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#include <iostream>
#include <Eigen/Dense>
#include <boost/program_options.hpp>
#include <list>
#include "tools/easylogging++.h"
#include "model/ranksvmtn.h"
#include "tools/fileDataProvider.h"
#include "model/rankaccu.h"
INITIALIZE_EASYLOGGINGPP
using namespace Eigen;
using namespace std;
namespace po = boost::program_options;
po::variables_map vm;
typedef int (*mainFunc)(DataProvider &dp);
int train(DataProvider &dp) {
RSVM *rsvm;
rsvm = RSVM::loadModel(vm["model"].as<string>());
dp.open();
RidList D;
LOG(INFO)<<"Training started";
dp.getAllDataSet(D);
LOG(INFO)<<"Read "<<D.getSize()<<" entries with "<< D.getfSize()<<" features";
LOG(INFO)<<"C: "<<C<<" ,iter: "<<maxiter<<" ,prec: "<<prec;
LOG(INFO)<<"cg_maxiter: "<<cg_maxiter<<" ,cg_prec: "<<cg_prec<<" ,ls_maxiter: "<<ls_maxiter<<" ,ls_prec: "<<ls_prec;
rsvm->train(D);
LOG(INFO)<<"Training finished,saving model";
dp.close();
rsvm->saveModel(vm["output"].as<string>().c_str());
delete rsvm;
return 0;
}
int predict(DataProvider &dp) {
RSVM *rsvm;
rsvm = RSVM::loadModel(vm["model"].as<string>().c_str());
dp.open();
RidList D;
vector<double> L;
CMC cmc;
Fscore f;
LOG(INFO)<<"Prediction started";
ofstream fout;
ostream* ot;
if (vm.count("output")) {
fout.open(vm["output"].as<string>().c_str());
ot=&fout;
}
else
ot=&cout;
dp.getAllDataSet(D);
LOG(INFO)<<"Read "<<D.getSize()<<" entries with "<< D.getfSize()<<" features";
rsvm->predict(D,L);
if (vm.count("validate"))
{
rank_accu(D,L);
if (vm.count("cmc"))
rank_CMC(D,L,cmc);
}
if (vm.count("predict"))
{
if (vm.count("pair"))
{
vector<double> pair;
rank_pair(D,L,pair);
for (int i=0;i<pair.size();++i)
*ot<<pair[i]<<endl;
}
else
if (vm.count("fscore"))
{
vector<double> pair;
f.audit(D);
pair=f.getFscore();
for (int i=0;i<D.getfSize();++i)
*ot<<pair[i]<<endl;
}
else
for (int i=0; i<L.size();++i)
*ot<<L[i]<<endl;
}
LOG(INFO)<<"Finished";
if (vm.count("cmc"))
{
LOG(INFO)<< "CMC accounted over " <<cmc.getCount() << " queries";
*ot << "CMC"<<endl;
vector<double> cur = cmc.getAcc();
for (int i = 0;i<CMC_MAX;++i)
*ot << cur[i]<<endl;
*ot << "AVG"<<endl;
*ot << cmc.getAvg()/D.getqSize() <<endl;
}
if (vm.count("output"))
fout.close();
dp.close();
delete rsvm;
return 0;
}
void getmask(string fname,vector<double> &msk)
{
ifstream fin;
int fsize;
fin.open(fname.c_str());
fin>>fsize;
for (int i=0;i<fsize;++i)
fin>>msk[i];
fin.close();
}
int main(int argc, char **argv) {
el::Configurations defaultConf;
defaultConf.setToDefault();
// Values are always std::string
defaultConf.setGlobally(el::ConfigurationType::Format, "%datetime %level %msg");
// Defining program options
po::options_description desc("Allowed options");
desc.add_options()
("help,h", "produce help message")
("train,T", "training model")
("validate,V", "validate model")
("predict,P", "use model for prediction")
("cmc,C", "enable cmc auditing")
("debug,d", "show debug messages")
("single,s", "one from a pair")
("pair,p","get pair result")
("fscore,f","get F-score")
("mask,M", po::value<string>(), "set feature mask")
("model,m", po::value<string>(), "set input model file")
("output,o", po::value<string>(), "set output model/prediction file")
("feature,i", po::value<string>(), "set input feature file")
("c,c",po::value<double>(),"trades margin size against training error")
("iter",po::value<int>(),"iter main")
("prec",po::value<double>(),"prec main")
("cg_iter",po::value<int>(),"iter conjugate gradient")
("cg_prec",po::value<double>(),"prec conjugate gradient")
("ls_iter",po::value<int>(),"iter line search")
("ls_prec",po::value<double>(),"prec line search");
// Parsing program options
po::store(po::parse_command_line(argc, argv, desc), vm);
po::notify(vm);
// Print help if necessary
if (vm.count("help") || !(vm.count("train") || vm.count("validate") || vm.count("predict"))) {
cout << desc;
return 0;
}
if (!vm.count("debug"))
defaultConf.setGlobally(el::ConfigurationType::Enabled, "false");
// default logger uses default configurations
el::Loggers::reconfigureLogger("default", defaultConf);
mainFunc mainf;
RidList::single=vm.count("single")>0;
if (vm.count("train")) {
if (vm.count("c")) { C=vm["c"].as<double>(); }
if (vm.count("iter")) { maxiter=vm["iter"].as<int>(); }
if (vm.count("prec")) { prec=vm["prec"].as<double>(); }
if (vm.count("cg_iter")) { cg_maxiter=vm["cg_iter"].as<int>(); }
if (vm.count("cg_prec")) { cg_prec=vm["cg_prec"].as<double>(); }
if (vm.count("ls_iter")) { ls_maxiter=vm["ls_iter"].as<int>(); }
if (vm.count("ls_prec")) { ls_prec=vm["ls_prec"].as<double>(); }
mainf = &train;
}
else if (vm.count("validate")||vm.count("predict")) {
mainf = &predict;
}
else return 0;
DataProvider* dp;
if (vm["feature"].as<string>().find(".rid") == string::npos)
LOG(FATAL)<<"Format not supported";
else
{
RidFileDP* tmpdp = new RidFileDP(vm["feature"].as<string>());
if (vm.count("mask"))
{
vector<double> msk;
getmask(vm["mask"].as<string>(),msk);
tmpdp->datmask(msk);
}
dp = tmpdp;
}
mainf(*dp);
delete dp;
return 0;
}
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