learner.h File Reference

#include <iostream>
#include <memory>
#include "memory.h"
#include "multiclass.h"
#include "simple_label.h"
#include "parser.h"
#include "debug_log.h"
#include "future_compat.h"
#include "example.h"
#include "scope_exit.h"
#include "metric_sink.h"

Classes
struct	VW::LEARNER::learner< T, E >
	Defines the interface for a learning algorithm. More...
struct	VW::LEARNER::func_data
struct	VW::LEARNER::learn_data
struct	VW::LEARNER::sensitivity_data
struct	VW::LEARNER::save_load_data
struct	VW::LEARNER::save_metric_data
struct	VW::LEARNER::finish_example_data
struct	VW::LEARNER::learner< T, E >
	Defines the interface for a learning algorithm. More...
struct	VW::LEARNER::common_learner_builder< FluentBuilderT, DataT, ExampleT, BaseLearnerT >
struct	VW::LEARNER::reduction_learner_builder< DataT, ExampleT, BaseLearnerT >
struct	VW::LEARNER::reduction_no_data_learner_builder< ExampleT, BaseLearnerT >
struct	VW::LEARNER::base_learner_builder< DataT, ExampleT >

Namespaces
	VW
	VW::LEARNER
	Contains the VW::LEARNER::learner object and utilities for interacting with it.

Macros
#define	VW_DEBUG_LOG   vw_dbg::learner

Typedefs
using	VW::LEARNER::base_learner = learner< char, char >
	Used to type erase the object and pass around common type. More...
using	VW::LEARNER::single_learner = learner< char, example >
	Used for reductions that process single example objects at at time. It type erases the specific reduction object type. More...
using	VW::LEARNER::multi_learner = learner< char, multi_ex >
	Used for multiline examples where there are several example objects required to describe the overall example. It type erases the specific reduction object type. More...

Enumerations
enum	prediction_type_t {   prediction_type_t::scalar, prediction_type_t::scalars, prediction_type_t::action_scores, prediction_type_t::pdf,   prediction_type_t::action_probs, prediction_type_t::multiclass, prediction_type_t::multilabels, prediction_type_t::prob,   prediction_type_t::multiclassprobs, prediction_type_t::decision_probs, prediction_type_t::action_pdf_value, prediction_type_t::active_multiclass }

