recall_tree.h File Reference

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

Function Documentation

recall_tree_setup()

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

Definition at line 502 of file recall_tree.cc.

References add(), VW::config::options_i::add_and_parse(), LEARNER::as_singleline(), f, vw::get_random_state(), LEARNER::init_multiclass_learner(), init_tree(), recall_tree_ns::learn(), LEARNER::make_base(), VW::config::make_option(), vw::p, recall_tree_ns::predict(), vw::quiet, save_load_tree(), LEARNER::learner< T, E >::set_save_load(), setup_base(), vw::trace_message, vw::training, and VW::config::options_i::was_supplied().

Referenced by parse_reductions().

{
  auto tree = scoped_calloc_or_throw<recall_tree>();
  option_group_definition new_options("Recall Tree");
  new_options.add(make_option("recall_tree", tree->k).keep().help("Use online tree for multiclass"))
      .add(make_option("max_candidates", tree->max_candidates)
               .keep()
               .help("maximum number of labels per leaf in the tree"))
      .add(make_option("bern_hyper", tree->bern_hyper).default_value(1.f).help("recall tree depth penalty"))
      .add(make_option("max_depth", tree->max_depth).keep().help("maximum depth of the tree, default log_2 (#classes)"))
      .add(make_option("node_only", tree->node_only).keep().help("only use node features, not full path features"))
      .add(make_option("randomized_routing", tree->randomized_routing).keep().help("randomized routing"));
  options.add_and_parse(new_options);

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

  tree->all = &all;
  tree->_random_state = all.get_random_state();
  tree->max_candidates = options.was_supplied("max_candidates")
      ? tree->max_candidates
      : std::min(tree->k, 4 * (uint32_t)(ceil(log(tree->k) / log(2.0))));
  tree->max_depth =
      options.was_supplied("max_depth") ? tree->max_depth : (uint32_t)std::ceil(std::log(tree->k) / std::log(2.0));

  init_tree(*tree.get());

  if (!all.quiet)
    all.trace_message << "recall_tree:"
                      << " node_only = " << tree->node_only << " bern_hyper = " << tree->bern_hyper
                      << " max_depth = " << tree->max_depth << " routing = "
                      << (all.training ? (tree->randomized_routing ? "randomized" : "deterministic") : "n/a testonly")
                      << std::endl;

  learner<recall_tree, example>& l = init_multiclass_learner(
      tree, as_singleline(setup_base(options, all)), learn, predict, all.p, tree->max_routers + tree->k);
  l.set_save_load(save_load_tree);

  return make_base(l);
}