MRMFeatureSelector Class Reference

#include <OpenMS/ANALYSIS/OPENSWATH/MRMFeatureSelector.h>

Inheritance diagram for MRMFeatureSelector:

Classes
struct	SelectorParameters

Public Types
enum class	VariableType { INTEGER = 1 , CONTINUOUS }
enum class	LambdaScore {   LINEAR = 1 , INVERSE , LOG , INVERSE_LOG ,   INVERSE_LOG10 }

Public Member Functions
	MRMFeatureSelector ()=default
virtual	~MRMFeatureSelector ()=default
virtual void	optimize (const std::vector< std::pair< double, String >> &time_to_name, const std::map< String, std::vector< Feature >> &feature_name_map, std::vector< String > &result, const SelectorParameters &parameters) const =0
void	selectMRMFeature (const FeatureMap &features, FeatureMap &selected_filtered, const SelectorParameters &parameters) const

Protected Member Functions
Int	addVariable_ (LPWrapper &problem, const String &name, const bool bounded, const double obj, const VariableType variableType) const
double	computeScore_ (const Feature &feature, const std::map< String, LambdaScore > &score_weights) const
void	addConstraint_ (LPWrapper &problem, std::vector< Int > indices, std::vector< double > values, const String &name, const double lb, const double ub, const LPWrapper::Type param) const

Private Member Functions
void	constructTargTransList_ (const FeatureMap &features, std::vector< std::pair< double, String >> &time_to_name, std::map< String, std::vector< Feature >> &feature_name_map, const bool select_transition_group) const
double	weightScore_ (const double score, const LambdaScore lambda_score) const
String	removeSpaces_ (String str) const
	Removes spaces from the given string, not-in-place. More...

Friends
class	MRMFeatureSelector_test
	To test private and protected methods. More...

Detailed Description

A Base class (it contains a pure virtual function named `optimize()`) for selection of MRM Features through Linear Programming.

Member Enumeration Documentation

LambdaScore

enum LambdaScore

strong

Enumerator
LINEAR
INVERSE
LOG
INVERSE_LOG
INVERSE_LOG10

VariableType

enum VariableType

strong

Enumerator
INTEGER
CONTINUOUS

Constructor & Destructor Documentation

MRMFeatureSelector()

MRMFeatureSelector ( )

default

~MRMFeatureSelector()

virtual ~MRMFeatureSelector ( )

virtualdefault

Member Function Documentation

addConstraint_()

void addConstraint_ ( LPWrapper &  problem, std::vector< Int >  indices, std::vector< double >  values, const String &  name, const double  lb, const double  ub, const LPWrapper::Type  param  ) const

protected

Add constraint to the LP problem instantiated in `optimize()`

Parameters

[in,out]	problem	LPWrapper object
[in]	indices	LP matrix indices
[in]	values	LP matrix values
[in]	name	Row name
[in]	lb	Lower bound
[in]	ub	Upper bound
[in]	param	Row type

addVariable_()

Int addVariable_ ( LPWrapper &  problem, const String &  name, const bool  bounded, const double  obj, const VariableType  variableType  ) const

protected

Add variable to the LP problem instantiated in `optimize()`

Parameters

[in,out]	problem	LPWrapper object
[in]	name	Column name
[in]	bounded	Double bounded if true, otherwise Unbounded.
[in]	obj	Objective value
[in]	variableType	Either integer or continuous

Returns

The variable's column index

computeScore_()

double computeScore_ ( const Feature &  feature, const std::map< String, LambdaScore > &  score_weights  ) const

protected

Scoring method used by the optimizer. Metavalues to use are decided by the `score_weights` argument. The returned value is used in the LP problems' variables and constraints.

Parameters

[in]	feature	Input feature
[in]	score_weights	Score weights

Returns

Computed score

Referenced by MRMFeatureSelector_test::computeScore_().

constructTargTransList_()

void constructTargTransList_ ( const FeatureMap &  features, std::vector< std::pair< double, String >> &  time_to_name, std::map< String, std::vector< Feature >> &  feature_name_map, const bool  select_transition_group  ) const

private

Construct the target transition's or transition group's retention times that will be used to score candidate features based on their deviation from the relative distance between the target transition's or transition group's times

Parameters

[in]	features	Input features
[out]	time_to_name	Pairs representing a mapping of retention times to transition names
[out]	feature_name_map	Transitions' names to their features objects
[in]	select_transition_group	Transition group selection

Referenced by MRMFeatureSelector_test::constructTargTransList_().

optimize()

virtual void optimize ( const std::vector< std::pair< double, String >> &  time_to_name, const std::map< String, std::vector< Feature >> &  feature_name_map, std::vector< String > &  result, const SelectorParameters &  parameters  ) const

pure virtual

Derived classes implement this pure virtual method.

It sets up the linear programming problem and solves it.

Parameters

[in]	time_to_name	Pairs representing a mapping of retention times to transition names
[in]	feature_name_map	Transitions' names to their features objects
[out]	result	Transitions' names filtered out of the LP problem
[in]	parameters	Parameters

Implemented in MRMFeatureSelectorScore, and MRMFeatureSelectorQMIP.

removeSpaces_()

String removeSpaces_ ( String  str ) const

private

Removes spaces from the given string, not-in-place.

Referenced by MRMFeatureSelector_test::removeSpaces_().

selectMRMFeature()

void selectMRMFeature	(	const FeatureMap &	features,
		FeatureMap &	selected_filtered,
		const SelectorParameters &	parameters
	)		const

The features are sorted by retention time and splitted into segments with the given step and window length. The features are then selected based on the results of `optimize()` method applied to each segment. The segments may overlap.

Parameters

[in]	features	Input features
[out]	selected_filtered	Output features
[in]	parameters	Parameters

weightScore_()

double weightScore_ ( const double  score, const LambdaScore  lambda_score  ) const

private

Transform the given score through the chosen lambda function

Possible values for `lambda_score` are:

• LambdaScore::LINEAR
• LambdaScore::INVERSE
• LambdaScore::LOG
• LambdaScore::INVERSE_LOG
• LambdaScore::INVERSE_LOG10

Exceptions

Exception::IllegalArgument

When an invalid `lambda_score` is passed

Parameters

[in]	score	Value to transform
[in]	lambda_score	A string representing the desired transformation

Returns

The weighted value

Referenced by MRMFeatureSelector_test::weightScore_().

Friends And Related Function Documentation

MRMFeatureSelector_test

friend class MRMFeatureSelector_test

friend

To test private and protected methods.