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upload BORG example file
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rtaormina committed Jul 8, 2015
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212 changes: 212 additions & 0 deletions script_example_BORG.m
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% This script illustrates the Borg implementation of the
% WQEISS/WMOSS/FQEISS feature selection techniques described in:
%
% Karakaya, G., Galelli, S., Ahipasaoglu, S.D., Taormina, R., 2015.
% Identifying (Quasi) Equally Informative Subsets in Feature Selection Problems
% for Classification: A Max-Relevance Min-Redundancy Approach.
% IEEE Trans. Cybern. doi:10.1109/TCYB.2015.2444435
%
%
% Copyright 2015 Riccardo Taormina (riccardo_taormina@sutd.edu.sg),
% Gulsah Karakaya (gulsahkilickarakaya@gmail.com;),
% Stefano Galelli (stefano_galelli@sutd.edu.sg),
% and Selin Damla Ahipasaoglu (ahipasaoglu@sutd.edu.sg;.
%
% Please refer to README.txt for further information.
%
%
% This file is part of Matlab-Multi-objective-Feature-Selection.
%
% Matlab-Multi-objective-Feature-Selection 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.
%
% This code 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 MATLAB_IterativeInputSelection.
% If not, see <http://www.gnu.org/licenses/>.

clc; clear;

%% specify include paths
addpath('..\..\Work\Code\toolboxes\mi'); % Peng's mutual information
addpath('..\toolboxes\borg-matlab\'); % Borg
addpath('..\toolboxes\pareto_front'); % paretofront toolbox



%% Load and prepare dataset

% load dataset
filePath = 'Heart.csv';
[orig_data,varNames,varTypes] = readData(filePath);

% transform data
transf_data = transformData(orig_data,varTypes);

% normalize data
norm_data = normalizeData(transf_data);

% compute relevance and redundacy
global suRED suREL
[suRED,suREL] = computeRelevanceRedundancy(norm_data);


%% Prepare for launching the algorithms

% specify GO algorithm to use (BORG or NSGA2)
GOalgorithm = 'BORG';

% get algorithm options
global objFunOptions

[options,objFunOptions] = ...
getAlgorithmOptions(GOalgorithm,norm_data);

% initialize overall archive and array containing the values of the
% objctive functions (fvals)
global archive fvals ix_solutions
archive = {}; % archive of all solutions explored
fvals = []; % values of the obj function explored
% RELEVANCE - REDUNDACY - ACCURACY - #INPUTS

ix_solutions = []; % this will track which solutions are found by each algorithm


%% launch WQEISS
fprintf ('Launching WQEISS\n')

% define number of obj functions and the matlab function coding them
options.nobjs = 4;
options.objectiveFcn = @objFunWQEISS;
epsilon = 10^-3;
epsilons = repmat(epsilon, [1,options.nobjs]);

% launch
borg(...
options.nvars,options.nobjs,options.nconstrs,...
options.objectiveFcn, options.NFE,...
options.lowerBounds, options.upperBounds, epsilons);


% get solutions indexes for WQEISS
ixWQEISS = find(ix_solutions);


% compute final pareto front
ixesPF = find(paretofront(fvals(ixWQEISS,:)));
PF_WQEISS.archive = archive(ixWQEISS(ixesPF));
PF_WQEISS.fvals = fvals(ixWQEISS(ixesPF),:);
PF_WQEISS.fvals_ext = fvals(ixWQEISS(ixesPF),:);



%% launch WMOSS
fprintf ('Launching WMOSS\n')

% define number of obj functions and the matlab function coding them
options.nobjs = 2;
options.objectiveFcn = @objFunWMOSS;
epsilon = 10^-3;
epsilons = repmat(epsilon, [1,options.nobjs]);

% launch
ix_solutions = zeros(numel(archive),1); % re-initialize ix_solutions.
% at the start of the algorithm, none
% of solutions in the archive has been
% found yet;
% launch
borg(...
options.nvars,options.nobjs,options.nconstrs,...
options.objectiveFcn, options.NFE,...
options.lowerBounds, options.upperBounds, epsilons);
% get solutions indexes for WMOSS
ixWMOSS = find(ix_solutions);

