Trainer for Logistic regression. More...

#include <shark/Algorithms/Trainers/LogisticRegression.h>

Inheritance diagram for shark::LogisticRegression< InputVectorType >:

Public Types
typedef base_type::ModelType	ModelType

typedef base_type::DatasetType	DatasetType

typedef base_type::WeightedDatasetType	WeightedDatasetType

Public Types inherited from shark::AbstractWeightedTrainer< LinearClassifier< InputVectorType > >
typedef base_type::ModelType	ModelType

typedef base_type::InputType	InputType

typedef base_type::LabelType	LabelType

typedef base_type::DatasetType	DatasetType

typedef WeightedLabeledData< InputType, LabelType >	WeightedDatasetType

Public Types inherited from shark::IParameterizable<>
typedef RealVector	ParameterVectorType

Public Member Functions
	LogisticRegression (double lambda1=0, double lambda2=0, bool bias=true, double accuracy=1.e-8)
	Constructor. More...

std::string	name () const
	From INameable: return the class name. More...

double	lambda1 () const
	Return the current setting of the l1-regularization parameter. More...

double	lambda2 () const
	Return the current setting of the l2-regularization parameter. More...

void	setLambda1 (double lambda)
	Set the l1-regularization parameter. More...

void	setLambda2 (double lambda)
	Set the l2-regularization parameter. More...

double	accuracy () const
	Return the current setting of the accuracy (maximal gradient component of the optimization problem). More...

void	setAccuracy (double accuracy)
	Set the accuracy (maximal gradient component of the optimization problem). More...

RealVector	parameterVector () const
	Get the regularization parameters lambda1 and lambda2 through the IParameterizable interface. More...

void	setParameterVector (const RealVector &param)
	Set the regularization parameters lambda1 and lambda2 through the IParameterizable interface. More...

size_t	numberOfParameters () const
	Return the number of parameters (one in this case). More...

void	train (ModelType &model, DatasetType const &dataset)
	Train a linear model with logistic regression. More...

void	train (ModelType &model, WeightedDatasetType const &dataset)
	Train a linear model with logistic regression using weights. More...

Public Member Functions inherited from shark::AbstractWeightedTrainer< LinearClassifier< InputVectorType > >
virtual void	train (ModelType &model, WeightedDatasetType const &dataset)=0
	Executes the algorithm and trains a model on the given weighted data. More...

virtual void	train (ModelType &model, DatasetType const &dataset)
	Executes the algorithm and trains a model on the given unweighted data. More...

Public Member Functions inherited from shark::INameable
virtual	~INameable ()

Public Member Functions inherited from shark::ISerializable
virtual	~ISerializable ()
	Virtual d'tor. More...

virtual void	read (InArchive &archive)
	Read the component from the supplied archive. More...

virtual void	write (OutArchive &archive) const
	Write the component to the supplied archive. More...

void	load (InArchive &archive, unsigned int version)
	Versioned loading of components, calls read(...). More...

void	save (OutArchive &archive, unsigned int version) const
	Versioned storing of components, calls write(...). More...

	BOOST_SERIALIZATION_SPLIT_MEMBER ()

Public Member Functions inherited from shark::IParameterizable<>
virtual	~IParameterizable ()

Detailed Description

template<class InputVectorType = RealVector>
class shark::LogisticRegression< InputVectorType >

Trainer for Logistic regression.

Logistic regression solves the following optimization problem:

\[ \min_{w,b} \sum_i u_i l(y_i,f(x_i^Tw+b)) +\lambda_1 |w|_1 +\lambda_2 |w|^2_2 \]

Where \(l\) is the cross-entropy loss and \(u_i\) are individual weuights for each point(assumed to be 1). Logistic regression is one of the most well known machine learning algorithms for classification using linear models.

The solver is based on LBFGS for the case where no l1-regularization is used. Otherwise the problem is transformed into a constrained problem and the constrined-LBFGS algorithm is used. This is one of the most efficient solvers for logistic regression as long as the number of data points is not too large.

Definition at line 64 of file LogisticRegression.h.

Member Typedef Documentation

◆ DatasetType

template<class InputVectorType = RealVector>

typedef base_type::DatasetType shark::LogisticRegression< InputVectorType >::DatasetType

Definition at line 70 of file LogisticRegression.h.

◆ ModelType

template<class InputVectorType = RealVector>

typedef base_type::ModelType shark::LogisticRegression< InputVectorType >::ModelType

Definition at line 69 of file LogisticRegression.h.

◆ WeightedDatasetType

template<class InputVectorType = RealVector>

typedef base_type::WeightedDatasetType shark::LogisticRegression< InputVectorType >::WeightedDatasetType

Definition at line 71 of file LogisticRegression.h.

Constructor & Destructor Documentation

◆ LogisticRegression()

template<class InputVectorType = RealVector>

shark::LogisticRegression< InputVectorType >::LogisticRegression	(	double	lambda1 = `0`,
		double	lambda2 = `0`,
		bool	bias = `true`,
		double	accuracy = `1.e-8`
	)

inline

Constructor.

Parameters

lambda1	value of the 1-norm regularization parameter (see class description)
lambda2	value of the 2-norm regularization parameter (see class description)
lbias	whether to train with bias or not
accuracy	stopping criterion for the iterative solver, maximal gradient component of the objective function (see class description)

Definition at line 79 of file LogisticRegression.h.

References shark::LogisticRegression< InputVectorType >::accuracy(), shark::LogisticRegression< InputVectorType >::lambda1(), shark::LogisticRegression< InputVectorType >::lambda2(), shark::LogisticRegression< InputVectorType >::setAccuracy(), shark::LogisticRegression< InputVectorType >::setLambda1(), and shark::LogisticRegression< InputVectorType >::setLambda2().

