LEARNER::learner< T, E > Struct Template Reference

#include <cb_explore.h>

Public Types
using	end_fptr_type = void(*)(vw &, void *, void *)
using	finish_fptr_type = void(*)(void *)

Public Member Functions
void	learn (E &ec, size_t i=0)
void	predict (E &ec, size_t i=0)
void	multipredict (E &ec, size_t lo, size_t count, polyprediction *pred, bool finalize_predictions)
template<class L >
void	set_predict (void(*u)(T &, L &, E &))
template<class L >
void	set_learn (void(*u)(T &, L &, E &))
template<class L >
void	set_multipredict (void(*u)(T &, L &, E &, size_t, size_t, polyprediction *, bool))
void	update (E &ec, size_t i=0)
template<class L >
void	set_update (void(*u)(T &data, L &base, E &))
void	set_sensitivity (float(*u)(T &data, base_learner &base, example &))
float	sensitivity (example &ec, size_t i=0)
void	save_load (io_buf &io, const bool read, const bool text)
void	set_save_load (void(*sl)(T &, io_buf &, bool, bool))
void	set_finish (void(*f)(T &))
void	finish ()
void	end_pass ()
void	set_end_pass (void(*f)(T &))
void	end_examples ()
void	set_end_examples (void(*f)(T &))
void	init_driver ()
void	set_init_driver (void(*f)(T &))
void	finish_example (vw &all, E &ec)
void	set_finish_example (void(*f)(vw &all, T &, E &))

Static Public Member Functions
template<class L >
static learner< T, E > &	init_learner (T *dat, L *base, void(*learn)(T &, L &, E &), void(*predict)(T &, L &, E &), size_t ws, prediction_type::prediction_type_t pred_type)

Public Attributes
prediction_type::prediction_type_t	pred_type
size_t	weights
size_t	increment
bool	is_multiline

Private Member Functions
	learner ()

Private Attributes
func_data	init_fd
learn_data	learn_fd
sensitivity_data	sensitivity_fd
finish_example_data	finish_example_fd
save_load_data	save_load_fd
func_data	end_pass_fd
func_data	end_examples_fd
func_data	finisher_fd
std::shared_ptr< void >	learner_data

Detailed Description

template<class T, class E>
struct LEARNER::learner< T, E >

Definition at line 11 of file cb_explore.h.

Member Typedef Documentation

end_fptr_type

template<class T, class E>

using LEARNER::learner< T, E >::end_fptr_type = void (*)(vw&, void*, void*)

Definition at line 156 of file learner.h.

finish_fptr_type

template<class T, class E>

using LEARNER::learner< T, E >::finish_fptr_type = void (*)(void*)

Definition at line 157 of file learner.h.

Constructor & Destructor Documentation

learner()

template<class T, class E>

LEARNER::learner< T, E >::learner ( )

inlineprivate

Definition at line 149 of file learner.h.

{};  // Should only be able to construct a learner through init_learner function

Member Function Documentation

end_examples()

template<class T, class E>

void LEARNER::learner< T, E >::end_examples ( )

inline

Definition at line 289 of file learner.h.

Referenced by LEARNER::drain_examples(), LEARNER::learner< CB_EXPLORE::cb_explore, example >::end_examples(), and LEARNER::generic_driver_onethread().

  {
    end_examples_fd.func(end_examples_fd.data);
    if (end_examples_fd.base)
      end_examples_fd.base->end_examples();
  }

end_pass()

template<class T, class E>

void LEARNER::learner< T, E >::end_pass ( )

inline

Definition at line 280 of file learner.h.

Referenced by LEARNER::end_pass(), and LEARNER::learner< CB_EXPLORE::cb_explore, example >::end_pass().

  {
    end_pass_fd.func(end_pass_fd.data);
    if (end_pass_fd.base)
      end_pass_fd.base->end_pass();
  }  // autorecursive

finish()

template<class T, class E>

void LEARNER::learner< T, E >::finish ( )

inline

Definition at line 266 of file learner.h.

