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kalenhaha 2014-04-06 22:51:52 +08:00
parent ddb8a6982c
commit 6bc71df494
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dev/README.md
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this folder contains codes under development

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dev/base/xgboost_boost_task.h Normal file
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#define _CRT_SECURE_NO_WARNINGS
#define _CRT_SECURE_NO_DEPRECATE
#include <ctime>
#include <string>
#include <cstring>
#include "xgboost_data_instance.h"
#include "xgboost_learner.h"
#include "../utils/xgboost_fmap.h"
#include "../utils/xgboost_random.h"
#include "../utils/xgboost_config.h"
namespace xgboost{
namespace base{
/*!
* \brief wrapping the training process of the gradient boosting model,
* given the configuation
* \author Kailong Chen: chenkl198812@gmail.com, Tianqi Chen: tianqi.chen@gmail.com
*/
class BoostTask{
public:
inline int Run(int argc, char *argv[]){
if (argc < 2){
printf("Usage: <config>\n");
return 0;
}
utils::ConfigIterator itr(argv[1]);
while (itr.Next()){
this->SetParam(itr.name(), itr.val());
}
for (int i = 2; i < argc; i++){
char name[256], val[256];
if (sscanf(argv[i], "%[^=]=%s", name, val) == 2){
this->SetParam(name, val);
}
}
this->InitData();
this->InitLearner();
if (task == "dump"){
this->TaskDump();
return 0;
}
if (task == "interact"){
this->TaskInteractive(); return 0;
}
if (task == "dumppath"){
this->TaskDumpPath(); return 0;
}
if (task == "eval"){
this->TaskEval(); return 0;
}
if (task == "pred"){
this->TaskPred();
}
else{
this->TaskTrain();
}
return 0;
}
enum learning_tasks{
REGRESSION = 0,
BINARY_CLASSIFICATION = 1,
RANKING = 2
};
/* \brief set learner
* \param learner the passed in learner
*/
inline void SetLearner(BoostLearner* learner){
learner_ = learner;
}
inline void SetParam(const char *name, const char *val){
if (!strcmp("learning_task", name)) learning_task = atoi(val);
if (!strcmp("silent", name)) silent = atoi(val);
if (!strcmp("use_buffer", name)) use_buffer = atoi(val);
if (!strcmp("seed", name)) random::Seed(atoi(val));
if (!strcmp("num_round", name)) num_round = atoi(val);
if (!strcmp("save_period", name)) save_period = atoi(val);
if (!strcmp("task", name)) task = val;
if (!strcmp("data", name)) train_path = val;
if (!strcmp("test:data", name)) test_path = val;
if (!strcmp("model_in", name)) model_in = val;
if (!strcmp("model_out", name)) model_out = val;
if (!strcmp("model_dir", name)) model_dir_path = val;
if (!strcmp("fmap", name)) name_fmap = val;
if (!strcmp("name_dump", name)) name_dump = val;
if (!strcmp("name_dumppath", name)) name_dumppath = val;
if (!strcmp("name_pred", name)) name_pred = val;
if (!strcmp("dump_stats", name)) dump_model_stats = atoi(val);
if (!strcmp("interact:action", name)) interact_action = val;
if (!strncmp("batch:", name, 6)){
cfg_batch.PushBack(name + 6, val);
}
if (!strncmp("eval[", name, 5)) {
char evname[256];
utils::Assert(sscanf(name, "eval[%[^]]", evname) == 1, "must specify evaluation name for display");
eval_data_names.push_back(std::string(evname));
eval_data_paths.push_back(std::string(val));
}
cfg.PushBack(name, val);
}
public:
BoostTask(void){
// default parameters
silent = 0;
use_buffer = 1;
num_round = 10;
save_period = 0;
dump_model_stats = 0;
task = "train";
model_in = "NULL";
model_out = "NULL";
name_fmap = "NULL";
name_pred = "pred.txt";
name_dump = "dump.txt";
name_dumppath = "dump.path.txt";
model_dir_path = "./";
interact_action = "update";
}
~BoostTask(void){
for (size_t i = 0; i < deval.size(); i++){
delete deval[i];
}
}
private:
inline void InitData(void){
if (name_fmap != "NULL") fmap.LoadText(name_fmap.c_str());
if (task == "dump") return;
if (learning_task == RANKING){
char instance_path[256], group_path[256];
if (task == "pred" || task == "dumppath"){
sscanf(test_path.c_str(), "%[^;];%s", instance_path, group_path);
data.CacheLoad(instance_path, group_path, silent != 0, use_buffer != 0);
}
else{
// training
sscanf(train_path.c_str(), "%[^;];%s", instance_path, group_path);
data.CacheLoad(instance_path, group_path, silent != 0, use_buffer != 0);
utils::Assert(eval_data_names.size() == eval_data_paths.size());
for (size_t i = 0; i < eval_data_names.size(); ++i){
deval.push_back(new DMatrix());
sscanf(eval_data_paths[i].c_str(), "%[^;];%s", instance_path, group_path);
deval.back()->CacheLoad(instance_path, group_path, silent != 0, use_buffer != 0);
}
}
}
else{
if (task == "pred" || task == "dumppath"){
data.CacheLoad(test_path.c_str(), "", silent != 0, use_buffer != 0);
}
else{
// training
data.CacheLoad(train_path.c_str(), "", silent != 0, use_buffer != 0);
utils::Assert(eval_data_names.size() == eval_data_paths.size());
for (size_t i = 0; i < eval_data_names.size(); ++i){
deval.push_back(new DMatrix());
deval.back()->CacheLoad(eval_data_paths[i].c_str(), "", silent != 0, use_buffer != 0);
}
}
}
learner_->SetData(&data, deval, eval_data_names);
}
inline void InitLearner(void){
cfg.BeforeFirst();
while (cfg.Next()){
learner_->SetParam(cfg.name(), cfg.val());
}
if (model_in != "NULL"){
utils::FileStream fi(utils::FopenCheck(model_in.c_str(), "rb"));
learner_->LoadModel(fi);
fi.Close();
}
else{
utils::Assert(task == "train", "model_in not specified");
learner_->InitModel();
}
learner_->InitTrainer();
}
inline void TaskTrain(void){
const time_t start = time(NULL);
unsigned long elapsed = 0;
for (int i = 0; i < num_round; ++i){
elapsed = (unsigned long)(time(NULL) - start);
if (!silent) printf("boosting round %d, %lu sec elapsed\n", i, elapsed);
learner_->UpdateOneIter(i);
learner_->EvalOneIter(i);
if (save_period != 0 && (i + 1) % save_period == 0){
this->SaveModel(i);
}
elapsed = (unsigned long)(time(NULL) - start);
}
// always save final round
if (save_period == 0 || num_round % save_period != 0){
if (model_out == "NULL"){
this->SaveModel(num_round - 1);
}
else{
this->SaveModel(model_out.c_str());
}
}
if (!silent){
printf("\nupdating end, %lu sec in all\n", elapsed);
}
}
inline void TaskEval(void){
learner_->EvalOneIter(0);
}
inline void TaskInteractive(void){
const time_t start = time(NULL);
unsigned long elapsed = 0;
int batch_action = 0;
cfg_batch.BeforeFirst();
while (cfg_batch.Next()){
if (!strcmp(cfg_batch.name(), "run")){
learner_->UpdateInteract(interact_action);
batch_action += 1;
}
else{
learner_->SetParam(cfg_batch.name(), cfg_batch.val());
}
}
if (batch_action == 0){
learner_->UpdateInteract(interact_action);
}
utils::Assert(model_out != "NULL", "interactive mode must specify model_out");
this->SaveModel(model_out.c_str());
elapsed = (unsigned long)(time(NULL) - start);
if (!silent){
printf("\ninteractive update, %d batch actions, %lu sec in all\n", batch_action, elapsed);
}
}
inline void TaskDump(void){
FILE *fo = utils::FopenCheck(name_dump.c_str(), "w");
learner_->DumpModel(fo, fmap, dump_model_stats != 0);
fclose(fo);
}
inline void TaskDumpPath(void){
FILE *fo = utils::FopenCheck(name_dumppath.c_str(), "w");
learner_->DumpPath(fo, data);
fclose(fo);
}
inline void SaveModel(const char *fname) const{
utils::FileStream fo(utils::FopenCheck(fname, "wb"));
learner_->SaveModel(fo);
fo.Close();
}
inline void SaveModel(int i) const{
char fname[256];
sprintf(fname, "%s/%04d.model", model_dir_path.c_str(), i + 1);
this->SaveModel(fname);
}
inline void TaskPred(void){
std::vector<float> preds;
if (!silent) printf("start prediction...\n");
learner_->Predict(preds, data);
if (!silent) printf("writing prediction to %s\n", name_pred.c_str());
FILE *fo = utils::FopenCheck(name_pred.c_str(), "w");
for (size_t i = 0; i < preds.size(); i++){
fprintf(fo, "%f\n", preds[i]);
}
fclose(fo);
}
private:
/* \brief specify the learning task*/
int learning_task;
/* \brief whether silent */
int silent;
/* \brief whether use auto binary buffer */
int use_buffer;
/* \brief number of boosting iterations */
int num_round;
/* \brief the period to save the model, 0 means only save the final round model */
int save_period;
/*! \brief interfact action */
std::string interact_action;
/* \brief the path of training/test data set */
std::string train_path, test_path;
/* \brief the path of test model file, or file to restart training */
std::string model_in;
/* \brief the path of final model file, to be saved */
std::string model_out;
/* \brief the path of directory containing the saved models */
std::string model_dir_path;
/* \brief task to perform, choosing training or testing */
std::string task;
/* \brief name of predict file */
std::string name_pred;
/* \brief whether dump statistics along with model */
int dump_model_stats;
/* \brief name of feature map */
std::string name_fmap;
/* \brief name of dump file */
std::string name_dump;
/* \brief name of dump path file */
std::string name_dumppath;
/* \brief the paths of validation data sets */
std::vector<std::string> eval_data_paths;
/* \brief the names of the evaluation data used in output log */
std::vector<std::string> eval_data_names;
/*! \brief saves configurations */
utils::ConfigSaver cfg;
/*! \brief batch configurations */
utils::ConfigSaver cfg_batch;
private:
DMatrix data;
std::vector<DMatrix*> deval;
utils::FeatMap fmap;
BoostLearner* learner_;
};
};
};

