txeo::Trainer< T > Class Template Reference

Abstract base class for machine learning trainers. More...

#include <Trainer.h>

Inheritance diagram for txeo::Trainer< T >:

Collaboration diagram for txeo::Trainer< T >:

Public Member Functions
	Trainer (const Trainer &)=delete
	Trainer (Trainer &&)=delete
Trainer &	operator= (const Trainer &)=delete
Trainer &	operator= (Trainer &&)=delete
virtual	~Trainer ()=default
	Trainer (txeo::DataTable< T > &&data, txeo::Logger &logger=txeo::LoggerConsole::instance())
	Construct a new Trainer object from a data table.
	Trainer (const txeo::DataTable< T > &data)
virtual void	fit (size_t epochs, txeo::LossFunc metric)
	Trains the model for specified number of epochs.
virtual void	fit (size_t epochs, txeo::LossFunc metric, size_t patience)
	Trains with early stopping based on validation performance.
virtual void	fit (size_t epochs, txeo::LossFunc metric, size_t patience, txeo::NormalizationType type)
	Trains with early stopping based on validation performance and feature normalization.
T	compute_test_loss (txeo::LossFunc metric) const
	Evaluates test data based on a specified metric.
virtual txeo::Tensor< T >	predict (const txeo::Tensor< T > &input) const =0
	Makes predictions using the trained model (pure virtual)
bool	is_trained () const
	Checks if model has been trained.
const txeo::DataTable< T > &	data_table () const
	Returns the data table of this trainer.
void	enable_feature_norm (txeo::NormalizationType type)
	Enable feature normalization.
void	disable_feature_norm ()
	Disable feature normalization.

Protected Member Functions
	Trainer ()=default
virtual void	train (size_t epochs, txeo::LossFunc loss_func)=0

Protected Attributes
bool	_is_trained {false}
bool	_is_early_stop {false}
size_t	_patience {0}
std::unique_ptr< txeo::DataTable< T > >	_data_table
txeo::Logger *	_logger {nullptr}
txeo::DataTableNorm< T >	_data_table_norm
bool	_is_norm_enabled {false}

Detailed Description

template<typename T>
class txeo::Trainer< T >

Abstract base class for machine learning trainers.

Template Parameters

T	Numeric type for tensor elements (e.g., float, double)

This class provides the core interface for training machine learning models with:

• Training/evaluation data management
• Common training parameters
• Basic training lifecycle control

Derived classes must implement the prediction logic and training algorithm.

Definition at line 32 of file Trainer.h.

Constructor & Destructor Documentation

Trainer() [1/5]

template<typename T >

txeo::Trainer< T >::Trainer ( const Trainer< T > &  )

delete

Trainer() [2/5]

template<typename T >

txeo::Trainer< T >::Trainer ( Trainer< T > &&  )

delete

~Trainer()

template<typename T >

virtual txeo::Trainer< T >::~Trainer ( )

virtualdefault

Trainer() [3/5]

template<typename T >

txeo::Trainer< T >::Trainer ( txeo::DataTable< T > &&  data, txeo::Logger &  logger = txeo::LoggerConsole::instance()  )

inline

Construct a new Trainer object from a data table.

Parameters

data	Training/Evaluation/Test data.

Definition at line 45 of file Trainer.h.

        : _data_table{std::make_unique<txeo::DataTable<T>>(std::move(data))}, _logger{&logger} {};

Trainer() [4/5]

template<typename T >

txeo::Trainer< T >::Trainer ( const txeo::DataTable< T > &  data )

inline

Definition at line 48 of file Trainer.h.

: Trainer{data.clone()} {};

Trainer() [5/5]

template<typename T >

txeo::Trainer< T >::Trainer ( )

protecteddefault

Member Function Documentation

compute_test_loss()

template<typename T >

T txeo::Trainer< T >::compute_test_loss

(

txeo::LossFunc

metric

)

const

Evaluates test data based on a specified metric.

@ throws txeo::TrainerError

Parameters

metric

Loss function type

Returns

T Loss value

data_table()

template<typename T >

const txeo::DataTable< T > & txeo::Trainer< T >::data_table ( ) const

inline

Returns the data table of this trainer.

