stagewise_poly.h File Reference

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

Function Documentation

stagewise_poly_setup()

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

Definition at line 656 of file stagewise_poly.cc.

References VW::config::option_group_definition::add(), add(), VW::config::options_i::add_and_parse(), LEARNER::as_singleline(), depthsbits_create(), LEARNER::end_pass(), finish_example(), LEARNER::init_learner(), learn(), LEARNER::make_base(), VW::config::make_option(), predict(), save_load(), LEARNER::learner< T, E >::set_end_pass(), LEARNER::learner< T, E >::set_finish_example(), LEARNER::learner< T, E >::set_save_load(), setup_base(), and sort_data_create().

Referenced by parse_reductions().

{
  auto poly = scoped_calloc_or_throw<stagewise_poly>();
  bool stage_poly = false;
  option_group_definition new_options("Stagewise polynomial options");
  new_options.add(make_option("stage_poly", stage_poly).keep().help("use stagewise polynomial feature learning"))
      .add(make_option("sched_exponent", poly->sched_exponent)
               .default_value(1.f)
               .help("exponent controlling quantity of included features"))
      .add(make_option("batch_sz", poly->batch_sz)
               .default_value(1000)
               .help("multiplier on batch size before including more features"))
      .add(make_option("batch_sz_no_doubling", poly->batch_sz_double).help("batch_sz does not double"));
#ifdef MAGIC_ARGUMENT
  new_options.add(
      make_typed_option("magic_argument", poly->magic_argument).default_value(0.).help("magical feature flag"));
#endif  // MAGIC_ARGUMENT
  options.add_and_parse(new_options);

  if (!stage_poly)
    return nullptr;

  poly->all = &all;
  depthsbits_create(*poly.get());
  sort_data_create(*poly.get());

  poly->batch_sz_double = !poly->batch_sz_double;

  poly->sum_sparsity = 0;
  poly->sum_input_sparsity = 0;
  poly->num_examples = 0;
  poly->sum_sparsity_sync = 0;
  poly->sum_input_sparsity_sync = 0;
  poly->num_examples_sync = 0;
  poly->last_example_counter = -1;
  poly->numpasses = 1;
  poly->update_support = false;
  poly->original_ec = nullptr;
  poly->next_batch_sz = poly->batch_sz;

  learner<stagewise_poly, example> &l = init_learner(poly, as_singleline(setup_base(options, all)), learn, predict);
  l.set_save_load(save_load);
  l.set_finish_example(finish_example);
  l.set_end_pass(end_pass);

  return make_base(l);
}