Member Function Documentation

◆ accuracy()

template<class InputVectorType = RealVector>

double shark::LogisticRegression< InputVectorType >::accuracy ( ) const

inline

Return the current setting of the accuracy (maximal gradient component of the optimization problem).

Definition at line 113 of file LogisticRegression.h.

Referenced by shark::LogisticRegression< InputVectorType >::LogisticRegression(), and shark::LogisticRegression< InputVectorType >::setAccuracy().

◆ lambda1()

template<class InputVectorType = RealVector>

double shark::LogisticRegression< InputVectorType >::lambda1 ( ) const

inline

Return the current setting of the l1-regularization parameter.

Definition at line 92 of file LogisticRegression.h.

Referenced by shark::LogisticRegression< InputVectorType >::LogisticRegression().

◆ lambda2()

template<class InputVectorType = RealVector>

double shark::LogisticRegression< InputVectorType >::lambda2 ( ) const

inline

Return the current setting of the l2-regularization parameter.

Definition at line 97 of file LogisticRegression.h.

Referenced by shark::LogisticRegression< InputVectorType >::LogisticRegression().

◆ name()

template<class InputVectorType = RealVector>

std::string shark::LogisticRegression< InputVectorType >::name ( ) const

inlinevirtual

From INameable: return the class name.

Reimplemented from shark::INameable.

Definition at line 87 of file LogisticRegression.h.

◆ numberOfParameters()

template<class InputVectorType = RealVector>

size_t shark::LogisticRegression< InputVectorType >::numberOfParameters ( ) const

inlinevirtual

Return the number of parameters (one in this case).

Reimplemented from shark::IParameterizable<>.

Definition at line 136 of file LogisticRegression.h.

◆ parameterVector()

template<class InputVectorType = RealVector>

RealVector shark::LogisticRegression< InputVectorType >::parameterVector ( ) const

inlinevirtual

Get the regularization parameters lambda1 and lambda2 through the IParameterizable interface.

Reimplemented from shark::IParameterizable<>.

Definition at line 124 of file LogisticRegression.h.

◆ setAccuracy()

template<class InputVectorType = RealVector>

void shark::LogisticRegression< InputVectorType >::setAccuracy ( double accuracy )

inline

Set the accuracy (maximal gradient component of the optimization problem).

Definition at line 118 of file LogisticRegression.h.

References shark::LogisticRegression< InputVectorType >::accuracy(), and SHARK_RUNTIME_CHECK.

Referenced by shark::LogisticRegression< InputVectorType >::LogisticRegression().

◆ setLambda1()

template<class InputVectorType = RealVector>

void shark::LogisticRegression< InputVectorType >::setLambda1 ( double lambda )

inline

Set the l1-regularization parameter.

Definition at line 102 of file LogisticRegression.h.

References lambda, and SHARK_RUNTIME_CHECK.

Referenced by shark::LogisticRegression< InputVectorType >::LogisticRegression(), and shark::LogisticRegression< InputVectorType >::setParameterVector().

◆ setLambda2()

template<class InputVectorType = RealVector>

void shark::LogisticRegression< InputVectorType >::setLambda2 ( double lambda )

inline

Set the l2-regularization parameter.

Definition at line 108 of file LogisticRegression.h.

References lambda, and SHARK_RUNTIME_CHECK.

Referenced by shark::LogisticRegression< InputVectorType >::LogisticRegression(), and shark::LogisticRegression< InputVectorType >::setParameterVector().

◆ setParameterVector()

template<class InputVectorType = RealVector>

void shark::LogisticRegression< InputVectorType >::setParameterVector ( const RealVector & param )

inlinevirtual

Set the regularization parameters lambda1 and lambda2 through the IParameterizable interface.

Reimplemented from shark::IParameterizable<>.

Definition at line 129 of file LogisticRegression.h.

References shark::LogisticRegression< InputVectorType >::setLambda1(), shark::LogisticRegression< InputVectorType >::setLambda2(), and SIZE_CHECK.

◆ train() [1/2]

template<class InputVectorType = RealVector>

void shark::LogisticRegression< InputVectorType >::train	(	ModelType &	model,
		DatasetType const &	dataset
	)

inline

Train a linear model with logistic regression.

Definition at line 141 of file LogisticRegression.h.

◆ train() [2/2]

template<class InputVectorType = RealVector>

void shark::LogisticRegression< InputVectorType >::train	(	ModelType &	model,
		WeightedDatasetType const &	dataset
	)

inline

Train a linear model with logistic regression using weights.

Definition at line 146 of file LogisticRegression.h.

References shark::inputDimension(), and shark::numberOfClasses().

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

include/shark/Algorithms/Trainers/LogisticRegression.h

Public Types

Public Member Functions

Detailed Description

template<class InputVectorType = RealVector> class shark::LogisticRegression< InputVectorType >

Member Typedef Documentation

◆ DatasetType

◆ ModelType

◆ WeightedDatasetType

Constructor & Destructor Documentation

◆ LogisticRegression()

Member Function Documentation

◆ accuracy()

◆ lambda1()

◆ lambda2()

◆ name()

◆ numberOfParameters()

◆ parameterVector()

◆ setAccuracy()

◆ setLambda1()

◆ setLambda2()

◆ setParameterVector()

◆ train() [1/2]

◆ train() [2/2]

template<class InputVectorType = RealVector>
class shark::LogisticRegression< InputVectorType >