Referenced by LEARNER::learner< CB_EXPLORE::cb_explore, example >::finish(), and VW::finish().

  {
    if (finisher_fd.data)
    {
      finisher_fd.func(finisher_fd.data);
    }
    learner_data.~shared_ptr<void>();
    if (finisher_fd.base)
    {
      finisher_fd.base->finish();
      free(finisher_fd.base);
    }
  }

finish_example()

template<class T, class E>

void LEARNER::learner< T, E >::finish_example ( vw &  all, E &  ec  )

inline

Definition at line 302 of file learner.h.

Referenced by vw::finish_example(), LEARNER::learn_ex(), and LEARNER::learn_multi_ex().

  {
    finish_example_fd.finish_example_f(all, finish_example_fd.data, (void*)&ec);
  }

init_driver()

template<class T, class E>

void LEARNER::learner< T, E >::init_driver ( )

inline

Definition at line 298 of file learner.h.

Referenced by VW::initialize().

{ init_fd.func(init_fd.data); }

init_learner()

template<class T, class E>

template<class L >

static learner<T, E>& LEARNER::learner< T, E >::init_learner ( T *  dat, L *  base, void(*)(T &, L &, E &)  learn, void(*)(T &, L &, E &)  predict, size_t  ws, prediction_type::prediction_type_t  pred_type  )

inlinestatic

Definition at line 314 of file learner.h.

  {
    learner<T, E>& ret = calloc_or_throw<learner<T, E> >();

    if (base != nullptr)
    {  // a reduction

      // This is a copy assignment into the current object. The purpose is to copy all of the
      // function data objects so that if this reduction does not define a function such as
      // save_load then calling save_load on this object will essentially result in forwarding the
      // call the next reduction that actually implements it.
      ret = *(learner<T, E>*)(base);

      ret.learn_fd.base = make_base(*base);
      ret.sensitivity_fd.sensitivity_f = (sensitivity_data::fn)recur_sensitivity;
      ret.finisher_fd.data = dat;
      ret.finisher_fd.base = make_base(*base);
      ret.finisher_fd.func = (func_data::fn)noop;
      ret.weights = ws;
      ret.increment = base->increment * ret.weights;
    }
    else  // a base learner
    {
      ret.weights = 1;
      ret.increment = ws;
      ret.end_pass_fd.func = (func_data::fn)noop;
      ret.end_examples_fd.func = (func_data::fn)noop;
      ret.init_fd.func = (func_data::fn)noop;
      ret.save_load_fd.save_load_f = (save_load_data::fn)noop_sl;
      ret.finisher_fd.data = dat;
      ret.finisher_fd.func = (func_data::fn)noop;
      ret.sensitivity_fd.sensitivity_f = (sensitivity_data::fn)noop_sensitivity;
      ret.finish_example_fd.data = dat;
      ret.finish_example_fd.finish_example_f = (finish_example_data::fn)return_simple_example;
    }

    ret.learner_data = std::shared_ptr<T>(dat, [](T* ptr) {
      ptr->~T();
      free(ptr);
    });

    ret.learn_fd.data = dat;
    ret.learn_fd.learn_f = (learn_data::fn)learn;
    ret.learn_fd.update_f = (learn_data::fn)learn;
    ret.learn_fd.predict_f = (learn_data::fn)predict;
    ret.learn_fd.multipredict_f = nullptr;
    ret.pred_type = pred_type;
    ret.is_multiline = std::is_same<multi_ex, E>::value;

    return ret;
  }

learn()

template<class T, class E>

void LEARNER::learner< T, E >::learn ( E &  ec, size_t  i = 0  )

inline

Definition at line 160 of file learner.h.

