Defines the interface for a learning algorithm. More...

#include <learner.h>

Inheritance diagram for VW::LEARNER::learner:

Public Member Functions
void *	get_internal_type_erased_data_pointer_test_use_only ()

std::shared_ptr< void >	get_internal_type_erased_data_pointer_test_use_only_shared ()

void	set_internal_type_erased_data_pointer_does_not_override_funcs (std::shared_ptr< void > data)

void	learn (polymorphic_ex ec, size_t i=0)
	Will update the model according to the labels and examples supplied. More...

void	predict (polymorphic_ex ec, size_t i=0)
	Make a prediction for the given example. More...

void	multipredict (polymorphic_ex ec, size_t lo, size_t count, polyprediction *pred, bool finalize_predictions)

void	update (polymorphic_ex ec, size_t i=0)

float	sensitivity (example &ec, size_t i=0)

void	save_load (io_buf &io, const bool read, const bool text)

void	pre_save_load (VW::workspace &all)

void	persist_metrics (metric_sink &metrics)

void	finish ()

void	end_pass ()

void	end_examples ()

void	init_driver ()

void	finish_example (VW::workspace &all, polymorphic_ex ec)

void	update_stats (const VW::workspace &all, VW::shared_data &sd, const polymorphic_ex ec, VW::io::logger &logger)

void	update_stats (VW::workspace &all, const polymorphic_ex ec)

void	output_example_prediction (VW::workspace &all, const polymorphic_ex ec, VW::io::logger &logger)

void	output_example_prediction (VW::workspace &all, const polymorphic_ex ec)

void	print_update (VW::workspace &all, VW::shared_data &sd, const polymorphic_ex ec, VW::io::logger &logger)

void	print_update (VW::workspace &all, const polymorphic_ex ec)

void	cleanup_example (polymorphic_ex ec)

void	get_enabled_learners (std::vector< std::string > &enabled_learners) const

learner *	get_learner_by_name_prefix (const std::string &learner_name)

void	merge (const std::vector< float > &per_model_weighting, const std::vector< const VW::workspace * > &all_workspaces, const std::vector< const learner * > &all_learners, VW::workspace &output_workspace, learner &output_learner)

void	add (const VW::workspace &base_ws, const VW::workspace &delta_ws, const learner base_l, const learner delta_l, VW::workspace &output_ws, learner *output_l)

void	subtract (const VW::workspace &ws1, const VW::workspace &ws2, const learner l1, const learner l2, VW::workspace &output_ws, learner *output_l)

bool	has_legacy_finish () const

bool	has_update_stats () const

bool	has_print_update () const

bool	has_output_example_prediction () const

bool	has_cleanup_example () const

bool	has_merge () const

bool	has_add () const

bool	has_subtract () const

prediction_type_t	get_output_prediction_type () const

prediction_type_t	get_input_prediction_type () const

label_type_t	get_output_label_type () const

label_type_t	get_input_label_type () const

bool	is_multiline () const

const std::string &	get_name () const

const learner *	get_base_learner () const

learner *	get_base_learner ()

bool	learner_defines_own_save_load ()

Public Attributes
size_t	feature_width = 1

size_t	feature_width_below = 1

bool	learn_returns_prediction = false

Friends
template<class FluentBuilderT , class DataT , class ExampleT >
class	common_learner_builder

template<class DataT , class ExampleT >
class	bottom_learner_builder

template<class DataT , class ExampleT >
class	reduction_learner_builder

template<class ExampleT >
class	reduction_no_data_learner_builder

Detailed Description

Defines the interface for a learning algorithm.

VW has two types of learners: reduction learners and bottom learners. The same learner class is used to implement both. A reduction stack is created as a chain of learners, starting from a bottom learner at the bottom of the stack. Each learner after it is a reduction learner and holds a shared_ptr to its base, the learner immediately below it in the stack. The final VW workspace holds a shared_ptr to the topmost learner. The difference between a reduction and a bottom learner is that a reduction will recursively call its base, whereas a bottom learner has no base and will simply return its result.

The learner class is not meant to be inherited from. Instead, it implements a sort of virtual inheritance via std::function objects. Each type of learner can have its own data object that stores the learner's internal state. When a learner is created, the function objects are bound to the data object, and the data object is stored in a std::shared_ptr<void>. The end result is that the data type representing the learner's state is type-erased, thus allowing for arbitrary learner types to be implemented by the same learner class.

The learner class itself has a private constructor. Learners should be created only through the make_reduction_learner and make_bottom_learner functions and their associated learner builder template classes. The templates enforce consistency of types at compile time, before types-erasure occurs to make the acutal learner object.