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#ifndef XGBOOST_DATA_INSTANCE_H
#define XGBOOST_DATA_INSTANCE_H
#include <cstdio>
#include <vector>
#include "../booster/xgboost_data.h"
#include "../utils/xgboost_utils.h"
#include "../utils/xgboost_stream.h"
namespace xgboost{
namespace base{
/*! \brief data matrix for regression,classification,rank content */
struct DMatrix{
public:
/*! \brief maximum feature dimension */
unsigned num_feature;
/*! \brief feature data content */
booster::FMatrixS data;
/*! \brief label of each instance */
std::vector<float> labels;
/*! \brief the index of begin and end of a group,
* needed when the learning task is ranking*/
std::vector<int> group_index;
public:
/*! \brief default constructor */
DMatrix(void){}
/*! \brief get the number of instances */
inline size_t Size() const{
return labels.size();
}
/*!
* \brief load from text file
* \param fname file of instances data
* \param fgroup file of the group data
* \param silent whether print information or not
*/
inline void LoadText(const char* fname, const char* fgroup, bool silent = false){
data.Clear();
FILE* file = utils::FopenCheck(fname, "r");
float label; bool init = true;
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){
findex.push_back(index); fvalue.push_back(value);
}
else{
if (!init){
labels.push_back(label);
data.AddRow(findex, fvalue);
}
findex.clear(); fvalue.clear();
utils::Assert(sscanf(tmp, "%f", &label) == 1, "invalid format");
init = false;
}
}
labels.push_back(label);
data.AddRow(findex, fvalue);
// initialize column support as well
data.InitData();
if (!silent){
printf("%ux%u matrix with %lu entries is loaded from %s\n",
(unsigned)data.NumRow(), (unsigned)data.NumCol(), (unsigned long)data.NumEntry(), fname);
}
fclose(file);
//if exists group data load it in
FILE *file_group = fopen64(fgroup, "r");
if (file_group != NULL){
group_index.push_back(0);
int tmp = 0, acc = 0;
while (fscanf(file_group, "%d", tmp) == 1){
acc += tmp;
group_index.push_back(acc);
}
}
}
/*!
* \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, const char* fgroup, bool silent = false){
FILE *fp = fopen64(fname, "rb");
if (fp == NULL) return false;
utils::FileStream fs(fp);
data.LoadBinary(fs);
labels.resize(data.NumRow());
utils::Assert(fs.Read(&labels[0], sizeof(float)* data.NumRow()) != 0, "DMatrix LoadBinary");
fs.Close();
// initialize column support as well
data.InitData();
if (!silent){
printf("%ux%u matrix with %lu entries is loaded from %s\n",
(unsigned)data.NumRow(), (unsigned)data.NumCol(), (unsigned long)data.NumEntry(), fname);
}
//if group data exists load it in
FILE *file_group = fopen64(fgroup, "r");
if (file_group != NULL){
int group_index_size = 0;
utils::FileStream group_stream(file_group);
utils::Assert(group_stream.Read(&group_index_size, sizeof(int)) != 0, "Load group indice size");
group_index.resize(group_index_size);
utils::Assert(group_stream.Read(&group_index, sizeof(int)* group_index_size) != 0, "Load group indice");
if (!silent){
printf("the group index of %d groups is loaded from %s\n",
group_index_size - 1, fgroup);
}
}
return true;
}
/*!
* \brief save to binary file
* \param fname name of binary data
* \param silent whether print information or not
*/
inline void SaveBinary(const char* fname, const char* fgroup, bool silent = false){
// initialize column support as well
data.InitData();
utils::FileStream fs(utils::FopenCheck(fname, "wb"));
data.SaveBinary(fs);
fs.Write(&labels[0], sizeof(float)* data.NumRow());
fs.Close();
if (!silent){
printf("%ux%u matrix with %lu entries is saved to %s\n",
(unsigned)data.NumRow(), (unsigned)data.NumCol(), (unsigned long)data.NumEntry(), fname);
}
//save group data
if (group_index.size() > 0){
utils::FileStream file_group(utils::FopenCheck(fgroup, "wb"));
int group_index_size = group_index.size();
file_group.Write(&(group_index_size), sizeof(int));
file_group.Write(&group_index[0], sizeof(int) * group_index_size);
}
}
/*!
* \brief cache load data given a file name, if filename ends with .buffer, direct load binary
* otherwise the function will first check if fname + '.buffer' 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
* \param savebuffer whether do save binary buffer if it is text
*/
inline void CacheLoad(const char *fname, const char *fgroup, bool silent = false, bool savebuffer = true){
int len = strlen(fname);
if (len > 8 && !strcmp(fname + len - 7, ".buffer")){
this->LoadBinary(fname, fgroup, silent); return;
}
char bname[1024];
sprintf(bname, "%s.buffer", fname);
if (!this->LoadBinary(bname, fgroup, silent)){
this->LoadText(fname, fgroup, silent);
if (savebuffer) this->SaveBinary(bname, fgroup, silent);
}
}
private:
/*! \brief update num_feature info */
inline void UpdateInfo(void){
this->num_feature = 0;
for (size_t i = 0; i < data.NumRow(); i++){
booster::FMatrixS::Line sp = data[i];
for (unsigned j = 0; j < sp.len; j++){
if (num_feature <= sp[j].findex){
num_feature = sp[j].findex + 1;
}
}
}
}
};
}
};
#endif