Referenced by GEN_CS::call_cs_ldf(), do_actual_learning_ldf(), CSOAA::do_actual_learning_oaa(), CSOAA::do_actual_learning_wap(), ect_predict(), ect_train(), ExpReplay::end_pass(), Search::generate_training_example(), CB_ALGS::get_cost_pred(), CSOAA::inner_loop(), inner_loop(), VW::autolink::learn(), VW::topk::learn(), recall_tree_ns::learn(), vw::learn(), learn(), memory_tree_ns::learn_at_leaf_random(), learn_bandit_adf(), learn_randomized(), learn_sup_adf(), LEARNER::multiline_learn_or_predict(), VW::shared_feature_merger::predict_or_learn(), ExpReplay::predict_or_learn(), CLASSWEIGHTS::predict_or_learn(), CB_ALGS::predict_or_learn(), MWT::predict_or_learn(), predict_or_learn(), MARGINAL::predict_or_learn(), predict_or_learn_active(), predict_or_learn_active_cover(), predict_or_learn_adaptive(), predict_or_learn_adf(), CB_EXPLORE::predict_or_learn_bag(), CB_EXPLORE::predict_or_learn_cover(), CB_EXPLORE::predict_or_learn_first(), CB_EXPLORE::predict_or_learn_greedy(), predict_or_learn_logistic(), predict_or_learn_multi(), predict_or_learn_simulation(), predict_or_learn_with_confidence(), memory_tree_ns::return_reward_from_node(), memory_tree_ns::single_query_and_learn(), train_node(), recall_tree_ns::train_node(), memory_tree_ns::train_node(), and memory_tree_ns::train_one_against_some_at_leaf().

  {
    assert((is_multiline && std::is_same<multi_ex, E>::value) ||
        (!is_multiline && std::is_same<example, E>::value));  // sanity check under debug compile
    increment_offset(ec, increment, i);
    learn_fd.learn_f(learn_fd.data, *learn_fd.base, (void*)&ec);
    decrement_offset(ec, increment, i);
  }

multipredict()

template<class T, class E>

void LEARNER::learner< T, E >::multipredict ( E &  ec, size_t  lo, size_t  count, polyprediction *  pred, bool  finalize_predictions  )

inline

Definition at line 178 of file learner.h.

Referenced by multipredict(), ExpReplay::multipredict(), CSOAA::predict_or_learn(), predict_or_learn(), and predict_or_learn_multi().

  {
    assert((is_multiline && std::is_same<multi_ex, E>::value) ||
        (!is_multiline && std::is_same<example, E>::value));  // sanity check under debug compile
    if (learn_fd.multipredict_f == NULL)
    {
      increment_offset(ec, increment, lo);
      for (size_t c = 0; c < count; c++)
      {
        learn_fd.predict_f(learn_fd.data, *learn_fd.base, (void*)&ec);
        if (finalize_predictions)
          pred[c] = ec.pred;  // TODO: this breaks for complex labels because = doesn't do deep copy!
        else
          pred[c].scalar = ec.partial_prediction;
        // pred[c].scalar = finalize_prediction ec.partial_prediction; // TODO: this breaks for complex labels because =
        // doesn't do deep copy! // note works if ec.partial_prediction, but only if finalize_prediction is run????
        increment_offset(ec, increment, 1);
      }
      decrement_offset(ec, increment, lo + count);
    }
    else
    {
      increment_offset(ec, increment, lo);
      learn_fd.multipredict_f(learn_fd.data, *learn_fd.base, (void*)&ec, count, increment, pred, finalize_predictions);
      decrement_offset(ec, increment, lo);
    }
  }

predict()

template<class T, class E>

void LEARNER::learner< T, E >::predict ( E &  ec, size_t  i = 0  )

inline

Definition at line 169 of file learner.h.