Member Function Documentation

◆ add()

void VW::LEARNER::learner::add	(	const VW::workspace &	base_ws,
		const VW::workspace &	delta_ws,
		const learner *	base_l,
		const learner *	delta_l,
		VW::workspace &	output_ws,
		learner *	output_l
	)

◆ cleanup_example()

void VW::LEARNER::learner::cleanup_example ( polymorphic_ex ec )

◆ end_examples()

void VW::LEARNER::learner::end_examples ( )

◆ end_pass()

void VW::LEARNER::learner::end_pass ( )

◆ finish()

void VW::LEARNER::learner::finish ( )

◆ finish_example()

void VW::LEARNER::learner::finish_example	(	VW::workspace &	all,
		polymorphic_ex	ec
	)

◆ get_base_learner() [1/2]

learner * VW::LEARNER::learner::get_base_learner ( )

inline

◆ get_base_learner() [2/2]

const learner * VW::LEARNER::learner::get_base_learner ( ) const

inline

◆ get_enabled_learners()

void VW::LEARNER::learner::get_enabled_learners ( std::vector< std::string > & enabled_learners ) const

◆ get_input_label_type()

label_type_t VW::LEARNER::learner::get_input_label_type ( ) const

inline

◆ get_input_prediction_type()

prediction_type_t VW::LEARNER::learner::get_input_prediction_type ( ) const

inline

◆ get_internal_type_erased_data_pointer_test_use_only()

void * VW::LEARNER::learner::get_internal_type_erased_data_pointer_test_use_only ( )

inline

◆ get_internal_type_erased_data_pointer_test_use_only_shared()

std::shared_ptr< void > VW::LEARNER::learner::get_internal_type_erased_data_pointer_test_use_only_shared ( )

inline

◆ get_learner_by_name_prefix()

learner * VW::LEARNER::learner::get_learner_by_name_prefix ( const std::string & learner_name )

◆ get_name()

const std::string & VW::LEARNER::learner::get_name ( ) const

inline

◆ get_output_label_type()

label_type_t VW::LEARNER::learner::get_output_label_type ( ) const

inline

◆ get_output_prediction_type()

prediction_type_t VW::LEARNER::learner::get_output_prediction_type ( ) const

inline

◆ has_add()

bool VW::LEARNER::learner::has_add ( ) const

inline

◆ has_cleanup_example()

bool VW::LEARNER::learner::has_cleanup_example ( ) const

inline

◆ has_legacy_finish()

bool VW::LEARNER::learner::has_legacy_finish ( ) const

inline

◆ has_merge()

bool VW::LEARNER::learner::has_merge ( ) const

inline

◆ has_output_example_prediction()

bool VW::LEARNER::learner::has_output_example_prediction ( ) const

inline

◆ has_print_update()

bool VW::LEARNER::learner::has_print_update ( ) const

inline

◆ has_subtract()

bool VW::LEARNER::learner::has_subtract ( ) const

inline

◆ has_update_stats()

bool VW::LEARNER::learner::has_update_stats ( ) const

inline

◆ init_driver()

void VW::LEARNER::learner::init_driver ( )

◆ is_multiline()

bool VW::LEARNER::learner::is_multiline ( ) const

inline

◆ learn()

void VW::LEARNER::learner::learn	(	polymorphic_ex	ec,
		size_t	i = `0`
	)

Will update the model according to the labels and examples supplied.

Parameters

ec	The example object or multi_ex to be operated on. This object must have a valid label set for every example in the field example::l that corresponds to the type this learner expects.
i	This is the offset used for the feature_width in this call. If using multiple regressors/learners you can feature_width_below this value for each call.

Returns: While some learner may fill the example::pred, this is not guaranteed and is undefined behavior if accessed.

◆ learner_defines_own_save_load()

bool VW::LEARNER::learner::learner_defines_own_save_load ( )

inline

◆ merge()

void VW::LEARNER::learner::merge	(	const std::vector< float > &	per_model_weighting,
		const std::vector< const VW::workspace * > &	all_workspaces,
		const std::vector< const learner * > &	all_learners,
		VW::workspace &	output_workspace,
		learner &	output_learner
	)

◆ multipredict()

void VW::LEARNER::learner::multipredict	(	polymorphic_ex	ec,
		size_t	lo,
		size_t	count,
		polyprediction *	pred,
		bool	finalize_predictions
	)

◆ output_example_prediction() [1/2]

void VW::LEARNER::learner::output_example_prediction	(	VW::workspace &	all,
		const polymorphic_ex	ec
	)

◆ output_example_prediction() [2/2]

void VW::LEARNER::learner::output_example_prediction	(	VW::workspace &	all,
		const polymorphic_ex	ec,
		VW::io::logger &	logger
	)

◆ persist_metrics()

void VW::LEARNER::learner::persist_metrics ( metric_sink & metrics )

◆ pre_save_load()

void VW::LEARNER::learner::pre_save_load ( VW::workspace & all )

◆ predict()

void VW::LEARNER::learner::predict	(	polymorphic_ex	ec,
		size_t	i = `0`
	)

Make a prediction for the given example.

