Returns a program options description for all available options.
20 {
21 po::options_description description("General options");
22 description.add_options()
23 ("help", "print this message")
24 (
"datafiles", po::value<std::vector<std::string>>(&
m_datafiles)->required()->multitoken(),
25 "ROOT files containing the training dataset")
26 (
"treename", po::value<std::string>(&
m_treename),
"Name of tree in ROOT datafile")
27 (
"identifier", po::value<std::string>(&
m_identifier)->required(),
"Identifier of the outputted weightfile")
28 (
"variables", po::value<std::vector<std::string>>(&
m_variables)->required()->multitoken(),
29 "feature variables used in the training")
30 (
"spectators", po::value<std::vector<std::string>>(&
m_spectators)->multitoken(),
31 "spectator variables used in the training")
33 "target variable used to distinguish between signal and background, isSignal is used as default.")
34 (
"signal_class", po::value<int>(&
m_signal_class),
"integer which identifies signal events")
35 (
"nClasses", po::value<unsigned int>(&
m_nClasses),
36 "number of classes under consideration. Must be supplied for multiclass classifications. Not all methods support multiclass classification.")
37 (
"weight_variable", po::value<std::string>(&
m_weight_variable),
"weight variable used to weight each event")
38 (
"max_events", po::value<unsigned int>(&
m_max_events),
"maximum number of events to process, 0 means all")
39 (
"method", po::value<std::string>(&
m_method)->required(),
40 "MVA Method [FastBDT|TMVAClassification|TMVARegression|Python|FANN|]");
41 return description;
42 }
std::vector< std::string > m_datafiles
Name of the datafiles containing the training data.
int m_signal_class
Signal class which is used as signal in a classification problem.
std::vector< std::string > m_variables
Vector of all variables (branch names) used in the training.
std::string m_weight_variable
Weight variable (branch name) defining the weights.
std::vector< std::string > m_spectators
Vector of all spectators (branch names) used in the training.
std::string m_method
Name of the MVA method to use.
unsigned int m_max_events
Maximum number of events to process, 0 means all.
std::string m_treename
Name of the TTree inside the datafile containing the training data.
std::string m_target_variable
Target variable (branch name) defining the target.
unsigned int m_nClasses
Number of classes in a classification problem.
std::string m_identifier
Identifier containing the finished training.