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

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

Inheritance diagram for SimpleSVM:

Collaboration diagram for SimpleSVM:

Classes
struct	Prediction
	SVM/SVR prediction result. More...

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

typedef std::map< String, std::pair< double, double > >	ScaleMap
	Mapping from predictor name to predictor min and max. More...

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

	~SimpleSVM () override
	Destructor. More...

	SimpleSVM (const SimpleSVM &)=delete

SimpleSVM &	operator= (const SimpleSVM &)=delete

void	setup (PredictorMap &predictors, const std::map< Size, double > &outcomes, bool classification=true)
	Load data and train a model. More...

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

void	predict (PredictorMap &predictors, std::vector< Prediction > &predictions) const
	Predict class labels or regression values (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...

const ScaleMap &	getScaling () const
	Get data range of predictors before scaling to [0, 1]. 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...

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 Attributes
std::unique_ptr< Impl >	pimpl_

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...

Additional Inherited Members
Static Public Member Functions inherited from DefaultParamHandler
static void	writeParametersToMetaValues (const Param &write_this, MetaInfoInterface &write_here, const String &key_prefix="")
	Writes all parameters to meta values. 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...

Detailed Description

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

This class supports:

(multi-class) classification and regression with an epsilon-SVR.
linear or RBF kernel.

It uses cross-validation to optimize the respective SVM/SVR parameters C, p (SVR-only) and (RBF kernel only) gamma.

Usage: SVM models are generated by the the setup() method. SimpleSVM provides two common use cases for convinience:

all data: Passing all data (training + test) as predictors to setup and training on a subset.
training only: Passing exclusivly training data as predictors to setup. The parameter outcomes of setup() defines in both cases the training set; it contains the indexes of observations (corresponding to positions in the vectors in predictors) together with the class labels (or regression values) for training.

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).

To predict class labels (or regression values) based on a model, use one of the predict() methods:

if setup with all data: 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.)
if setup with training data only: by passing a new PredictorMap.

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	[-5.0, -3.0, -1.0, 1.0, 3.0, 5.0, 7.0, 9.0, 11.0, 13.0, 15.0]		Values to try for the SVM parameter 'C' during parameter optimization. A value 'x' is used as 'C = 2^x'.
log2_gamma	float list	[-15.0, -13.0, -11.0, -9.0, -7.0, -5.0, -3.0, -1.0, 1.0, 3.0]		Values to try for the SVM parameter 'gamma' during parameter optimization (RBF kernel only). A value 'x' is used as 'gamma = 2^x'.
log2_p	float list	[-15.0, -12.0, -9.0, -6.0, -3.32192809489, 0.0, 3.32192809489, 6.0, 9.0, 12.0, 15.0]		Values to try for the SVM parameter 'epsilon' during parameter optimization (epsilon-SVR only). A value 'x' is used as 'epsilon = 2^x'.
epsilon	float	1.0e-03	min: 0.0	Stopping criterion
cache_size	float	100.0	min: 1.0	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.

Class Documentation

◆ OpenMS::SimpleSVM::Prediction

struct OpenMS::SimpleSVM::Prediction

SVM/SVR prediction result.

Collaboration diagram for SimpleSVM::Prediction:

[legend]

Class Members
double	outcome	Predicted class label (or regression value)
map< double, double >	probabilities	Class label (or regression value) and their predicted probabilities.

Member Typedef Documentation

◆ PredictorMap

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

Mapping from predictor name to vector of predictor values.

◆ ScaleMap

typedef std::map<String, std::pair<double, double> > ScaleMap

Mapping from predictor name to predictor min and max.

Constructor & Destructor Documentation

◆ SimpleSVM() [1/2]

SimpleSVM ( )

Default constructor.

◆ ~SimpleSVM()

~SimpleSVM ( )

override

Destructor.

◆ SimpleSVM() [2/2]

SimpleSVM ( const SimpleSVM & )

delete

Member Function Documentation

◆ 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

◆ getScaling()

const ScaleMap& getScaling ( ) const

Get data range of predictors before scaling to [0, 1].

◆ operator=()

SimpleSVM& operator= ( const SimpleSVM & )

delete

◆ predict() [1/2]

void predict	(	PredictorMap &	predictors,
		std::vector< Prediction > &	predictions
	)		const

Predict class labels or regression values (and probabilities).

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 applied to training data in setup.
predictions	Output vector of prediction results (same order as `indexes`).

Exceptions

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

◆ predict() [2/2]

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

Predict class labels or regression values (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`

◆ setup()

void setup	(	PredictorMap &	predictors,
		const std::map< Size, double > &	outcomes,
		bool	classification = `true`
	)

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.
outcomes	Mapping from observation index to class label or regression value in the training set.
classification	true (default) if SVM classification should be used, SVR otherwise

Exceptions

Exception::IllegalArgument	if `predictors` is empty
Exception::InvalidValue	if an invalid index is used in `outcomes`
Exception::MissingInformation	if there are fewer than two class labels in `outcomes`, 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

◆ pimpl_

std::unique_ptr<Impl> pimpl_

protected

Classes

Public Types

Public Member Functions

Protected Attributes

Additional Inherited Members

Detailed Description

Class Documentation

◆ OpenMS::SimpleSVM::Prediction

Member Typedef Documentation

◆ PredictorMap

◆ ScaleMap

Constructor & Destructor Documentation

◆ SimpleSVM() [1/2]

◆ ~SimpleSVM()

◆ SimpleSVM() [2/2]

Member Function Documentation

◆ getFeatureWeights()

◆ getScaling()

◆ operator=()

◆ predict() [1/2]

◆ predict() [2/2]

◆ setup()

◆ writeXvalResults()

Member Data Documentation

◆ pimpl_