scorer.cc File Reference

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

Go to the source code of this file.

Classes
struct	scorer

Functions
template<bool is_learn, float(*)(float in) link>
void	predict_or_learn (scorer &s, LEARNER::single_learner &base, example &ec)
template<float(*)(float in) link>
void	multipredict (scorer &, LEARNER::single_learner &base, example &ec, size_t count, size_t, polyprediction *pred, bool finalize_predictions)
void	update (scorer &s, LEARNER::single_learner &base, example &ec)
float	logistic (float in)
float	glf1 (float in)
float	id (float in)
LEARNER::base_learner *	scorer_setup (options_i &options, vw &all)

Function Documentation

glf1()

float glf1 ( float  in )

inline

Definition at line 49 of file scorer.cc.

References correctedExp, and f.

{ return 2.f / (1.f + correctedExp(-in)) - 1.f; }

id()

float id ( float  in )

inline

Definition at line 51 of file scorer.cc.

Referenced by create_circuit(), ect_predict(), ect_train(), Search::foreach_action_from_cache(), CCB::inject_slot_id(), TC_parser< audit >::maybeFeature(), parse_feature_tweaks(), VW::SpanningTree::Run(), GraphTask::run_bfs(), and Search::predictor::set_learner_id().

{ return in; }

logistic()

float logistic ( float  in )

inline

Definition at line 43 of file scorer.cc.

References correctedExp.

{ return 1.f / (1.f + correctedExp(-in)); }

multipredict()

template<float(*)(float in) link>

void multipredict ( scorer &  , LEARNER::single_learner &  base, example &  ec, size_t  count, size_t  , polyprediction *  pred, bool  finalize_predictions  )

inline

Definition at line 29 of file scorer.cc.

References c, LEARNER::learner< T, E >::multipredict(), and prediction_type::scalar.

{
  base.multipredict(ec, 0, count, pred, finalize_predictions);  // TODO: need to thread step through???
  for (size_t c = 0; c < count; c++) pred[c].scalar = link(pred[c].scalar);
}

predict_or_learn()

template<bool is_learn, float(*)(float in) link>

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

Definition at line 14 of file scorer.cc.

References scorer::all, loss_function::getLoss(), example::l, label_data::label, LEARNER::learner< T, E >::learn(), example::loss, vw::loss, example::pred, LEARNER::learner< T, E >::predict(), polyprediction::scalar, vw::sd, vw::set_minmax, polylabel::simple, and example::weight.

{
  s.all->set_minmax(s.all->sd, ec.l.simple.label);
  if (is_learn && ec.l.simple.label != FLT_MAX && ec.weight > 0)
    base.learn(ec);
  else
    base.predict(ec);

  if (ec.weight > 0 && ec.l.simple.label != FLT_MAX)
    ec.loss = s.all->loss->getLoss(s.all->sd, ec.pred.scalar, ec.l.simple.label) * ec.weight;

  ec.pred.scalar = link(ec.pred.scalar);
}

scorer_setup()

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

Definition at line 53 of file scorer.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(), vw::scorer, LEARNER::learner< T, E >::set_multipredict(), LEARNER::learner< T, E >::set_update(), setup_base(), THROW, and update().

Referenced by parse_reductions().

{
  auto s = scoped_calloc_or_throw<scorer>();
  std::string link;
  option_group_definition new_options("scorer options");
  new_options.add(make_option("link", link)
                      .default_value("identity")
                      .keep()
                      .help("Specify the link function: identity, logistic, glf1 or poisson"));
  options.add_and_parse(new_options);

  // This always returns a base_learner.

  s->all = &all;

  auto base = as_singleline(setup_base(options, all));
  LEARNER::learner<scorer, example>* l;
  void (*multipredict_f)(scorer&, LEARNER::single_learner&, example&, size_t, size_t, polyprediction*, bool) =
      multipredict<id>;

  if (link == "identity")
    l = &init_learner(s, base, predict_or_learn<true, id>, predict_or_learn<false, id>);
  else if (link == "logistic")
  {
    l = &init_learner(s, base, predict_or_learn<true, logistic>, predict_or_learn<false, logistic>);
    multipredict_f = multipredict<logistic>;
  }
  else if (link == "glf1")
  {
    l = &init_learner(s, base, predict_or_learn<true, glf1>, predict_or_learn<false, glf1>);
    multipredict_f = multipredict<glf1>;
  }
  else if (link == "poisson")
  {
    l = &init_learner(s, base, predict_or_learn<true, expf>, predict_or_learn<false, expf>);
    multipredict_f = multipredict<expf>;
  }
  else
    THROW("Unknown link function: " << link);

  l->set_multipredict(multipredict_f);
  l->set_update(update);
  all.scorer = LEARNER::as_singleline(l);

  return make_base(*all.scorer);
}

update()

void update	(	scorer &	s,
		LEARNER::single_learner &	base,
		example &	ec
	)

Definition at line 36 of file scorer.cc.

References scorer::all, example::l, label_data::label, vw::sd, vw::set_minmax, polylabel::simple, and LEARNER::learner< T, E >::update().

Referenced by scorer_setup().

{
  s.all->set_minmax(s.all->sd, ec.l.simple.label);
  base.update(ec);
}