Returns

const txeo::DataTable<T>&

Definition at line 112 of file Trainer.h.

{ return *_data_table; }

disable_feature_norm()

template<typename T >

void txeo::Trainer< T >::disable_feature_norm ( )

inline

Disable feature normalization.

Parameters

type	Type of normalization

Definition at line 126 of file Trainer.h.

{ _is_norm_enabled = false; };

enable_feature_norm()

template<typename T >

void txeo::Trainer< T >::enable_feature_norm

(

txeo::NormalizationType

type

)

Enable feature normalization.

Parameters

type	Type of normalization

fit() [1/3]

template<typename T >

virtual void txeo::Trainer< T >::fit ( size_t  epochs, txeo::LossFunc  metric  )

virtual

Trains the model for specified number of epochs.

Parameters

epochs	Number of training iterations
metric	Loss function to optimize

fit() [2/3]

template<typename T >

virtual void txeo::Trainer< T >::fit ( size_t  epochs, txeo::LossFunc  metric, size_t  patience  )

virtual

Trains with early stopping based on validation performance.

Parameters

epochs	Maximum number of training iterations
metric	Loss function to optimize
patience	Number of epochs to wait without improvement before stopping

fit() [3/3]

template<typename T >

virtual void txeo::Trainer< T >::fit ( size_t  epochs, txeo::LossFunc  metric, size_t  patience, txeo::NormalizationType  type  )

virtual

Trains with early stopping based on validation performance and feature normalization.

Parameters

epochs	Maximum number of training iterations
metric	Loss function to optimize
patience	Number of epochs to wait without improvement before stopping
type	Type of normalization

is_trained()

template<typename T >

bool txeo::Trainer< T >::is_trained ( ) const

inline

Checks if model has been trained.

Returns

true if training has been completed, false otherwise

Definition at line 105 of file Trainer.h.

{ return _is_trained; }

operator=() [1/2]

template<typename T >

Trainer & txeo::Trainer< T >::operator= ( const Trainer< T > &  )

delete

operator=() [2/2]

template<typename T >

Trainer & txeo::Trainer< T >::operator= ( Trainer< T > &&  )

delete

predict()

template<typename T >

virtual txeo::Tensor< T > txeo::Trainer< T >::predict ( const txeo::Tensor< T > &  input ) const

pure virtual

Makes predictions using the trained model (pure virtual)

Parameters

input

Input tensor for prediction (shape: [samples, features])

Returns

Prediction tensor (shape: [samples, outputs])

Exceptions

txeo::TrainerError

Note

Must be implemented in derived classes

Implemented in txeo::OlsGDTrainer< T >.

train()

template<typename T >

virtual void txeo::Trainer< T >::train ( size_t  epochs, txeo::LossFunc  loss_func  )

protectedpure virtual

Member Data Documentation

_data_table

template<typename T >

std::unique_ptr<txeo::DataTable<T> > txeo::Trainer< T >::_data_table

protected

Definition at line 135 of file Trainer.h.

_data_table_norm

template<typename T >

txeo::DataTableNorm<T> txeo::Trainer< T >::_data_table_norm

protected

Definition at line 138 of file Trainer.h.

_is_early_stop

template<typename T >

bool txeo::Trainer< T >::_is_early_stop {false}

protected

Definition at line 132 of file Trainer.h.

{false};

_is_norm_enabled

template<typename T >

bool txeo::Trainer< T >::_is_norm_enabled {false}

protected

Definition at line 139 of file Trainer.h.

{false};

_is_trained

template<typename T >

bool txeo::Trainer< T >::_is_trained {false}

protected

Definition at line 131 of file Trainer.h.

{false};

_logger

template<typename T >

txeo::Logger* txeo::Trainer< T >::_logger {nullptr}

protected

Definition at line 136 of file Trainer.h.

{nullptr};

_patience

template<typename T >

size_t txeo::Trainer< T >::_patience {0}

protected

Definition at line 133 of file Trainer.h.

{0};

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

• include/txeo/Trainer.h