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

261
dev/rank/xgboost_rank.h
View File

@ -9,21 +9,22 @@
#include <cstdlib>
#include <cstring>
#include "xgboost_sample.h"
#include "xgboost_rank_data.h"
#include "xgboost_rank_eval.h"
#include "../base/xgboost_data_instance.h"
#include "../utils/xgboost_omp.h"
#include "../booster/xgboost_gbmbase.h"
#include "../utils/xgboost_utils.h"
#include "../utils/xgboost_stream.h"
#include "../base/xgboost_learner.h"
namespace xgboost {
namespace rank {
/*! \brief class for gradient boosted regression */
class RankBoostLearner {
class RankBoostLearner :public base::BoostLearner{
public:
/*! \brief constructor */
RegBoostLearner( void ) {
silent = 0;
RankBoostLearner(void) {
BoostLearner();
}
/*!
* \brief a rank booster associated with training and evaluating data
@ -31,220 +32,49 @@ public:
* \param evals array of evaluating data
* \param evname name of evaluation data, used print statistics
*/
RankBoostLearner( const RMatrix *train,
const std::vector<RMatrix *> &evals,
RankBoostLearner(const base::DMatrix *train,
const std::vector<base::DMatrix *> &evals,
const std::vector<std::string> &evname) {
silent = 0;
this->SetData(train,evals,evname);
BoostLearner(train, evals, evname);
}
/*!
* \brief associate rank booster with training and evaluating data
* \param train pointer to the training data
* \param evals array of evaluating data
* \param evname name of evaluation data, used print statistics
*/
inline void SetData( const RMatrix *train,
const std::vector<RMatrix *> &evals,
const std::vector<std::string> &evname ) {
this->train_ = train;
this->evals_ = evals;
this->evname_ = evname;
// estimate feature bound
int num_feature = (int)(train->data.NumCol());
// assign buffer index
unsigned buffer_size = static_cast<unsigned>( train->Size() );
for( size_t i = 0; i < evals.size(); ++ i ) {
buffer_size += static_cast<unsigned>( evals[i]->Size() );
num_feature = std::max( num_feature, (int)(evals[i]->data.NumCol()) );
}
char str_temp[25];
if( num_feature > mparam.num_feature ) {
mparam.num_feature = num_feature;
sprintf( str_temp, "%d", num_feature );
base_gbm.SetParam( "bst:num_feature", str_temp );
}
sprintf( str_temp, "%u", buffer_size );
base_gbm.SetParam( "num_pbuffer", str_temp );
if( !silent ) {
printf( "buffer_size=%u\n", buffer_size );
}
// set eval_preds tmp sapce
this->eval_preds_.resize( evals.size(), std::vector<float>() );
}
/*!
* \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( name, "silent") ) silent = atoi( val );
if( !strcmp( name, "eval_metric") ) evaluator_.AddEval( val );
mparam.SetParam( name, val );
base_gbm.SetParam( name, val );
}
/*!
* \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
*/
inline void InitTrainer(void) {
base_gbm.InitTrainer();
BoostLearner::InitTrainer();
if (mparam.loss_type == PAIRWISE) {
evaluator_.AddEval("PAIR");
} else if( mparam.loss_type == MAP) {
}
else if (mparam.loss_type == MAP) {
evaluator_.AddEval("MAP");
} else {
}
else {
evaluator_.AddEval("NDCG");
}
evaluator_.Init();
sampler.AssignSampler(mparam.sampler_type);
}
/*!
* \brief initialize the current data storage for model, if the model is used first time, call this function
*/
inline void InitModel( void ) {
base_gbm.InitModel();
}
/*!
* \brief load model from stream
* \param fi input stream
*/
inline void LoadModel( utils::IStream &fi ) {
base_gbm.LoadModel( fi );
utils::Assert( fi.Read( &mparam, sizeof(ModelParam) ) != 0 );
}
/*!
* \brief DumpModel
* \param fo text file
* \param fmap feature map that may help give interpretations of feature
* \param with_stats whether print statistics as well
*/
inline void DumpModel( FILE *fo, const utils::FeatMap& fmap, bool with_stats ) {
base_gbm.DumpModel( fo, fmap, with_stats );
}
/*!
* \brief Dump path of all trees
* \param fo text file
* \param data input data
*/
inline void DumpPath( FILE *fo, const RMatrix &data ) {
base_gbm.DumpPath( fo, data.data );
}
/*!
* \brief save model to stream
* \param fo output stream
*/
inline void SaveModel( utils::IStream &fo ) const {
base_gbm.SaveModel( fo );
fo.Write( &mparam, sizeof(ModelParam) );
}
/*!
* \brief update the model for one iteration
* \param iteration iteration number
*/
inline void UpdateOneIter( int iter ) {
this->PredictBuffer( preds_, *train_, 0 );
this->GetGradient( preds_, train_->labels,train_->group_index, grad_, hess_ );
std::vector<unsigned> root_index;
base_gbm.DoBoost( grad_, hess_, train_->data, root_index );
}
/*!
* \brief evaluate the model for specific iteration
* \param iter iteration number
* \param fo file to output log
*/
inline void EvalOneIter( int iter, FILE *fo = stderr ) {
void EvalOneIter(int iter, FILE *fo = stderr) {
fprintf(fo, "[%d]", iter);
int buffer_offset = static_cast<int>(train_->Size());
for (size_t i = 0; i < evals_.size(); ++i) {
std::vector<float> &preds = this->eval_preds_[i];
this->PredictBuffer(preds, *evals_[i], buffer_offset);
evaluator_.Eval( fo, evname_[i].c_str(), preds, (*evals_[i]).labels );
evaluator_.Eval(fo, evname_[i].c_str(), preds, (*evals_[i]).labels, (*evals_[i]).group_index);
