#include <ml/optimization/optimization_interface.hpp>

Public Member Functions
virtual	~second_order_opt_interface ()

virtual void	compute_second_order_statistics (const DenseVector &point, DenseMatrix &Hessian, DenseVector &gradient, double &function_value)=0

virtual void	compute_hessian (const DenseVector &point, DenseMatrix &hessian)

virtual size_t	num_examples () const =0

virtual size_t	num_variables () const =0

virtual void	compute_first_order_statistics (const DenseVector &point, DenseVector &gradient, double &function_value, const size_t mbStart=0, const size_t mbSize=-1)=0

virtual double	compute_function_value (const DenseVector &point, const size_t mbStart=0, const size_t mbSize=-1)

virtual void	compute_gradient (const DenseVector &point, DenseVector &gradient, const size_t mbStart=0, const size_t mbSize=-1)

virtual void	reset (int seed)

virtual std::vector< std::pair< std::string, size_t > >	get_status_header (const std::vector< std::string > &stats)

virtual std::vector< std::string >	get_status (const DenseVector &coefs, const std::vector< std::string > &stats)

Detailed Description

The interface to inherit from to describe a second order optimization model.

This model must implement methods to compute hessian as well as gradient.

Definition at line 253 of file optimization_interface.hpp.

Constructor & Destructor Documentation

◆ ~second_order_opt_interface()

virtual turi::optimization::second_order_opt_interface::~second_order_opt_interface ( )

virtual

Default desctuctor.

Member Function Documentation

◆ compute_first_order_statistics()

virtual void turi::optimization::first_order_opt_interface::compute_first_order_statistics	(	const DenseVector &	point,
		DenseVector &	gradient,
		double &	function_value,
		const size_t	mbStart = `0`,
		const size_t	mbSize = `-1`
	)

pure virtualinherited

Compute first order statistics at the given point. (Gradient & Function value)

Parameters

[in]	point	Point at which we are computing the stats.
[out]	gradient	Dense gradient
[out]	function_value	Function value
[in]	mbStart	Minibatch start index
[in]	mbSize	Minibatch size (-1 implies all)

Warning: DO NOT USE this if your model is truly sparse.

Implemented in turi::supervised::linear_svm_scaled_logistic_opt_interface, turi::supervised::logistic_regression_opt_interface, and turi::supervised::linear_regression_opt_interface.

◆ compute_function_value()

virtual double turi::optimization::first_order_opt_interface::compute_function_value	(	const DenseVector &	point,
		const size_t	mbStart = `0`,
		const size_t	mbSize = `-1`
	)

virtualinherited

Optimizations for performance reasons.

Compute the function value at a given point.

Parameters

[in]	point	Point at which we are computing the gradient.
[in]	mbStart	Minibatch start index
[in]	mbSize	Minibatch size (-1 implies all)

Returns: Function value at "point"

◆ compute_gradient()

virtual void turi::optimization::first_order_opt_interface::compute_gradient	(	const DenseVector &	point,
		DenseVector &	gradient,
		const size_t	mbStart = `0`,
		const size_t	mbSize = `-1`
	)

virtualinherited

Compute a gradient at the given point.

Parameters

[in]	point	Point at which we are computing the gradient.
[out]	gradient	Dense gradient
[in]	mbStart	Minibatch start index
[in]	mbSize	Minibatch size (-1 implies all)

Warning: See warning on "sparse" compute_gradient to see a potential danger of an infinite loop if you don't imeplement at least one of "dense" or "sparse" versions of compute_gradient.; DO NOT USE this if your model is truly sparse.

◆ compute_hessian()

virtual void turi::optimization::second_order_opt_interface::compute_hessian	(	const DenseVector &	point,
		DenseMatrix &	hessian
	)

virtual

Optimizations for performance reasons.

Compute the hessian at the given point

Parameters

[in]	point	Point at which we are computing the function value.
[out]	hessian	Returns a dense hessian matrix.

Note: Hessians are always dense matrices. Even if they are sparse, it is still really hard to factorize them by make sure the factors stay sparse.

◆ compute_second_order_statistics()

virtual void turi::optimization::second_order_opt_interface::compute_second_order_statistics	(	const DenseVector &	point,
		DenseMatrix &	Hessian,
		DenseVector &	gradient,
		double &	function_value
	)

pure virtual

Compute second order statistics at the given point. (Hessian, Gradient & Function value)

Parameters

[in]	point	Point at which we are computing the stats.
[out]	hessian	Hessian computation (dense)
[out]	gradient	Dense gradient
[out]	function_value	Function value

Implemented in turi::supervised::logistic_regression_opt_interface, and turi::supervised::linear_regression_opt_interface.

◆ get_status()

virtual std::vector<std::string> turi::optimization::first_order_opt_interface::get_status	(	const DenseVector &	coefs,
		const std::vector< std::string > &	stats
	)

virtualinherited

Get strings needed to print a row of the progress table.

Parameters

[in]	a	vector of model coefficients.
[in]	a	vector of stats to print at the beginning of each row

Reimplemented in turi::supervised::logistic_regression_opt_interface, turi::supervised::linear_svm_scaled_logistic_opt_interface, and turi::supervised::linear_regression_opt_interface.

◆ get_status_header()

virtual std::vector<std::pair<std::string,size_t> > turi::optimization::first_order_opt_interface::get_status_header ( const std::vector< std::string > & stats )

virtualinherited

Get strings needed to print the header for the progress table.

Parameters

[in] a vector of strings to print at the beginning of the header.

Reimplemented in turi::supervised::logistic_regression_opt_interface, turi::supervised::linear_svm_scaled_logistic_opt_interface, and turi::supervised::linear_regression_opt_interface.

◆ num_examples()

virtual size_t turi::optimization::first_order_opt_interface::num_examples ( ) const

pure virtualinherited

Get the number of examples in the dataset (Required for SGD).

Returns: Number of examples in the data.

Implemented in turi::supervised::linear_svm_scaled_logistic_opt_interface, turi::supervised::logistic_regression_opt_interface, and turi::supervised::linear_regression_opt_interface.

◆ num_variables()

virtual size_t turi::optimization::first_order_opt_interface::num_variables ( ) const

pure virtualinherited

Get the number of variables in the optimization problem.

Returns: Number of variables (features) in the optimization.

Note: Statisticians beware. Bias terms are variables in the optimization problem.

Implemented in turi::supervised::linear_svm_scaled_logistic_opt_interface, turi::supervised::logistic_regression_opt_interface, and turi::supervised::linear_regression_opt_interface.

◆ reset()

virtual void turi::optimization::first_order_opt_interface::reset ( int seed )

virtualinherited

Reset the state of the model's "randomness" source.

Parameters

[in] seed Seed that is the source of randomness.

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

ml/optimization/optimization_interface.hpp

Public Member Functions

Detailed Description

Constructor & Destructor Documentation

◆ ~second_order_opt_interface()

Member Function Documentation

◆ compute_first_order_statistics()

◆ compute_function_value()

Optimizations for performance reasons.

◆ compute_gradient()

◆ compute_hessian()

Optimizations for performance reasons.

◆ compute_second_order_statistics()

◆ get_status()

◆ get_status_header()

◆ num_examples()

◆ num_variables()

◆ reset()