classweight.cc File Reference

#include <unordered_map>
#include "reductions.h"

Go to the source code of this file.

Classes
struct	CLASSWEIGHTS::classweights

Namespaces
	CLASSWEIGHTS

Functions
template<bool is_learn, int pred_type>
static void	CLASSWEIGHTS::predict_or_learn (classweights &cweights, LEARNER::single_learner &base, example &ec)
LEARNER::base_learner *	classweight_setup (options_i &options, vw &all)

Function Documentation

classweight_setup()

LEARNER::base_learner* classweight_setup	(	options_i &	options,
		vw &	all
	)

Definition at line 71 of file classweight.cc.

References VW::config::options_i::add_and_parse(), LEARNER::as_singleline(), LEARNER::make_base(), VW::config::make_option(), prediction_type::multiclass, LEARNER::learner< T, E >::pred_type, vw::quiet, prediction_type::scalar, setup_base(), THROW, vw::trace_message, and VW::config::options_i::was_supplied().

Referenced by parse_reductions().

{
  std::vector<std::string> classweight_array;
  auto cweights = scoped_calloc_or_throw<classweights>();
  option_group_definition new_options("importance weight classes");
  new_options.add(make_option("classweight", classweight_array).help("importance weight multiplier for class"));
  options.add_and_parse(new_options);

  if (!options.was_supplied("classweight"))
    return nullptr;

  for (auto& s : classweight_array) cweights->load_string(s);

  if (!all.quiet)
    all.trace_message << "parsed " << cweights->weights.size() << " class weights" << std::endl;

  LEARNER::single_learner* base = as_singleline(setup_base(options, all));

  LEARNER::learner<classweights, example>* ret;
  if (base->pred_type == prediction_type::scalar)
    ret = &LEARNER::init_learner<classweights>(cweights, base, predict_or_learn<true, prediction_type::scalar>,
        predict_or_learn<false, prediction_type::scalar>);
  else if (base->pred_type == prediction_type::multiclass)
    ret = &LEARNER::init_learner<classweights>(cweights, base, predict_or_learn<true, prediction_type::multiclass>,
        predict_or_learn<false, prediction_type::multiclass>);
  else
    THROW("--classweight not implemented for this type of prediction");
  return make_base(*ret);
}