buffer_offset += static_cast<int>(evals_[i]->Size());
}
fprintf(fo, "\n");
}
/*! \brief get intransformed prediction, without buffering */
inline void Predict( std::vector<float> &preds, const DMatrix &data ) {
preds.resize( data.Size() );
const unsigned ndata = static_cast<unsigned>( data.Size() );
#pragma omp parallel for schedule( static )
for( unsigned j = 0; j < ndata; ++ j ) {
preds[j] = base_gbm.Predict( data.data, j, -1 );
inline void SetParam(const char *name, const char *val){
if (!strcmp(name, "eval_metric")) evaluator_.AddEval(val);
if (!strcmp(name, "rank:sampler")) sampler.AssignSampler(atoi(val));
}
}
public:
/*!
* \brief update the model for one iteration
* \param iteration iteration number
*/
inline void UpdateInteract( std::string action ) {
this->InteractPredict( preds_, *train_, 0 );
int buffer_offset = static_cast<int>( train_->Size() );
for( size_t i = 0; i < evals_.size(); ++i ) {
std::vector<float> &preds = this->eval_preds_[ i ];
this->InteractPredict( preds, *evals_[i], buffer_offset );
buffer_offset += static_cast<int>( evals_[i]->Size() );
}
if( action == "remove" ) {
base_gbm.DelteBooster();
return;
}
this->GetGradient( preds_, train_->labels, grad_, hess_ );
std::vector<unsigned> root_index;
base_gbm.DoBoost( grad_, hess_, train_->data, root_index );
this->InteractRePredict( *train_, 0 );
buffer_offset = static_cast<int>( train_->Size() );
for( size_t i = 0; i < evals_.size(); ++i ) {
this->InteractRePredict( *evals_[i], buffer_offset );
buffer_offset += static_cast<int>( evals_[i]->Size() );
}
}
private:
/*! \brief get the transformed predictions, given data */
inline void InteractPredict( std::vector<float> &preds, const DMatrix &data, unsigned buffer_offset ) {
preds.resize( data.Size() );
const unsigned ndata = static_cast<unsigned>( data.Size() );
#pragma omp parallel for schedule( static )
for( unsigned j = 0; j < ndata; ++ j ) {
preds[j] = base_gbm.InteractPredict( data.data, j, buffer_offset + j );
}
}
/*! \brief repredict trial */
inline void InteractRePredict( const DMatrix &data, unsigned buffer_offset ) {
const unsigned ndata = static_cast<unsigned>( data.Size() );
#pragma omp parallel for schedule( static )
for( unsigned j = 0; j < ndata; ++ j ) {
base_gbm.InteractRePredict( data.data, j, buffer_offset + j );
}
}
private:
/*! \brief get intransformed predictions, given data */
inline void PredictBuffer( std::vector<float> &preds, const RMatrix &data, unsigned buffer_offset ) {
preds.resize( data.Size() );
const unsigned ndata = static_cast<unsigned>( data.Size() );
#pragma omp parallel for schedule( static )
for( unsigned j = 0; j < ndata; ++ j ) {
preds[j] = base_gbm.Predict( data.data, j, buffer_offset + j );
}
}
/*! \brief get the first order and second order gradient, given the transformed predictions and labels */
inline void GetGradient(const std::vector<float> &preds,
const std::vector<float> &labels,
@ -256,7 +86,6 @@ private:
bool j_better;
float pred_diff, pred_diff_exp, first_order_gradient, second_order_gradient;
for (int i = 0; i < group_index.size() - 1; i++){
sample::Pairs pairs = sampler.GenPairs(preds, labels, group_index[i], group_index[i + 1]);
for (int j = group_index[i]; j < group_index[i + 1]; j++){
std::vector<int> pair_instance = pairs.GetPairs(j);
@ -265,8 +94,8 @@ private:
if (j_better){
pred_diff = preds[preds[j] - pair_instance[k]];
pred_diff_exp = j_better ? expf(-pred_diff) : expf(pred_diff);
first_order_gradient = mparam.FirstOrderGradient(pred_diff_exp);
second_order_gradient = 2 * mparam.SecondOrderGradient(pred_diff_exp);
first_order_gradient = FirstOrderGradient(pred_diff_exp);
second_order_gradient = 2 * SecondOrderGradient(pred_diff_exp);
hess[j] += second_order_gradient;
grad[j] += first_order_gradient;
hess[pair_instance[k]] += second_order_gradient;
@ -275,9 +104,11 @@ private:
}
}
}
}
inline void UpdateInteract(std::string action) {
}
private:
enum LossType {
PAIRWISE = 0,
@ -285,32 +116,6 @@ private:
NDCG = 2
};
/*! \brief training parameter for regression */
struct ModelParam {
/* \brief type of loss function */
int loss_type;
/* \brief number of features */
int num_feature;
/*! \brief reserved field */
int reserved[ 16 ];
/*! \brief sampler type */
int sampler_type;
/*! \brief constructor */
ModelParam( void ) {
loss_type = 0;
num_feature = 0;
memset( reserved, 0, sizeof( reserved ) );
}
/*!
* \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("loss_type", name ) ) loss_type = atoi( val );
if( !strcmp("bst:num_feature", name ) ) num_feature = atoi( val );
if( !strcmp("rank:sampler",name)) sampler = atoi( val );
}
/*!
@ -334,22 +139,12 @@ private:
inline float SecondOrderGradient(float pred_diff_exp) const {
return pred_diff_exp / pow(1 + pred_diff_exp, 2);
}
};
private:
int silent;
RankEvalSet evaluator_;
sample::PairSamplerWrapper sampler;
booster::GBMBase base_gbm;
ModelParam mparam;
const RMatrix *train_;
std::vector<RMatrix *> evals_;
std::vector<std::string> evname_;
std::vector<unsigned> buffer_index_;
private:
std::vector<float> grad_, hess_, preds_;
std::vector< std::vector<float> > eval_preds_;
};
}
};
};
#endif

