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#include <fstream>
#include <BALL/QSAR/registry.h>
#include <BALL/QSAR/configIO.h>
using namespace BALL::QSAR;
using namespace BALL;
void startPrediction(std::ifstream& in, QSARData* q, String* data_filename)
{
PredictionConfiguration conf = ConfigIO::readPredictionConfiguration(&in);
if(conf.done) return; // stop processing this section
bool created_data_object=0;
if(q==NULL || data_filename==NULL || conf.data!=*data_filename)
{
if(q==NULL)
{
q = new QSARData;
created_data_object=1;
}
q->readFromFile(conf.data);
if(data_filename) *data_filename = conf.data;
}
Registry reg;
Model* m;
String model_type;
std::ifstream model_input(conf.model.c_str()); // read model-abbreviation
if(!model_input)
{
std::cout<<"Error: Model-file '"<<conf.model<<"' does not exist!!"<<std::endl;
return;
}
std::getline(model_input,model_type);
std::getline(model_input,model_type);
model_type = model_type.getField(0,"\t");
model_input.close();
RegistryEntry* entry = reg.getEntry(model_type);
bool regression = entry->regression;
if(!entry->kernel)
{
m = (*entry->create)(*q);
}
else
{
// parameters irrelevant; will be overwritten by those read from file
m = (*entry->createKernel1)(*q,1,1, -1);
}
m->readFromFile(conf.model.c_str()); // do NOT train again (done by ModelCreator) !!
m->model_val->selectStat(conf.statistic);
m->model_val->testInputData(1); // calculate prediction quality
m->model_val->setCVRes(m->model_val->getFitRes());
m->model_val->saveToFile(conf.output);
std::ofstream out(conf.output.c_str(),std::ios::app);
out<<std::endl<<"[Predictions]"<<std::endl;
int no_act = q->getNoResponseVariables();
int no_cols = no_act;
if(conf.print_expected)
{
no_cols*=2;
out<<"# format: predition0, expectation0, ..."<<std::endl;
}
out<<"expected_values = "<<conf.print_expected<<std::endl;
out<<"dimensions = "<<q->getNoSubstances()<<" "<<no_cols<<std::endl;
for(int i=0;i<q->getNoSubstances();i++)
{
vector<double>* v = q->getSubstance(i); // get UNcentered descriptor-vector of test compound
Eigen::VectorXd res = m->predict(*v,1); // transform val. data according to centering of training data
delete v;
vector<double>* exp = q->getActivity(i); // get UNcentered response value vector
for(int j=0; j<res.rows();j++)
{
out<<res[j]<<"\t";
if(conf.print_expected)
{
out<<(*exp)[j]<<"\t";
}
}
delete exp;
out<<std::endl;
}
if(created_data_object) delete q;
delete m;
}
#ifndef EXT_MAIN
int main(int argc, char* argv[])
{
if(argc<2)
{
std::cout<<"Please specify configuration file!"<<std::endl;
return 0;
}
std::ifstream in(argv[1]);
if(!in)
{
std::cout<<"Configuration file '"<<argv[1]<<"' not found!"<<std::endl;
return 0;
}
String line;
for(int i=0;!in.eof();i++) // read ALL Predictor sections
{
for(int j=0;!in.eof();j++) // skip everthing until the beginning of the next Validator-section
{
std::getline(in,line);
if(!line.hasPrefix("[Predictor]")) continue;
else break;
}
if(!line.hasPrefix("[Predictor]")) break; // there are no (more) Validator-sections!
ConfigIO::putbackLine(&in,line);
startPrediction(in,NULL,NULL);
}
}
#endif
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