MRMFeatureSelectorScore Class Reference

MRMFeatureSelector implementation that selects MRM features via a linear program with score-weighted per-feature variables. More...

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

Inheritance diagram for MRMFeatureSelectorScore:

Collaboration diagram for MRMFeatureSelectorScore:

Public Member Functions
void	optimize (const std::vector< std::pair< double, std::string > > &time_to_name, const std::map< std::string, std::vector< Feature > > &feature_name_map, std::vector< std::string > &result, const SelectorParameters &parameters) const override
	Build the linear program for time_to_name and write the names of selected features to result.
Public Member Functions inherited from MRMFeatureSelector
	MRMFeatureSelector ()=default
virtual	~MRMFeatureSelector ()=default
void	selectMRMFeature (const FeatureMap &features, FeatureMap &selected_filtered, const SelectorParameters &parameters) const

Additional Inherited Members
Public Types inherited from MRMFeatureSelector
enum class	VariableType { INTEGER = 1 , CONTINUOUS }
enum class	LambdaScore {   LINEAR = 1 , INVERSE , LOG , INVERSE_LOG ,   INVERSE_LOG10 }
Protected Member Functions inherited from MRMFeatureSelector
Int	addVariable_ (LPWrapper &problem, const std::string &name, const bool bounded, const double obj, const VariableType variableType) const
double	computeScore_ (const Feature &feature, const std::map< std::string, LambdaScore > &score_weights) const
void	addConstraint_ (LPWrapper &problem, const std::vector< Int > &indices, const std::vector< double > &values, const std::string &name, const double lb, const double ub, const LPWrapper::Type param) const

Detailed Description

MRMFeatureSelector implementation that selects MRM features via a linear program with score-weighted per-feature variables.

Simpler than MRMFeatureSelectorQMIP — no nearest-neighbour coupling, no pairwise quadratic terms. Each transition contributes one binary (or continuous) variable per candidate feature plus an equality constraint forcing exactly one feature to be picked per transition (sum == 1). The objective is the sum of per-feature scores produced by computeScore_ from MRMFeatureSelector::SelectorParameters::score_weights.

Member Function Documentation

optimize()

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

overridevirtual

Build the linear program for time_to_name and write the names of selected features to result.

Uses LPWrapper with LPWrapper::MIN objective sense. For each transition, registers one variable per candidate feature (binary or continuous, per parameters.variable_type) scored by computeScore_, and adds a DOUBLE_BOUNDED constraint with both bounds equal to 1.0 (i.e. exactly one feature must be picked per transition). After solving, every column whose value is >= parameters.optimal_threshold contributes its name to result. result is cleared on entry.

Parameters

[in]	time_to_name	Pairs of (retention time, transition name). Order is irrelevant — unlike the QMIP variant, no neighbour window is used.
[in]	feature_name_map	Transition name → its candidate features.
[out]	result	Names of the selected features (one per column whose solver value clears optimal_threshold). Cleared on entry.
[in]	parameters	Algorithm parameters (variable_type, score_weights, optimal_threshold; nn_threshold and locality_weight are not consulted).

Implements MRMFeatureSelector.