179
dev/rank/xgboost_rank_data.h
View File

@ -1,179 +0,0 @@
#ifndef XGBOOST_RANK_DATA_H
#define XGBOOST_RANK_DATA_H
/*!
* \file xgboost_rank_data.h
* \brief input data structure for rank task.
* Format:
* The data should contains groups of rank data, a group here may refer to
* the rank list of a query, or the browsing history of a user, etc.
* Each group first contains the size of the group in a single line,
* then following is the line data with the same format with the regression data:
* label <nonzero feature dimension> [feature index:feature value]+
* \author Kailong Chen: chenkl198812@gmail.com, Tianqi Chen: tianqi.tchen@gmail.com
*/
#include <cstdio>
#include <vector>
#include "../booster/xgboost_data.h"
#include "../utils/xgboost_utils.h"
#include "../utils/xgboost_stream.h"
namespace xgboost {
namespace rank {
/*! \brief data matrix for regression content */
struct RMatrix {
public:
/*! \brief maximum feature dimension */
unsigned num_feature;
/*! \brief feature data content */
booster::FMatrixS data;
/*! \brief label of each instance */
std::vector<float> labels;
/*! \brief The index of begin and end of each group */
std::vector<int> group_index;
public:
/*! \brief default constructor */
RMatrix( void ) {}
/*! \brief get the number of instances */
inline size_t Size() const {
return labels.size();
}
/*!
* \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" );
float label;
bool init = true;
char tmp[ 1024 ];
int group_size,group_size_acc = 0;
std::vector<booster::bst_uint> findex;
std::vector<booster::bst_float> fvalue;
group_index.push_back(0);
while(fscanf(file, "%d",group_size) == 1) {
group_size_acc += group_size;
group_index.push_back(group_size_acc);
unsigned index;
float value;
if( sscanf( tmp, "%u:%f", &index, &value ) == 2 ) {
findex.push_back( index );
fvalue.push_back( value );
} else {
if( !init ) {
labels.push_back( label );
data.AddRow( findex, fvalue );
}
findex.clear();
fvalue.clear();
utils::Assert( sscanf( tmp, "%f", &label ) == 1, "invalid format" );
init = false;
}
}
labels.push_back( label );
data.AddRow( findex, fvalue );
// initialize column support as well
data.InitData();
if( !silent ) {
printf("%ux%u matrix with %lu entries is loaded from %s\n",
(unsigned)data.NumRow(), (unsigned)data.NumCol(), (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
*/
inline bool LoadBinary( const char* fname, bool silent = false ) {
FILE *fp = fopen64( fname, "rb" );
int group_index_size = 0;
if( fp == NULL ) return false;
utils::FileStream fs( fp );
data.LoadBinary( fs );
labels.resize( data.NumRow() );
utils::Assert( fs.Read( &labels[0], sizeof(float) * data.NumRow() ) != 0, "DMatrix LoadBinary" );
utils::Assert( fs.Read( &group_index_size, sizeof(int) ) != 0, "Load group indice size" );
group_index.resize(group_index_size);
utils::Assert( fs.Read( &group_index, sizeof(int) * group_index_size) != 0, "Load group indice" d);
fs.Close();
// initialize column support as well
data.InitData();
if( !silent ) {
printf("%ux%u matrix with %lu entries is loaded from %s\n",
(unsigned)data.NumRow(), (unsigned)data.NumCol(), (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
*/
inline void SaveBinary( const char* fname, bool silent = false ) {
// initialize column support as well
data.InitData();
utils::FileStream fs( utils::FopenCheck( fname, "wb" ) );
data.SaveBinary( fs );
fs.Write( &labels[0], sizeof(float) * data.NumRow() );
fs.Write( &(group_index.size()), sizeof(int));
fs.Write( &group_index[0], sizeof(int) * group_index.size() );
fs.Close();
if( !silent ) {
printf("%ux%u matrix with %lu entries is saved to %s\n",
(unsigned)data.NumRow(), (unsigned)data.NumCol(), (unsigned long)data.NumEntry(), fname );
}
}
/*!
* \brief cache load data given a file name, if filename ends with .buffer, direct load binary
* otherwise the function will first check if fname + '.buffer' 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
* \param savebuffer whether do save binary buffer if it is text
*/
inline void CacheLoad( const char *fname, bool silent = false, bool savebuffer = true ) {
int len = strlen( fname );
if( len > 8 && !strcmp( fname + len - 7, ".buffer") ) {
this->LoadBinary( fname, silent );
return;
}
char bname[ 1024 ];
sprintf( bname, "%s.buffer", fname );
if( !this->LoadBinary( bname, silent ) ) {
this->LoadText( fname, silent );
if( savebuffer ) this->SaveBinary( bname, silent );
}
}
private:
/*! \brief update num_feature info */
inline void UpdateInfo( void ) {
this->num_feature = 0;
for( size_t i = 0; i < data.NumRow(); i ++ ) {
booster::FMatrixS::Line sp = data[i];
for( unsigned j = 0; j < sp.len; j ++ ) {
if( num_feature <= sp[j].findex ) {
num_feature = sp[j].findex + 1;
}
}
}
}
};
};
};
#endif