Parameters

ec	The example object or multi_ex to be operated on. This object must have a valid prediction allocated in the field example::pred that corresponds to this learner type.
i	This is the offset used for the feature_width in this call. If using multiple regressors/learners you can feature_width_below this value for each call.

Returns: The prediction calculated by this learner be set on example::pred. If the polymorphic_ex is multi_ex then the prediction is set on the 0th item in the list.

◆ print_update() [1/2]

void VW::LEARNER::learner::print_update	(	VW::workspace &	all,
		const polymorphic_ex	ec
	)

◆ print_update() [2/2]

void VW::LEARNER::learner::print_update	(	VW::workspace &	all,
		VW::shared_data &	sd,
		const polymorphic_ex	ec,
		VW::io::logger &	logger
	)

◆ save_load()

void VW::LEARNER::learner::save_load	(	io_buf &	io,
		const bool	read,
		const bool	text
	)

◆ sensitivity()

float VW::LEARNER::learner::sensitivity	(	example &	ec,
		size_t	i = `0`
	)

◆ set_internal_type_erased_data_pointer_does_not_override_funcs()

void VW::LEARNER::learner::set_internal_type_erased_data_pointer_does_not_override_funcs ( std::shared_ptr< void > data )

inline

◆ subtract()

void VW::LEARNER::learner::subtract	(	const VW::workspace &	ws1,
		const VW::workspace &	ws2,
		const learner *	l1,
		const learner *	l2,
		VW::workspace &	output_ws,
		learner *	output_l
	)

◆ update()

void VW::LEARNER::learner::update	(	polymorphic_ex	ec,
		size_t	i = `0`
	)

◆ update_stats() [1/2]

void VW::LEARNER::learner::update_stats	(	const VW::workspace &	all,
		VW::shared_data &	sd,
		const polymorphic_ex	ec,
		VW::io::logger &	logger
	)

◆ update_stats() [2/2]

void VW::LEARNER::learner::update_stats	(	VW::workspace &	all,
		const polymorphic_ex	ec
	)

Friends And Related Function Documentation

◆ bottom_learner_builder

template<class DataT , class ExampleT >

friend class bottom_learner_builder

friend

◆ common_learner_builder

template<class FluentBuilderT , class DataT , class ExampleT >

friend class common_learner_builder

friend

◆ reduction_learner_builder

template<class DataT , class ExampleT >

friend class reduction_learner_builder

friend

◆ reduction_no_data_learner_builder

template<class ExampleT >

friend class reduction_no_data_learner_builder

friend

Member Data Documentation

◆ feature_width

size_t VW::LEARNER::learner::feature_width = 1

◆ feature_width_below

size_t VW::LEARNER::learner::feature_width_below = 1

◆ learn_returns_prediction

bool VW::LEARNER::learner::learn_returns_prediction = false

The documentation for this class was generated from the following file:

/nix/store/lp9wjr07fg70x7djxjnivn11qk52df1s-source/vowpalwabbit/core/include/vw/core/learner.h

Public Member Functions

Public Attributes

Friends

Detailed Description

Member Function Documentation

◆ add()

◆ cleanup_example()

◆ end_examples()

◆ end_pass()

◆ finish()

◆ finish_example()

◆ get_base_learner() [1/2]

◆ get_base_learner() [2/2]

◆ get_enabled_learners()

◆ get_input_label_type()

◆ get_input_prediction_type()

◆ get_internal_type_erased_data_pointer_test_use_only()

◆ get_internal_type_erased_data_pointer_test_use_only_shared()

◆ get_learner_by_name_prefix()

◆ get_name()

◆ get_output_label_type()

◆ get_output_prediction_type()

◆ has_add()

◆ has_cleanup_example()

◆ has_legacy_finish()

◆ has_merge()

◆ has_output_example_prediction()

◆ has_print_update()

◆ has_subtract()

◆ has_update_stats()

◆ init_driver()

◆ is_multiline()

◆ learn()

◆ learner_defines_own_save_load()

◆ merge()

◆ multipredict()

◆ output_example_prediction() [1/2]

◆ output_example_prediction() [2/2]

◆ persist_metrics()

◆ pre_save_load()

◆ predict()

◆ print_update() [1/2]

◆ print_update() [2/2]

◆ save_load()

◆ sensitivity()

◆ set_internal_type_erased_data_pointer_does_not_override_funcs()

◆ subtract()

◆ update()

◆ update_stats() [1/2]

◆ update_stats() [2/2]

Friends And Related Function Documentation

◆ bottom_learner_builder

◆ common_learner_builder

◆ reduction_learner_builder

◆ reduction_no_data_learner_builder

Member Data Documentation

◆ feature_width

◆ feature_width_below

◆ learn_returns_prediction