% compute final pareto front
ixesPF = find(paretofront(fvals(ixWMOSS,3:4)));
PF_WMOSS.archive = archive(ixWMOSS(ixesPF));
PF_WMOSS.fvals = fvals(ixWMOSS(ixesPF),[3,4]);
PF_WMOSS.fvals_ext = fvals(ixWMOSS(ixesPF),:);


%% launch FQEISS
fprintf ('Launching FQEISS\n')

% define number of obj functions and the matlab function coding them
options.nobjs = 3;
options.objectiveFcn = @objFunFQEISS;
epsilon = 10^-3;
epsilons = repmat(epsilon, [1,options.nobjs]);


% launch
ix_solutions = zeros(numel(archive),1); % re-initialize ix_solutions.
% at the start of the algorithm, none
% of solutions in the archive has been
% found yet;
% launch
borg(...
options.nvars,options.nobjs,options.nconstrs,...
options.objectiveFcn, options.NFE,...
options.lowerBounds, options.upperBounds, epsilons);
% get solutions indexes for FQEISS
ixFQEISS = find(ix_solutions);

% compute final pareto front
ixesPF = find(paretofront(fvals(ixFQEISS,[1,2,4])));
PF_FQEISS.archive = archive(ixFQEISS(ixesPF));
PF_FQEISS.fvals = fvals(ixFQEISS(ixesPF),[1,2,4]);
PF_FQEISS.fvals_ext = fvals(ixFQEISS(ixesPF),:);

%% delta elimination for WQEISS and WMOSS
delta = 20;
PFdelta_WQEISS = deltaElimination(PF_WQEISS,delta);
PFdelta_FQEISS = deltaElimination(PF_FQEISS,delta);

%% Plot WMOSS vs PFdeltas
figure;
subplot(1,2,1);
plot(PF_WMOSS.fvals_ext(:,4), -PF_WMOSS.fvals_ext(:,3),'ro');
hold on
plot(PFdelta_WQEISS.fvals_ext(:,4), -PFdelta_WQEISS.fvals_ext(:,3),'k.');
legend({'WMOSS','WQEISS'})
title('WMOSS vs WQEISS')
xlabel('Cardinality')
ylabel('Accuracy')
axis square

subplot(1,2,2);
plot(PF_WMOSS.fvals_ext(:,4), -PF_WMOSS.fvals_ext(:,3),'ro');
hold on
plot(PFdelta_FQEISS.fvals_ext(:,4), -PFdelta_FQEISS.fvals_ext(:,3),'k.');
legend({'WMOSS','FQEISS'})
title('WMOSS vs FQEISS')
xlabel('Cardinality')
ylabel('Accuracy')
axis square

%% Plot Frequency matrices
figure('name','FQEISS (left) and WQEISS (right) frequency matrices');
subplot(1,2,1);
plotFrequencyMatrix(PFdelta_FQEISS,options.nvars,varNames)

subplot(1,2,2);
plotFrequencyMatrix(PFdelta_WQEISS,options.nvars,varNames)












8 changes: 4 additions & 4 deletions script_example_NSGAII.m
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options.objfun = @objFunWMOSS;

% launch
ix_solutions = zeros(numel(archive),1); % re-initialize ix_solutions.
ix_solutions = zeros(numel(archive),1); % re-initialize ix_solutions.
% at the start of the algorithm, none
% of solutions in the archive have been
% of solutions in the archive has been
% found yet;
nsga2(options);
% get solutions indexes for WMOSS
Expand All @@ -131,9 +131,9 @@
options.objfun = @objFunFQEISS;

% launch
ix_solutions = zeros(numel(archive),1); % re-initialize ix_solutions.
ix_solutions = zeros(numel(archive),1); % re-initialize ix_solutions.
% at the start of the algorithm, none
% of solutions in the archive have been
% of solutions in the archive has been
% found yet;
nsga2(options);
% get solutions indexes for FQEISS
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