boosting.h File Reference

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

Function Documentation

boosting_setup()

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

Definition at line 396 of file boosting.cc.

References add(), VW::config::options_i::add_and_parse(), LEARNER::as_singleline(), f, vw::get_random_state(), LEARNER::make_base(), VW::config::make_option(), vw::quiet, return_example(), save_load(), save_load_sampling(), LEARNER::learner< T, E >::set_save_load(), setup_base(), THROW, and VW::config::options_i::was_supplied().

Referenced by parse_reductions().

{
  free_ptr<boosting> data = scoped_calloc_or_throw<boosting>();
  option_group_definition new_options("Boosting");
  new_options.add(make_option("boosting", data->N).keep().help("Online boosting with <N> weak learners"))
      .add(make_option("gamma", data->gamma)
               .default_value(0.1f)
               .help("weak learner's edge (=0.1), used only by online BBM"))
      .add(
          make_option("alg", data->alg)
              .keep()
              .default_value("BBM")
              .help("specify the boosting algorithm: BBM (default), logistic (AdaBoost.OL.W), adaptive (AdaBoost.OL)"));
  options.add_and_parse(new_options);

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

  // Description of options:
  // "BBM" implements online BBM (Algorithm 1 in BLK'15)
  // "logistic" implements AdaBoost.OL.W (importance weighted version
  //        of Algorithm 2 in BLK'15)
  // "adaptive" implements AdaBoost.OL (Algorithm 2 in BLK'15,
  //        using sampling rather than importance weighting)

  if (!all.quiet)
    cerr << "Number of weak learners = " << data->N << endl;
  if (!all.quiet)
    cerr << "Gamma = " << data->gamma << endl;

  data->C = std::vector<std::vector<int64_t> >(data->N, std::vector<int64_t>(data->N, -1));
  data->t = 0;
  data->all = &all;
  data->_random_state = all.get_random_state();
  data->alpha = std::vector<float>(data->N, 0);
  data->v = std::vector<float>(data->N, 1);

  learner<boosting, example>* l;
  if (data->alg == "BBM")
    l = &init_learner<boosting, example>(
        data, as_singleline(setup_base(options, all)), predict_or_learn<true>, predict_or_learn<false>, data->N);
  else if (data->alg == "logistic")
  {
    l = &init_learner<boosting, example>(data, as_singleline(setup_base(options, all)), predict_or_learn_logistic<true>,
        predict_or_learn_logistic<false>, data->N);
    l->set_save_load(save_load);
  }
  else if (data->alg == "adaptive")
  {
    l = &init_learner<boosting, example>(data, as_singleline(setup_base(options, all)), predict_or_learn_adaptive<true>,
        predict_or_learn_adaptive<false>, data->N);
    l->set_save_load(save_load_sampling);
  }
  else
    THROW("Unrecognized boosting algorithm: \'" << data->alg << "\' Bailing!");

  l->set_finish_example(return_example);

  return make_base(*l);
}