Referenced by GEN_CS::call_cs_ldf(), memory_tree_ns::compute_hamming_loss_via_oas(), do_actual_learning_ldf(), find_cost_range(), CB_ALGS::get_cost_pred(), CSOAA::inner_loop(), inner_loop(), vw::learn(), CSOAA::make_single_prediction(), LEARNER::multiline_learn_or_predict(), recall_tree_ns::oas_predict(), memory_tree_ns::pick_nearest(), VW::autolink::predict(), VW::topk::predict(), predict(), vw::predict(), memory_tree_ns::predict(), predict_bandit_adf(), recall_tree_ns::predict_from(), VW::shared_feature_merger::predict_or_learn(), ExpReplay::predict_or_learn(), CLASSWEIGHTS::predict_or_learn(), CB_ALGS::predict_or_learn(), MWT::predict_or_learn(), predict_or_learn(), MARGINAL::predict_or_learn(), predict_or_learn_active(), predict_or_learn_active_cover(), predict_or_learn_adaptive(), predict_or_learn_adf(), CB_EXPLORE::predict_or_learn_bag(), CB_EXPLORE::predict_or_learn_first(), CB_EXPLORE::predict_or_learn_greedy(), predict_or_learn_logistic(), predict_or_learn_multi(), predict_or_learn_simulation(), predict_or_learn_with_confidence(), predict_sublearner_adf(), VW::autolink::prepare_example(), query_decision(), memory_tree_ns::return_reward_from_node(), memory_tree_ns::route_to_leaf(), sensitivity(), Search::single_prediction_LDF(), Search::single_prediction_notLDF(), memory_tree_ns::split_leaf(), train_node(), recall_tree_ns::train_node(), memory_tree_ns::train_node(), VW_Predict(), and VW_PredictCostSensitive().

  {
    assert((is_multiline && std::is_same<multi_ex, E>::value) ||
        (!is_multiline && std::is_same<example, E>::value));  // sanity check under debug compile
    increment_offset(ec, increment, i);
    learn_fd.predict_f(learn_fd.data, *learn_fd.base, (void*)&ec);
    decrement_offset(ec, increment, i);
  }

save_load()

template<class T, class E>

void LEARNER::learner< T, E >::save_load ( io_buf &  io, const bool  read, const bool  text  )

inline

Definition at line 251 of file learner.h.

Referenced by dump_regressor(), load_input_model(), parse_mask_regressor_args(), and LEARNER::learner< CB_EXPLORE::cb_explore, example >::save_load().

  {
    save_load_fd.save_load_f(save_load_fd.data, io, read, text);
    if (save_load_fd.base)
      save_load_fd.base->save_load(io, read, text);
  }

sensitivity()

template<class T, class E>

float LEARNER::learner< T, E >::sensitivity ( example &  ec, size_t  i = 0  )

inline

Definition at line 242 of file learner.h.

Referenced by dis_test(), find_cost_range(), VW::cb_explore_adf::regcb::cb_explore_adf_regcb::get_cost_ranges(), predict_or_learn_active(), predict_or_learn_simulation(), predict_or_learn_with_confidence(), LEARNER::recur_sensitivity(), and sensitivity().

  {
    increment_offset(ec, increment, i);
    const float ret = sensitivity_fd.sensitivity_f(sensitivity_fd.data, *learn_fd.base, ec);
    decrement_offset(ec, increment, i);
    return ret;
  }

set_end_examples()

template<class T, class E>

void LEARNER::learner< T, E >::set_end_examples ( void(*)(T &)  f )

inline

Definition at line 295 of file learner.h.

Referenced by audit_regressor_setup(), lda_setup(), and Search::setup().

{ end_examples_fd = tuple_dbf(learn_fd.data, learn_fd.base, (func_data::fn)f); }

set_end_pass()

template<class T, class E>

void LEARNER::learner< T, E >::set_end_pass ( void(*)(T &)  f )

inline

Definition at line 286 of file learner.h.

Referenced by CSOAA::csldf_setup(), ExpReplay::expreplay_setup(), gd_mf_setup(), lda_setup(), lrq_setup(), memory_tree_setup(), nn_setup(), GD::setup(), Search::setup(), and stagewise_poly_setup().

{ end_pass_fd = tuple_dbf(learn_fd.data, learn_fd.base, (func_data::fn)f); }

set_finish()

template<class T, class E>

void LEARNER::learner< T, E >::set_finish ( void(*)(T &)  f )

inline

Definition at line 265 of file learner.h.

Referenced by audit_regressor_setup(), explore_eval_setup(), mf_setup(), Search::setup(), and warm_cb_setup().

