active_cover.h File Reference

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

Function Documentation

active_cover_setup()

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

Definition at line 230 of file active_cover.cc.

References add(), VW::config::options_i::add_and_parse(), LEARNER::as_singleline(), f, vw::get_random_state(), LEARNER::init_learner(), LEARNER::make_base(), VW::config::make_option(), setup_base(), THROW, and VW::config::options_i::was_supplied().

Referenced by parse_reductions().

{
  auto data = scoped_calloc_or_throw<active_cover>();
  option_group_definition new_options("Active Learning with Cover");

  bool active_cover_option = false;
  new_options.add(make_option("active_cover", active_cover_option).keep().help("enable active learning with cover"))
      .add(make_option("mellowness", data->active_c0)
               .default_value(8.f)
               .help("active learning mellowness parameter c_0. Default 8."))
      .add(make_option("alpha", data->alpha)
               .default_value(1.f)
               .help("active learning variance upper bound parameter alpha. Default 1."))
      .add(make_option("beta_scale", data->beta_scale)
               .default_value(sqrtf(10.f))
               .help("active learning variance upper bound parameter beta_scale. Default std::sqrt(10)."))
      .add(make_option("cover", data->cover_size).keep().default_value(12).help("cover size. Default 12."))
      .add(make_option("oracular", data->oracular).help("Use Oracular-CAL style query or not. Default false."));
  options.add_and_parse(new_options);

  if (!active_cover_option)
    return nullptr;

  data->all = &all;
  data->_random_state = all.get_random_state();
  data->beta_scale *= data->beta_scale;

  if (data->oracular)
    data->cover_size = 0;

  if (options.was_supplied("lda"))
    THROW("error: you can't combine lda and active learning");

  if (options.was_supplied("active"))
    THROW("error: you can't use --active_cover and --active at the same time");

  auto base = as_singleline(setup_base(options, all));

  data->lambda_n = new float[data->cover_size];
  data->lambda_d = new float[data->cover_size];

  for (size_t i = 0; i < data->cover_size; i++)
  {
    data->lambda_n[i] = 0.f;
    data->lambda_d[i] = 1.f / 8.f;
  }

  // Create new learner
  learner<active_cover, example>& l = init_learner(
      data, base, predict_or_learn_active_cover<true>, predict_or_learn_active_cover<false>, data->cover_size + 1);

  return make_base(l);
}