binary.cc File Reference

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

Go to the source code of this file.

Functions
template<bool is_learn>
void	predict_or_learn (char &, LEARNER::single_learner &base, example &ec)
LEARNER::base_learner *	binary_setup (options_i &options, vw &all)

Function Documentation

binary_setup()

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

Definition at line 30 of file binary.cc.

References VW::config::option_group_definition::add(), VW::config::options_i::add_and_parse(), LEARNER::as_singleline(), LEARNER::init_learner(), LEARNER::make_base(), VW::config::make_option(), and setup_base().

Referenced by parse_reductions().

{
  bool binary = false;
  option_group_definition new_options("Binary loss");
  new_options.add(make_option("binary", binary).keep().help("report loss as binary classification on -1,1"));
  options.add_and_parse(new_options);

  if (!binary)
    return nullptr;

  LEARNER::learner<char, example>& ret =
      LEARNER::init_learner(as_singleline(setup_base(options, all)), predict_or_learn<true>, predict_or_learn<false>);
  return make_base(ret);
}

predict_or_learn()

template<bool is_learn>

void predict_or_learn	(	char &	,
		LEARNER::single_learner &	base,
		example &	ec
	)

Definition at line 7 of file binary.cc.

References example::l, label_data::label, LEARNER::learner< T, E >::learn(), example::loss, example::pred, LEARNER::learner< T, E >::predict(), polyprediction::scalar, polylabel::simple, and example::weight.

{
  if (is_learn)
    base.learn(ec);
  else
    base.predict(ec);

  if (ec.pred.scalar > 0)
    ec.pred.scalar = 1;
  else
    ec.pred.scalar = -1;

  if (ec.l.simple.label != FLT_MAX)
  {
    if (fabs(ec.l.simple.label) != 1.f)
      std::cout << "You are using label " << ec.l.simple.label << " not -1 or 1 as loss function expects!" << std::endl;
    else if (ec.l.simple.label == ec.pred.scalar)
      ec.loss = 0.;
    else
      ec.loss = ec.weight;
  }
}