{ finisher_fd = tuple_dbf(learn_fd.data, learn_fd.base, (finish_fptr_type)(f)); }

set_finish_example()

template<class T, class E>

void LEARNER::learner< T, E >::set_finish_example ( void(*)(vw &all, T &, E &)  f )

inline

Definition at line 307 of file learner.h.

Referenced by active_setup(), audit_regressor_setup(), bs_setup(), cb_adf_setup(), cb_algs_setup(), cb_explore_setup(), cbifyldf_setup(), CCB::ccb_explore_adf_setup(), confidence_setup(), cs_active_setup(), CSOAA::csldf_setup(), explore_eval_setup(), LEARNER::init_cost_sensitive_learner(), LEARNER::init_multiclass_learner(), lda_setup(), multilabel_oaa_setup(), mwt_setup(), nn_setup(), oaa_setup(), OjaNewton_setup(), Search::setup(), and stagewise_poly_setup().

  {
    finish_example_fd.data = learn_fd.data;
    finish_example_fd.finish_example_f = (end_fptr_type)(f);
  }

set_init_driver()

template<class T, class E>

void LEARNER::learner< T, E >::set_init_driver ( void(*)(T &)  f )

inline

Definition at line 299 of file learner.h.

Referenced by audit_regressor_setup().

{ init_fd = tuple_dbf(learn_fd.data, learn_fd.base, (func_data::fn)f); }

set_learn()

template<class T, class E>

template<class L >

void LEARNER::learner< T, E >::set_learn ( void(*)(T &, L &, E &)  u )

inline

Definition at line 212 of file learner.h.

Referenced by oaa_setup().

  {
    learn_fd.learn_f = (learn_data::fn)u;
  }

set_multipredict()

template<class T, class E>

template<class L >

void LEARNER::learner< T, E >::set_multipredict ( void(*)(T &, L &, E &, size_t, size_t, polyprediction *, bool)  u )

inline

Definition at line 217 of file learner.h.

Referenced by nn_setup(), scorer_setup(), and GD::setup().

  {
    learn_fd.multipredict_f = (learn_data::multi_fn)u;
  }

set_predict()

template<class T, class E>

template<class L >

void LEARNER::learner< T, E >::set_predict ( void(*)(T &, L &, E &)  u )

inline

Definition at line 207 of file learner.h.

  {
    learn_fd.predict_f = (learn_data::fn)u;
  }

set_save_load()

template<class T, class E>

void LEARNER::learner< T, E >::set_save_load ( void(*)(T &, io_buf &, bool, bool)  sl )

inline

Definition at line 257 of file learner.h.

Referenced by boosting_setup(), cb_adf_setup(), gd_mf_setup(), kernel_svm_setup(), lda_setup(), memory_tree_setup(), mwt_setup(), OjaNewton_setup(), recall_tree_setup(), GD::setup(), stagewise_poly_setup(), and svrg_setup().

  {
    save_load_fd.save_load_f = (save_load_data::fn)sl;
    save_load_fd.data = learn_fd.data;
    save_load_fd.base = learn_fd.base;
  }

set_sensitivity()

template<class T, class E>

void LEARNER::learner< T, E >::set_sensitivity ( float(*)(T &data, base_learner< T, E > &base, example &)  u )

inline

Definition at line 237 of file learner.h.

Referenced by baseline_setup(), and GD::setup().

  {
    sensitivity_fd.data = learn_fd.data;
    sensitivity_fd.sensitivity_f = (sensitivity_data::fn)u;
  }

set_update()

template<class T, class E>

template<class L >

void LEARNER::learner< T, E >::set_update ( void(*)(T &data, L &base, E &)  u )

inline

Definition at line 231 of file learner.h.

Referenced by scorer_setup(), and GD::setup().

  {
    learn_fd.update_f = (learn_data::fn)u;
  }

update()

template<class T, class E>

void LEARNER::learner< T, E >::update ( E &  ec, size_t  i = 0  )

inline

Definition at line 222 of file learner.h.

