> root_index;
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base_gbm.DoBoost(grad_, hess_, train_->data, root_index);
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this->InteractRePredict(*train_, 0);
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buffer_offset = static_cast<int>(train_->Size());
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for (size_t i = 0; i < evals_.size(); ++i) {
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this->InteractRePredict(*evals_[i], buffer_offset);
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buffer_offset += static_cast<int>(evals_[i]->Size());
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}
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};
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protected:
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/*! \brief get the intransformed predictions, given data */
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inline void InteractPredict(std::vector<float> &preds, const DMatrix &data, unsigned buffer_offset) {
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preds.resize(data.Size());
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const unsigned ndata = static_cast<unsigned>(data.Size());
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#pragma omp parallel for schedule( static )
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for (unsigned j = 0; j < ndata; ++j) {
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preds[j] = base_gbm.InteractPredict(data.data, j, buffer_offset + j);
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}
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}
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/*! \brief repredict trial */
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inline void InteractRePredict(const xgboost::base::DMatrix &data, unsigned buffer_offset) {
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const unsigned ndata = static_cast<unsigned>(data.Size());
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#pragma omp parallel for schedule( static )
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for (unsigned j = 0; j < ndata; ++j) {
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base_gbm.InteractRePredict(data.data, j, buffer_offset + j);
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}
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}
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/*! \brief get intransformed predictions, given data */
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virtual inline void PredictBuffer(std::vector<float> &preds, const DMatrix &data, unsigned buffer_offset) {
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preds.resize(data.Size());
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const unsigned ndata = static_cast<unsigned>(data.Size());
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#pragma omp parallel for schedule( static )
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for (unsigned j = 0; j < ndata; ++j) {
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preds[j] = base_gbm.Predict(data.data, j, buffer_offset + j);
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}
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}
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/*! \brief get the first order and second order gradient, given the transformed predictions and labels */
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virtual inline void GetGradient(const std::vector<float> &preds,
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const std::vector<float> &labels,
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const std::vector<int> &group_index,
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std::vector<float> &grad,
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std::vector<float> &hess) {};
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protected:
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/*! \brief training parameter for regression */
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struct ModelParam {
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/* \brief type of loss function */
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int loss_type;
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/* \brief number of features */
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int num_feature;
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/*! \brief reserved field */
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int reserved[16];
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/*! \brief constructor */
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ModelParam(void) {
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loss_type = 0;
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num_feature = 0;
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memset(reserved, 0, sizeof(reserved));
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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("loss_type", name)) loss_type = atoi(val);
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if (!strcmp("bst:num_feature", name)) num_feature = atoi(val);
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}
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};
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int silent;
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booster::GBMBase base_gbm;
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ModelParam mparam;
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const DMatrix *train_;
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std::vector<DMatrix *> evals_;
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std::vector<std::string> evname_;
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std::vector<unsigned> buffer_index_;
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std::vector<float> grad_, hess_, preds_;
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std::vector< std::vector<float> > eval_preds_;
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};
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}
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};
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#endif
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