Simple interface to support vector machines for classification (via LIBSVM). More...

#include <OpenMS/ANALYSIS/SVM/SimpleSVM.h>

Inheritance diagram for SimpleSVM:

Classes
struct	Prediction
	SVM prediction result. More...

Public Types
typedef std::map< String, std::vector< double > >	PredictorMap
	Mapping from predictor name to vector of predictor values. More...

Public Member Functions
	SimpleSVM ()
	Default constructor. More...

	~SimpleSVM () override
	Destructor. More...

void	setup (PredictorMap &predictors, const std::map< Size, Int > &labels)
	Load data and train a model. More...

void	predict (std::vector< Prediction > &predictions, std::vector< Size > indexes=std::vector< Size >()) const
	Predict class labels (and probabilities). More...

void	getFeatureWeights (std::map< String, double > &feature_weights) const
	Get the weights used for features (predictors) in the SVM model. More...

void	writeXvalResults (const String &path) const
	Write cross-validation (parameter optimization) results to a CSV file. More...

Public Member Functions inherited from DefaultParamHandler
	DefaultParamHandler (const String &name)
	Constructor with name that is displayed in error messages. More...

	DefaultParamHandler (const DefaultParamHandler &rhs)
	Copy constructor. More...

virtual	~DefaultParamHandler ()
	Destructor. More...

virtual DefaultParamHandler &	operator= (const DefaultParamHandler &rhs)
	Assignment operator. More...

virtual bool	operator== (const DefaultParamHandler &rhs) const
	Equality operator. More...

void	setParameters (const Param &param)
	Sets the parameters. More...

const Param &	getParameters () const
	Non-mutable access to the parameters. More...

const Param &	getDefaults () const
	Non-mutable access to the default parameters. More...

const String &	getName () const
	Non-mutable access to the name. More...

void	setName (const String &name)
	Mutable access to the name. More...

const std::vector< String > &	getSubsections () const
	Non-mutable access to the registered subsections. More...

Protected Types
typedef std::vector< std::vector< double > >	SVMPerformance
	Classification performance for different param. combinations (C/gamma): More...

Protected Member Functions
void	scaleData_ (PredictorMap &predictors) const
	Scale predictor values to range 0-1. More...

void	convertData_ (const PredictorMap &predictors)
	Convert predictors to LIBSVM format. More...

std::pair< double, double >	chooseBestParameters_ () const
	Choose best SVM parameters based on cross-validation results. More...

void	optimizeParameters_ ()
	Run cross-validation to optimize SVM parameters. More...

Protected Member Functions inherited from DefaultParamHandler
virtual void	updateMembers_ ()
	This method is used to update extra member variables at the end of the setParameters() method. More...

void	defaultsToParam_ ()
	Updates the parameters after the defaults have been set in the constructor. More...

Static Protected Member Functions
static void	printNull_ (const char *)
	Dummy function to suppress LIBSVM output. More...

Protected Attributes
std::vector< std::vector< struct svm_node > >	nodes_
	Values of predictors (LIBSVM format) More...

struct svm_problem	data_
	SVM training data (LIBSVM format) More...

struct svm_parameter	svm_params_
	SVM parameters (LIBSVM format) More...

struct svm_model *	model_
	Pointer to SVM model (LIBSVM format) More...

std::vector< String >	predictor_names_
	Names of predictors in the model (excluding uninformative ones) More...

Size	n_parts_
	Number of partitions for cross-validation. More...

std::vector< double >	log2_C_
	Parameter values to try during optimization. More...

std::vector< double >	log2_gamma_

SVMPerformance	performance_
	Cross-validation results. More...

Protected Attributes inherited from DefaultParamHandler
Param	param_
	Container for current parameters. More...

Param	defaults_
	Container for default parameters. This member should be filled in the constructor of derived classes! More...

std::vector< String >	subsections_
	Container for registered subsections. This member should be filled in the constructor of derived classes! More...

String	error_name_
	Name that is displayed in error messages during the parameter checking. More...

bool	check_defaults_
	If this member is set to false no checking if parameters in done;. More...

bool	warn_empty_defaults_
	If this member is set to false no warning is emitted when defaults are empty;. More...

Detailed Description

Simple interface to support vector machines for classification (via LIBSVM).

This class supports (multi-class) classification with a linear or RBF kernel. It uses cross-validation to optimize the SVM parameters C and (RBF kernel only) gamma.

SVM models are generated by the the setup() method. To simplify the scaling of input data (predictors), the data for both the training and the test set together are passed in as parameter predictors. Given N observations of M predictors, the data are coded as a map of predictors (size M), each a numeric vector of values for different observations (size N).

The parameter labels of setup() defines the training set; it contains the indexes of observations (corresponding to positions in the vectors in predictors) together with the class labels for training.

To predict class labels based on a model, use the predict() method. The parameter indexes of predict() takes a vector of indexes corresponding to the observations for which predictions should be made. (With an empty vector, the default, predictions are made for all observations, including those used for training.)