Referenced by predict_or_learn(), predict_or_learn_multi(), and update().

  {
    assert((is_multiline && std::is_same<multi_ex, E>::value) ||
        (!is_multiline && std::is_same<example, E>::value));  // sanity check under debug compile
    increment_offset(ec, increment, i);
    learn_fd.update_f(learn_fd.data, *learn_fd.base, (void*)&ec);
    decrement_offset(ec, increment, i);
  }

Member Data Documentation

end_examples_fd

template<class T, class E>

func_data LEARNER::learner< T, E >::end_examples_fd

private

Definition at line 145 of file learner.h.

Referenced by LEARNER::learner< CB_EXPLORE::cb_explore, example >::init_learner().

end_pass_fd

template<class T, class E>

func_data LEARNER::learner< T, E >::end_pass_fd

private

Definition at line 144 of file learner.h.

Referenced by LEARNER::learner< CB_EXPLORE::cb_explore, example >::init_learner().

finish_example_fd

template<class T, class E>

finish_example_data LEARNER::learner< T, E >::finish_example_fd

private

Definition at line 142 of file learner.h.

Referenced by LEARNER::learner< CB_EXPLORE::cb_explore, example >::init_learner().

finisher_fd

template<class T, class E>

func_data LEARNER::learner< T, E >::finisher_fd

private

Definition at line 146 of file learner.h.

Referenced by LEARNER::learner< CB_EXPLORE::cb_explore, example >::init_learner().

increment

template<class T, class E>

size_t LEARNER::learner< T, E >::increment

Definition at line 153 of file learner.h.

Referenced by init_driver(), LEARNER::learner< CB_EXPLORE::cb_explore, example >::init_learner(), and parse_sources().

init_fd

template<class T, class E>

func_data LEARNER::learner< T, E >::init_fd

private

Definition at line 139 of file learner.h.

Referenced by LEARNER::learner< CB_EXPLORE::cb_explore, example >::init_learner().

is_multiline

template<class T, class E>

bool LEARNER::learner< T, E >::is_multiline

Definition at line 154 of file learner.h.

Referenced by LEARNER::as_multiline(), LEARNER::as_singleline(), LEARNER::generic_driver_onethread(), and LEARNER::learner< CB_EXPLORE::cb_explore, example >::init_learner().

learn_fd

template<class T, class E>

learn_data LEARNER::learner< T, E >::learn_fd

private

Definition at line 140 of file learner.h.

Referenced by LEARNER::learner< CB_EXPLORE::cb_explore, example >::init_learner().

learner_data

template<class T, class E>

std::shared_ptr<void> LEARNER::learner< T, E >::learner_data

private

Definition at line 148 of file learner.h.

Referenced by LEARNER::learner< CB_EXPLORE::cb_explore, example >::init_learner().

pred_type

template<class T, class E>

prediction_type::prediction_type_t LEARNER::learner< T, E >::pred_type

Definition at line 149 of file learner.h.

Referenced by classweight_setup(), and LEARNER::learner< CB_EXPLORE::cb_explore, example >::init_learner().

save_load_fd

template<class T, class E>

save_load_data LEARNER::learner< T, E >::save_load_fd

private

Definition at line 143 of file learner.h.

Referenced by LEARNER::learner< CB_EXPLORE::cb_explore, example >::init_learner().

sensitivity_fd

template<class T, class E>

sensitivity_data LEARNER::learner< T, E >::sensitivity_fd

private

Definition at line 141 of file learner.h.

Referenced by LEARNER::learner< CB_EXPLORE::cb_explore, example >::init_learner().

weights

template<class T, class E>

size_t LEARNER::learner< T, E >::weights

Definition at line 152 of file learner.h.

Referenced by init_driver(), and LEARNER::learner< CB_EXPLORE::cb_explore, example >::init_learner().

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The documentation for this struct was generated from the following files:

• /mnt/c/w/linux/vowpal_wabbit/vowpalwabbit/cb_explore.h
• /mnt/c/w/linux/vowpal_wabbit/vowpalwabbit/learner.h