69
dev/rank/xgboost_rank_eval.h
View File

@ -15,7 +15,8 @@
namespace xgboost {
namespace rank {
/*! \brief evaluator that evaluates the loss metrics */
struct IRankEvaluator {
class IRankEvaluator {
public:
/*!
* \brief evaluate a specific metric
* \param preds prediction
@ -28,7 +29,8 @@ struct IRankEvaluator {
virtual const char *Name(void) const = 0;
};
struct Pair{
class Pair{
public:
float key_;
float value_;
@ -46,17 +48,11 @@ bool PairValueComparer(const Pair &a, const Pair &b){
return a.value_ < b.value_;
}
struct EvalPair : public IRankEvaluator{
virtual float Eval( const std::vector<float> &preds,
const std::vector<float> &labels,
const std::vector<int> &group_index ) const {
return 0;
}
};
/*! \brief Mean Average Precision */
struct EvalMAP : public IRankEvaluator {
virtual float Eval( const std::vector<float> &preds,
class EvalMAP : public IRankEvaluator {
public:
float Eval(const std::vector<float> &preds,
const std::vector<float> &labels,
const std::vector<int> &group_index) const {
float acc = 0;
@ -71,8 +67,14 @@ struct EvalMAP : public IRankEvaluator {
return acc / (group_index.size() - 1);
}
float float average_precision(std::vector<Pair> pairs_sort){
std::sort<Pair>(pairs_sort.begin(),pairs_sort.end(),PairKeyComparer);
virtual const char *Name(void) const {
return "MAP";
}
float average_precision(std::vector<Pair> pairs_sort) const{
std::sort(pairs_sort.begin(), pairs_sort.end(), PairKeyComparer);
float hits = 0;
float average_precision = 0;
for (int j = 0; j < pairs_sort.size(); j++){
@ -84,19 +86,29 @@ struct EvalMAP : public IRankEvaluator {
if (hits != 0) average_precision /= hits;
return average_precision;
}
virtual const char *Name( void ) const {
return "MAP";
}
};
/*! \brief Normalized DCG */
struct EvalNDCG : public IRankEvaluator {
virtual float Eval( const std::vector<float> &preds,
class EvalPair : public IRankEvaluator{
public:
float Eval(const std::vector<float> &preds,
const std::vector<float> &labels,
const std::vector<int> &group_index) const {
return 0;
}
const char *Name(void) const {
return "PAIR";
}
};
/*! \brief Normalized DCG */
class EvalNDCG : public IRankEvaluator {
public:
float Eval(const std::vector<float> &preds,
const std::vector<float> &labels,
const std::vector<int> &group_index) const {
if (group_index.size() <= 1) return 0;
float acc = 0;
std::vector<Pair> pairs_sort;
for (int i = 0; i < group_index.size() - 1; i++){
@ -106,18 +118,19 @@ struct EvalNDCG : public IRankEvaluator {
}
acc += NDCG(pairs_sort);
}
return acc / (group_index.size() - 1);
}
float NDCG(std::vector<Pair> pairs_sort){
std::sort<Pair>(pairs_sort.begin(),pairs_sort.end(),PairKeyComparer);
float DCG = DCG(pairs_sort);
std::sort<Pair>(pairs_sort.begin(),pairs_sort.end(),PairValueComparer);
float NDCG(std::vector<Pair> pairs_sort) const{
std::sort(pairs_sort.begin(), pairs_sort.end(), PairKeyComparer);
float dcg = DCG(pairs_sort);
std::sort(pairs_sort.begin(), pairs_sort.end(), PairValueComparer);
float IDCG = DCG(pairs_sort);
if (IDCG == 0) return 0;
return DCG/IDCG;
return dcg / IDCG;
}
float DCG(std::vector<Pair> pairs_sort){
float DCG(std::vector<Pair> pairs_sort) const{
float ans = 0.0;
ans += pairs_sort[0].value_;
for (int i = 1; i < pairs_sort.size(); i++){
@ -135,7 +148,7 @@ struct EvalNDCG : public IRankEvaluator {
namespace rank {
/*! \brief a set of evaluators */
struct RankEvalSet {
class RankEvalSet {
public:
inline void AddEval(const char *name) {
if (!strcmp(name, "PAIR")) evals_.push_back(&pair_);

