mwt.h File Reference

Go to the source code of this file.

Namespaces
	MWT

Functions
LEARNER::base_learner *	mwt_setup (VW::config::options_i &options, vw &all)
void	MWT::delete_scalars (void *v)
void	MWT::print_scalars (int f, v_array< float > &scalars, v_array< char > &tag)

Function Documentation

mwt_setup()

LEARNER::base_learner* mwt_setup	(	VW::config::options_i &	options,
		vw &	all
	)

Definition at line 236 of file mwt.cc.

References VW::config::option_group_definition::add(), add(), VW::config::options_i::add_and_parse(), LEARNER::as_singleline(), c, calloc_reserve(), label_type::cb, CB::cb_label, vw::delete_prediction, delete_scalars(), MWT::finish_example(), LEARNER::init_learner(), VW::config::options_i::insert(), vw::label_type, vw::length(), parser::lp, LEARNER::make_base(), VW::config::make_option(), vw::p, MWT::save_load(), prediction_type::scalars, LEARNER::learner< T, E >::set_finish_example(), LEARNER::learner< T, E >::set_save_load(), setup_base(), and VW::config::options_i::was_supplied().

Referenced by parse_reductions().

{
  auto c = scoped_calloc_or_throw<mwt>();
  std::string s;
  bool exclude_eval = false;
  option_group_definition new_options("Multiworld Testing Options");
  new_options.add(make_option("multiworld_test", s).keep().help("Evaluate features as a policies"))
      .add(make_option("learn", c->num_classes).help("Do Contextual Bandit learning on <n> classes."))
      .add(make_option("exclude_eval", exclude_eval).help("Discard mwt policy features before learning"));
  options.add_and_parse(new_options);

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

  for (char i : s) c->namespaces[(unsigned char)i] = true;
  c->all = &all;

  calloc_reserve(c->evals, all.length());
  c->evals.end() = c->evals.begin() + all.length();

  all.delete_prediction = delete_scalars;
  all.p->lp = CB::cb_label;
  all.label_type = label_type::cb;

  if (c->num_classes > 0)
  {
    c->learn = true;

    if (!options.was_supplied("cb"))
    {
      std::stringstream ss;
      ss << c->num_classes;
      options.insert("cb", ss.str());
    }
  }

  learner<mwt, example>* l;
  if (c->learn)
    if (exclude_eval)
      l = &init_learner(c, as_singleline(setup_base(options, all)), predict_or_learn<true, true, true>,
          predict_or_learn<true, true, false>, 1, prediction_type::scalars);
    else
      l = &init_learner(c, as_singleline(setup_base(options, all)), predict_or_learn<true, false, true>,
          predict_or_learn<true, false, false>, 1, prediction_type::scalars);
  else
    l = &init_learner(c, as_singleline(setup_base(options, all)), predict_or_learn<false, false, true>,
        predict_or_learn<false, false, false>, 1, prediction_type::scalars);

  l->set_save_load(save_load);
  l->set_finish_example(finish_example);
  return make_base(*l);
}