featureSelection.h

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/* featureSelection.h
 * 
 * Copyright (C) 2009 Marcel Schumann
 * 
 * This file is part of QuEasy -- A Toolbox for Automated QSAR Model
 * Construction and Validation.
 * QuEasy is free software; you can redistribute it and/or modify
 * it under the terms of the GNU General Public License as published by
 * the Free Software Foundation; either version 3 of the License, or (at
 * your option) any later version.
 * 
 * QuEasy is distributed in the hope that it will be useful, but
 * WITHOUT ANY WARRANTY; without even the implied warranty of
 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
 * General Public License for more details.
 * 
 * You should have received a copy of the GNU General Public License
 * along with this program; if not, see <http://www.gnu.org/licenses/>.
 */

// -*- Mode: C++; tab-width: 2; -*-
// vi: set ts=2:
//
// 

#ifndef FEATURESEL
#define FEATURESEL

#ifndef MODEL
#include <BALL/QSAR/Model.h>
#endif

#ifndef KMODEL
#include <BALL/QSAR/kernelModel.h>
#endif

#ifndef LMODEL
#include <BALL/QSAR/linearModel.h>
#endif

#include <set>

namespace BALL 
{
  namespace QSAR
  {

    class BALL_EXPORT FeatureSelection
    {
      public:
        FeatureSelection(Model& m);
        
        FeatureSelection(KernelModel& m);

        ~FeatureSelection();
        
        
        void setModel(Model& m);
        
        void setModel(KernelModel& km);
  
        void forwardSelection(int k=4, bool optPar=0);
        
        void backwardSelection(int k=4, bool optPar=0);
        
        void stepwiseSelection(int k=4, bool optPar=0);
        
        void twinScan(int k, bool optPar=0);
        
        void implicitSelection(LinearModel& lm, int act=1, double d=1);
        
        void removeHighlyCorrelatedFeatures(double& cor_threshold);
        
        
        void removeLowResponseCorrelation(double& min_correlation);
        
        
        void removeEmptyDescriptors();
        
        void selectStat(int s);
        
        void setQualityIncreaseCutoff(double& d);
  
  
      private:
        
        void updateWeights(std::multiset<unsigned int>& oldDescIDs, std::multiset<unsigned int>& newDescIDs, Vector<double>& oldWeights);
        
        
        std::multiset<unsigned int>* findIrrelevantDescriptors();
        
        Model* model_;
        
        Vector<double>* weights_;
        
        void forward(bool stepwise, int k, bool optPar);
        
        double quality_increase_cutoff_;
    };
  }
}


#endif // FEATURESEL