287
dev/rank/xgboost_rank_main.cpp
View File

@ -1,283 +1,30 @@
#define _CRT_SECURE_NO_WARNINGS
#define _CRT_SECURE_NO_DEPRECATE
#include <ctime>
#include <string>
#include <cstring>
#include "xgboost_rank.h"
#include "../base/xgboost_learner.h"
#include "../utils/xgboost_fmap.h"
#include "../utils/xgboost_random.h"
#include "../utils/xgboost_config.h"
namespace xgboost {
namespace rank {
/*!
* \brief wrapping the training process of the gradient boosting regression model,
* given the configuation
* \author Kailong Chen: chenkl198812@gmail.com, Tianqi Chen: tianqi.chen@gmail.com
*/
class RankBoostTask {
public:
inline int Run( int argc, char *argv[] ) {
if( argc < 2 ) {
printf("Usage: <config>\n");
return 0;
}
utils::ConfigIterator itr( argv[1] );
while( itr.Next() ) {
this->SetParam( itr.name(), itr.val() );
}
for( int i = 2; i < argc; i ++ ) {
char name[256], val[256];
if( sscanf( argv[i], "%[^=]=%s", name, val ) == 2 ) {
this->SetParam( name, val );
}
}
this->InitData();
this->InitLearner();
if( task == "dump" ) {
this->TaskDump();
return 0;
}
if( task == "interact" ) {
this->TaskInteractive();
return 0;
}
if( task == "dumppath" ) {
this->TaskDumpPath();
return 0;
}
if( task == "eval" ) {
this->TaskEval();
return 0;
}
if( task == "pred" ) {
this->TaskPred();
} else {
this->TaskTrain();
}
return 0;
}
inline void SetParam( const char *name, const char *val ) {
if( !strcmp("silent", name ) ) silent = atoi( val );
if( !strcmp("use_buffer", name ) ) use_buffer = atoi( val );
if( !strcmp("seed", name ) ) random::Seed( atoi(val) );
if( !strcmp("num_round", name ) ) num_round = atoi( val );
if( !strcmp("save_period", name ) ) save_period = atoi( val );
if( !strcmp("task", name ) ) task = val;
if( !strcmp("data", name ) ) train_path = val;
if( !strcmp("test:data", name ) ) test_path = val;
if( !strcmp("model_in", name ) ) model_in = val;
if( !strcmp("model_out", name ) ) model_out = val;
if( !strcmp("model_dir", name ) ) model_dir_path = val;
if( !strcmp("fmap", name ) ) name_fmap = val;
if( !strcmp("name_dump", name ) ) name_dump = val;
if( !strcmp("name_dumppath", name ) ) name_dumppath = val;
if( !strcmp("name_pred", name ) ) name_pred = val;
if( !strcmp("dump_stats", name ) ) dump_model_stats = atoi( val );
if( !strcmp("interact:action", name ) ) interact_action = val;
if( !strncmp("batch:", name, 6 ) ) {
cfg_batch.PushBack( name + 6, val );
}
if( !strncmp("eval[", name, 5 ) ) {
char evname[ 256 ];
utils::Assert( sscanf( name, "eval[%[^]]", evname ) == 1, "must specify evaluation name for display");
eval_data_names.push_back( std::string( evname ) );
eval_data_paths.push_back( std::string( val ) );
}
cfg.PushBack( name, val );
}
public:
RankBoostTask( void ) {
// default parameters
silent = 0;
use_buffer = 1;
num_round = 10;
save_period = 0;
dump_model_stats = 0;
task = "train";
model_in = "NULL";
model_out = "NULL";
name_fmap = "NULL";
name_pred = "pred.txt";
name_dump = "dump.txt";
name_dumppath = "dump.path.txt";
model_dir_path = "./";
interact_action = "update";
}
~RankBoostTask( void ) {
for( size_t i = 0; i < deval.size(); i ++ ) {
delete deval[i];
}
}
private:
inline void InitData( void ) {
if( name_fmap != "NULL" ) fmap.LoadText( name_fmap.c_str() );
if( task == "dump" ) return;
if( task == "pred" || task == "dumppath" ) {
data.CacheLoad( test_path.c_str(), silent!=0, use_buffer!=0 );
} else {
// training
data.CacheLoad( train_path.c_str(), silent!=0, use_buffer!=0 );
utils::Assert( eval_data_names.size() == eval_data_paths.size() );
for( size_t i = 0; i < eval_data_names.size(); ++ i ) {
deval.push_back( new RMatrix() );
deval.back()->CacheLoad( eval_data_paths[i].c_str(), silent!=0, use_buffer!=0 );
}
}
learner.SetData( &data, deval, eval_data_names );
}
inline void InitLearner( void ) {
cfg.BeforeFirst();
while( cfg.Next() ) {
learner.SetParam( cfg.name(), cfg.val() );
}
if( model_in != "NULL" ) {
utils::FileStream fi( utils::FopenCheck( model_in.c_str(), "rb") );
learner.LoadModel( fi );
fi.Close();
} else {
utils::Assert( task == "train", "model_in not specified" );
learner.InitModel();
}
learner.InitTrainer();
}
inline void TaskTrain( void ) {
const time_t start = time( NULL );
unsigned long elapsed = 0;
for( int i = 0; i < num_round; ++ i ) {
elapsed = (unsigned long)(time(NULL) - start);
if( !silent ) printf("boosting round %d, %lu sec elapsed\n", i , elapsed );
learner.UpdateOneIter( i );
learner.EvalOneIter( i );
if( save_period != 0 && (i+1) % save_period == 0 ) {
this->SaveModel( i );
}
elapsed = (unsigned long)(time(NULL) - start);
}
// always save final round
if( save_period == 0 || num_round % save_period != 0 ) {
if( model_out == "NULL" ) {
this->SaveModel( num_round - 1 );
} else {
this->SaveModel( model_out.c_str() );
}
}
if( !silent ) {
printf("\nupdating end, %lu sec in all\n", elapsed );
}
}
inline void TaskEval( void ) {
learner.EvalOneIter( 0 );
}
inline void TaskInteractive( void ) {
const time_t start = time( NULL );
unsigned long elapsed = 0;
int batch_action = 0;
cfg_batch.BeforeFirst();
while( cfg_batch.Next() ) {
if( !strcmp( cfg_batch.name(), "run" ) ) {
learner.UpdateInteract( interact_action );
batch_action += 1;
} else {
learner.SetParam( cfg_batch.name(), cfg_batch.val() );
}
}
if( batch_action == 0 ) {
learner.UpdateInteract( interact_action );
}
utils::Assert( model_out != "NULL", "interactive mode must specify model_out" );
this->SaveModel( model_out.c_str() );
elapsed = (unsigned long)(time(NULL) - start);
if( !silent ) {
printf("\ninteractive update, %d batch actions, %lu sec in all\n", batch_action, elapsed );
}
}
inline void TaskDump( void ) {
FILE *fo = utils::FopenCheck( name_dump.c_str(), "w" );
learner.DumpModel( fo, fmap, dump_model_stats != 0 );
fclose( fo );
}
inline void TaskDumpPath( void ) {
FILE *fo = utils::FopenCheck( name_dumppath.c_str(), "w" );
learner.DumpPath( fo, data );
fclose( fo );
}
inline void SaveModel( const char *fname ) const {
utils::FileStream fo( utils::FopenCheck( fname, "wb" ) );
learner.SaveModel( fo );
fo.Close();
}
inline void SaveModel( int i ) const {
char fname[256];
sprintf( fname ,"%s/%04d.model", model_dir_path.c_str(), i+1 );
this->SaveModel( fname );
}
inline void TaskPred( void ) {
std::vector<float> preds;
if( !silent ) printf("start prediction...\n");
learner.Predict( preds, data );
if( !silent ) printf("writing prediction to %s\n", name_pred.c_str() );
FILE *fo = utils::FopenCheck( name_pred.c_str(), "w" );
for( size_t i = 0; i < preds.size(); i ++ ) {
fprintf( fo, "%f\n", preds[i] );
}
fclose( fo );
}
private:
/* \brief whether silent */
int silent;
/* \brief whether use auto binary buffer */
int use_buffer;
/* \brief number of boosting iterations */
int num_round;
/* \brief the period to save the model, 0 means only save the final round model */
int save_period;
/*! \brief interfact action */
std::string interact_action;
/* \brief the path of training/test data set */
std::string train_path, test_path;
/* \brief the path of test model file, or file to restart training */
std::string model_in;
/* \brief the path of final model file, to be saved */
std::string model_out;
/* \brief the path of directory containing the saved models */
std::string model_dir_path;
/* \brief task to perform */
std::string task;
/* \brief name of predict file */
std::string name_pred;
/* \brief whether dump statistics along with model */
int dump_model_stats;
/* \brief name of feature map */
std::string name_fmap;
/* \brief name of dump file */
std::string name_dump;
/* \brief name of dump path file */
std::string name_dumppath;
/* \brief the paths of validation data sets */
std::vector<std::string> eval_data_paths;
/* \brief the names of the evaluation data used in output log */
std::vector<std::string> eval_data_names;
/*! \brief saves configurations */
utils::ConfigSaver cfg;
/*! \brief batch configurations */
utils::ConfigSaver cfg_batch;
private:
RMatrix data;
std::vector<RMatrix*> deval;
utils::FeatMap fmap;
RankBoostLearner learner;
};
};
};
#include "../base/xgboost_learner.h"
#include "../base/xgboost_boost_task.h"
#include "xgboost_rank.h"
#include "../regression/xgboost_reg.h"
int main(int argc, char *argv[]) {
xgboost::random::Seed(0);
xgboost::rank::RankBoostTask tsk;
xgboost::base::BoostTask tsk;
xgboost::utils::ConfigIterator itr(argv[1]);
int learner_index = 0;
while (itr.Next()){
if (!strcmp(itr.name(), "learning_task")){
learner_index = atoi(itr.val());
}
}
xgboost::rank::RankBoostLearner* rank_learner = new xgboost::rank::RankBoostLearner;
xgboost::base::BoostLearner *parent = static_cast<xgboost::base::BoostLearner*>(rank_learner);
tsk.SetLearner(parent);
return tsk.Run(argc, argv);
}

