baseline.h File Reference

Go to the source code of this file.

Namespaces
	BASELINE

Functions
LEARNER::base_learner *	baseline_setup (VW::config::options_i &options, vw &all)
void	BASELINE::set_baseline_enabled (example *ec)
void	BASELINE::reset_baseline_disabled (example *ec)
bool	BASELINE::baseline_enabled (example *ec)

Function Documentation

baseline_setup()

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

Definition at line 193 of file baseline.cc.

References VW::config::option_group_definition::add(), add(), VW::config::options_i::add_and_parse(), VW::alloc_examples(), LEARNER::as_singleline(), loss_function::getType(), LEARNER::init_learner(), vw::interactions, label_parser::label_size, vw::loss, LEARNER::make_base(), VW::config::make_option(), sensitivity(), LEARNER::learner< T, E >::set_sensitivity(), setup_base(), and simple_label.

Referenced by parse_reductions().

{
  auto data = scoped_calloc_or_throw<baseline>();
  bool baseline_option = false;
  std::string loss_function;

  option_group_definition new_options("Baseline options");
  new_options
      .add(make_option("baseline", baseline_option)
               .keep()
               .help("Learn an additive baseline (from constant features) and a residual separately in regression."))
      .add(make_option("lr_multiplier", data->lr_multiplier).help("learning rate multiplier for baseline model"))
      .add(make_option("global_only", data->global_only)
               .keep()
               .help("use separate example with only global constant for baseline predictions"))
      .add(make_option("check_enabled", data->check_enabled)
               .keep()
               .help("only use baseline when the example contains enabled flag"));
  options.add_and_parse(new_options);

  if (!baseline_option)
    return nullptr;

  // initialize baseline example
  data->ec = VW::alloc_examples(simple_label.label_size, 1);
  data->ec->interactions = &all.interactions;

  data->ec->in_use = true;
  data->all = &all;

  auto loss_function_type = all.loss->getType();
  if (loss_function_type != "logistic")
    data->lr_scaling = true;

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

  learner<baseline, example>& l = init_learner(data, base, predict_or_learn<true>, predict_or_learn<false>);

  l.set_sensitivity(sensitivity);

  return make_base(l);
}