Functions
const char *	to_string (prediction_type_t prediction_type)
func_data	VW::LEARNER::tuple_dbf (void *data, base_learner *base, void(*func)(void *))
void	VW::LEARNER::generic_driver (vw &all)
void	VW::LEARNER::generic_driver (const std::vector< vw * > &alls)
void	VW::LEARNER::generic_driver_onethread (vw &all)
void	VW::LEARNER::noop_save_load (void *, io_buf &, bool, bool)
void	VW::LEARNER::noop_persist_metrics (void *, metric_sink &)
void	VW::LEARNER::noop (void *)
float	VW::LEARNER::noop_sensitivity (void *, base_learner &, example &)
float	VW::LEARNER::recur_sensitivity (void *, base_learner &, example &)
void	VW::LEARNER::debug_increment_depth (example &ex)
void	VW::LEARNER::debug_increment_depth (multi_ex &ec_seq)
void	VW::LEARNER::debug_decrement_depth (example &ex)
void	VW::LEARNER::debug_decrement_depth (multi_ex &ec_seq)
void	VW::LEARNER::increment_offset (example &ex, const size_t increment, const size_t i)
void	VW::LEARNER::increment_offset (multi_ex &ec_seq, const size_t increment, const size_t i)
void	VW::LEARNER::decrement_offset (example &ex, const size_t increment, const size_t i)
void	VW::LEARNER::decrement_offset (multi_ex &ec_seq, const size_t increment, const size_t i)
bool	VW::LEARNER::ec_is_example_header (example const &ec, label_type_t label_type)
template<class T , class E , class L >
learner< T, E > &	VW::LEARNER::init_learner (free_ptr< T > &dat, L *base, void(*learn)(T &, L &, E &), void(*predict)(T &, L &, E &), size_t ws, prediction_type_t pred_type, const std::string &name, bool learn_returns_prediction=false)
template<class T , class E , class L >
learner< T, E > &	VW::LEARNER::init_learner (free_ptr< T > &dat, void(*learn)(T &, L &, E &), void(*predict)(T &, L &, E &), size_t params_per_weight, const std::string &name, bool learn_returns_prediction=false)
template<class T , class E , class L >
learner< T, E > &	VW::LEARNER::init_learner (void(*predict)(T &, L &, E &), size_t params_per_weight, const std::string &name)
template<class T , class E , class L >
learner< T, E > &	VW::LEARNER::init_learner (free_ptr< T > &dat, void(*learn)(T &, L &, E &), void(*predict)(T &, L &, E &), size_t params_per_weight, prediction_type_t pred_type, const std::string &name, bool learn_returns_prediction=false)
template<class T , class E , class L >
learner< T, E > &	VW::LEARNER::init_learner (free_ptr< T > &dat, L *base, void(*learn)(T &, L &, E &), void(*predict)(T &, L &, E &), size_t ws, const std::string &name, bool learn_returns_prediction=false)
template<class T , class E , class L >
learner< T, E > &	VW::LEARNER::init_learner (free_ptr< T > &dat, L *base, void(*learn)(T &, L &, E &), void(*predict)(T &, L &, E &), const std::string &name, bool learn_returns_prediction=false)
template<class T , class E , class L >
learner< T, E > &	VW::LEARNER::init_learner (L *base, void(*learn)(T &, L &, E &), void(*predict)(T &, L &, E &), const std::string &name, bool learn_returns_prediction=false)
template<class T , class E , class L >
learner< T, E > &	VW::LEARNER::init_multiclass_learner (free_ptr< T > &dat, L *base, void(*learn)(T &, L &, E &), void(*predict)(T &, L &, E &), parser *p, size_t ws, const std::string &name, prediction_type_t pred_type=prediction_type_t::multiclass, bool learn_returns_prediction=false)
template<class T , class E , class L >
learner< T, E > &	VW::LEARNER::init_cost_sensitive_learner (free_ptr< T > &dat, L *base, void(*learn)(T &, L &, E &), void(*predict)(T &, L &, E &), parser *p, size_t ws, const std::string &name, prediction_type_t pred_type=prediction_type_t::multiclass, bool learn_returns_prediction=false)
template<class T , class E >
base_learner *	VW::LEARNER::make_base (learner< T, E > &base)
template<class T , class E >
multi_learner *	VW::LEARNER::as_multiline (learner< T, E > *l)
template<class T , class E >
single_learner *	VW::LEARNER::as_singleline (learner< T, E > *l)
template<bool is_learn>
void	VW::LEARNER::multiline_learn_or_predict (multi_learner &base, multi_ex &examples, const uint64_t offset, const uint32_t id=0)
float	VW::LEARNER::noop_sensitivity_base (void *, example &)
template<class DataT , class ExampleT , class BaseLearnerT >
VW_WARNING_STATE_POP reduction_learner_builder< DataT, ExampleT, BaseLearnerT >	VW::LEARNER::make_reduction_learner (std::unique_ptr< DataT > &&data, BaseLearnerT *base, void(*learn_fn)(DataT &, BaseLearnerT &, ExampleT &), void(*predict_fn)(DataT &, BaseLearnerT &, ExampleT &), const std::string &name)
template<class ExampleT , class BaseLearnerT >
reduction_no_data_learner_builder< ExampleT, BaseLearnerT >	VW::LEARNER::make_no_data_reduction_learner (BaseLearnerT *base, void(*learn_fn)(char &, BaseLearnerT &, ExampleT &), void(*predict_fn)(char &, BaseLearnerT &, ExampleT &), const std::string &name)
template<class DataT , class ExampleT >
base_learner_builder< DataT, ExampleT >	VW::LEARNER::make_base_learner (std::unique_ptr< DataT > &&data, void(*learn_fn)(DataT &, base_learner &, ExampleT &), void(*predict_fn)(DataT &, base_learner &, ExampleT &), const std::string &name, prediction_type_t pred_type, label_type_t label_type)

Macro Definition Documentation

#define VW_DEBUG_LOG   vw_dbg::learner

Enumeration Type Documentation

enum prediction_type_t

strong

Enumerator
scalar
scalars
action_scores
pdf
action_probs
multiclass
multilabels
prob
multiclassprobs
decision_probs
action_pdf_value
active_multiclass

Function Documentation

const char* to_string

(

prediction_type_t

prediction_type

)