Parameters of this class are:

Name	Type	Default	Restrictions	Description
kernel	string	RBF	RBF, linear	SVM kernel
xval	int	5	min: 1	Number of partitions for cross-validation (parameter optimization)
log2_C	float list	[-5, -3, -1, 1, 3, 5, 7, 9, 11, 13, 15]		Values to try for the SVM parameter 'C' during parameter optimization. A value 'x' is used as 'C = 2^x'.
log2_gamma	float list	[-15, -13, -11, -9, -7, -5, -3, -1, 1, 3]		Values to try for the SVM parameter 'gamma' during parameter optimization (RBF kernel only). A value 'x' is used as 'gamma = 2^x'.
epsilon	float	0.001	min: 0	Stopping criterion
cache_size	float	100	min: 1	Size of the kernel cache (in MB)
no_shrinking	string	false	true, false	Disable the shrinking heuristics

Note:

If a section name is documented, the documentation is displayed as tooltip.
Advanced parameter names are italic.

Member Typedef Documentation

◆ PredictorMap

typedef std::map<String, std::vector<double> > PredictorMap

Mapping from predictor name to vector of predictor values.

◆ SVMPerformance

typedef std::vector<std::vector<double> > SVMPerformance

protected

Classification performance for different param. combinations (C/gamma):

Constructor & Destructor Documentation

◆ SimpleSVM()

SimpleSVM ( )

Default constructor.

◆ ~SimpleSVM()

~SimpleSVM ( )

override

Destructor.

Member Function Documentation

◆ chooseBestParameters_()

std::pair<double, double> chooseBestParameters_ ( ) const

protected

Choose best SVM parameters based on cross-validation results.

◆ convertData_()

void convertData_ ( const PredictorMap & predictors )

protected

Convert predictors to LIBSVM format.

◆ getFeatureWeights()

void getFeatureWeights ( std::map< String, double > & feature_weights ) const

Get the weights used for features (predictors) in the SVM model.

Currently only supported for two-class classification. If a linear kernel is used, the weights are informative for ranking features.

Exceptions

Exception::Precondition if no model has been trained, or if the classification involves more than two classes

◆ optimizeParameters_()

void optimizeParameters_ ( )

protected

Run cross-validation to optimize SVM parameters.

◆ predict()

void predict	(	std::vector< Prediction > &	predictions,
		std::vector< Size >	indexes = `std::vector< Size >()`
	)		const

Predict class labels (and probabilities).

Parameters

predictions	Output vector of prediction results (same order as `indexes`).
indexes	Vector of observation indexes for which predictions are desired. If empty (default), predictions are made for all observations.

Exceptions

Exception::Precondition	if no model has been trained
Exception::InvalidValue	if an invalid index is used in `indexes`

◆ printNull_()

static void printNull_ ( const char * )

inlinestaticprotected

Dummy function to suppress LIBSVM output.

◆ scaleData_()

void scaleData_ ( PredictorMap & predictors ) const

protected

Scale predictor values to range 0-1.

◆ setup()

void setup	(	PredictorMap &	predictors,
		const std::map< Size, Int > &	labels
	)

Load data and train a model.

Parameters

predictors	Mapping from predictor name to vector of predictor values (for different observations). All vectors should have the same length; values will be changed by scaling.
labels	Mapping from observation index to class label in the training set.

Exceptions

Exception::IllegalArgument	if `predictors` is empty
Exception::InvalidValue	if an invalid index is used in `labels`
Exception::MissingInformation	if there are fewer than two class labels in `labels`, or if there are not enough observations for cross-validation

◆ writeXvalResults()

void writeXvalResults ( const String & path ) const

Write cross-validation (parameter optimization) results to a CSV file.

Member Data Documentation

◆ data_

struct svm_problem data_

protected

SVM training data (LIBSVM format)

◆ log2_C_

std::vector<double> log2_C_

protected

Parameter values to try during optimization.

◆ log2_gamma_

std::vector<double> log2_gamma_

protected

◆ model_

struct svm_model* model_

protected

Pointer to SVM model (LIBSVM format)

◆ n_parts_

Size n_parts_

protected

Number of partitions for cross-validation.

◆ nodes_

std::vector<std::vector<struct svm_node> > nodes_

protected

Values of predictors (LIBSVM format)

◆ performance_

SVMPerformance performance_

protected

Cross-validation results.

◆ predictor_names_

std::vector<String> predictor_names_

protected

Names of predictors in the model (excluding uninformative ones)

◆ svm_params_

struct svm_parameter svm_params_

protected

SVM parameters (LIBSVM format)

Classes

Public Types

Public Member Functions

Protected Types

Protected Member Functions

Static Protected Member Functions

Protected Attributes

Detailed Description

Member Typedef Documentation

◆ PredictorMap

◆ SVMPerformance

Constructor & Destructor Documentation

◆ SimpleSVM()

◆ ~SimpleSVM()

Member Function Documentation

◆ chooseBestParameters_()

◆ convertData_()

◆ getFeatureWeights()

◆ optimizeParameters_()

◆ predict()

◆ printNull_()

◆ scaleData_()

◆ setup()

◆ writeXvalResults()

Member Data Documentation

◆ data_

◆ log2_C_

◆ log2_gamma_

◆ model_

◆ n_parts_

◆ nodes_

◆ performance_

◆ predictor_names_

◆ svm_params_