8
dev/rank/xgboost_sample.h
View File

@ -21,7 +21,7 @@ namespace xgboost {
*/
Pairs(int start,int end):start_(start),end_(end_){
for(int i = start; i < end; i++){
vector<int> v;
std::vector<int> v;
pairs_.push_back(v);
}
}
@ -31,7 +31,7 @@ namespace xgboost {
* \return the index of instances paired
*/
std::vector<int> GetPairs(int index) {
utils::assert(index >= start_ && index < end_,"The query index out of sampling bound");
utils::Assert(index >= start_ && index < end_,"The query index out of sampling bound");
return pairs_[index-start_];
}
@ -115,7 +115,7 @@ namespace xgboost {
Pairs GenPairs(const std::vector<float> &preds,
const std::vector<float> &labels,
int start,int end){
return sampler_.GenPairs(preds,labels,start,end);
return sampler_->GenPairs(preds,labels,start,end);
}
private:
BinaryLinearSampler binary_linear_sampler;
@ -124,4 +124,4 @@ namespace xgboost {
}
}
}
#endif

5
regression/xgboost_reg_main.cpp
View File

@ -273,8 +273,3 @@ namespace xgboost{
};
};
int main( int argc, char *argv[] ){
xgboost::random::Seed( 0 );
xgboost::regression::RegBoostTask tsk;
return tsk.Run( argc, argv );
}

2
utils/xgboost_omp.h
View File

@ -10,7 +10,7 @@
#if defined(_OPENMP)
#include <omp.h>
#else
#warning "OpenMP is not available, compile to single thread code"
//#warning "OpenMP is not available, compile to single thread code"
inline int omp_get_thread_num() { return 0; }
inline int omp_get_num_threads() { return 1; }
inline void omp_set_num_threads( int nthread ) {}

10
utils/xgboost_utils.h
View File

@ -62,6 +62,16 @@ namespace xgboost{
}
return fp;
}
/*! \brief replace fopen, */
inline FILE *FopenTry( const char *fname , const char *flag ){
FILE *fp = fopen64( fname , flag );
if( fp == NULL ){
fprintf( stderr, "can not open file \"%s\"\n",fname );
exit( -1 );
}
return fp;
}
};
};