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agaieragaier
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updates and streamlining to core code
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‎Contents.m

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% FFDSAIL
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%
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% Files
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% runSail - - Example usage script of sail function

‎README.md

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@@ -19,6 +19,8 @@ https://hal.inria.fr/hal-01518786/file/aiaa_sail.pdf
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public as their are published. If you are interested in creating a new
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domain and having trouble, don't hesistate to ask!
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A third domain is now included airfoils created with free form deformations.
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Produced using
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Matlab Version: 9.1.0.441655 (R2016b)
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‎addAllToPath.m

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%addAllToPath - This function adds all folders above it to the path
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%
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% Author: Adam Gaier
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% Bonn-Rhein-Sieg University of Applied Sciences (HBRS)
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% email: adam.gaier@h-brs.de
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% Oct 2017; Last revision: 09-Oct-2017
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%------------- BEGIN CODE --------------
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% Clean up workspace and add relevant files to path
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currentPath = mfilename('fullpath');
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addpath(genpath(currentPath(1:end-length(mfilename))));
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addpath(genpath('~/Code/matlabExtensions/'));
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‎mapElites/createChildren.m

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@@ -1,18 +1,17 @@
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function children = createChildren(map, p, d)
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function children = createChildren(map, nChildren, p, d)
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%createChildren - produce new children through mutation of map elite
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%
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% Syntax: children = createChildren(map,p)
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% Syntax: children = createChildren(map,nChildren,p,d)
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%
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% Inputs:
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% map - Population struct
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% map - Population struct
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% .fitness
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% .genes
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% .<additional info> (e.g., drag, lift, etc)
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% p - SAIL hyperparameter struct
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% .nChildren - number of children created
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% .mutSigma - sigma of gaussian mutation applied to children
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% d - Domain description struct
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% .dof - Degrees of freedom (genome length)
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% nChildren - number of children to create
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% p - SAIL hyperparameter struct
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% .mutSigma - sigma of gaussian mutation applied to children
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% d - Domain description struct
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% .dof - Degrees of freedom (genome length)
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%
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% Outputs:
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% children - [nChildren X genomeLength] - new solutions
@@ -22,16 +21,16 @@
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% Author: Adam Gaier
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% Bonn-Rhein-Sieg University of Applied Sciences (HBRS)
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% email: adam.gaier@h-brs.de
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% Jun 2016; Last revision: 01-Aug-2017
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% Jun 2016; Last revision: 17-Oct-2017
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%------------- BEGIN CODE --------------
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% Remove empty bins from parent pool
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parentPool = reshape(map.genes,[numel(map.fitness), d.dof]);
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parentPool(isnan(parentPool(:,1)),:) = [];
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% Choose parents and create mutation
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parents = parentPool(randi([1 size(parentPool,1)], [p.nChildren 1]), :);
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mutation = randn(p.nChildren,d.dof) .* p.mutSigma;
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parents = parentPool(randi([1 size(parentPool,1)], [nChildren 1]), :);
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mutation = randn(nChildren,d.dof) .* p.mutSigma;
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% Apply mutation
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children = parents + mutation;

‎mapElites/mapElites.m

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@@ -1,4 +1,4 @@
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function [map, h] = mapElites(fitnessFunction,map,p,d)
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function [map, percImproved, h] = mapElites(fitnessFunction,map,p,d)
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%mapElites - Multi-dimensional Archive of Phenotypic Elites algorithm
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%
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% Syntax: map = mapElites(fitnessFunction, map, p, d);
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%
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% Outputs:
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% map - struct - population archive
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% percImproved - percentage of children which improved on elites
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% h - [1X2] - axes handle, data handle
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%
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%
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% Author: Adam Gaier
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% Bonn-Rhein-Sieg University of Applied Sciences (HBRS)
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% email: adam.gaier@h-brs.de
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% Jun 2016; Last revision: 02-Aug-2017
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% Jun 2016; Last revision: 17-Oct-2017
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%------------- BEGIN CODE --------------
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% TODO:
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% parser instead of the sloppy ifs at top
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%------------- BEGIN CODE --------------
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% View Initial Map
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h = [];
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if p.display.illu
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figure(2); clf;
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[h(1), h(2)] = viewMap(map.fitness, d, map.edges); title('Illumination Fitness')
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end
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33-
iGen = 0;
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while (iGen < p.nGens)
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%% Create and Evaluate Children
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% Continue to remutate until enough children which satisfy geometric
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% constraints are created
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children = [];
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while size(children,1) < p.nChildren
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newChildren = createChildren(map, p, d);
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validInds = feval(d.validate,newChildren,d);
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children = [children ; newChildren(validInds,:)] ; %#ok<AGROW>
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end
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children = children(1:p.nChildren,:);
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[fitness, values] = fitnessFunction(children); %% TODO: Speed up without anonymous functions
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%% MAP-Elites
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iGen = 1;
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while (iGen <= p.nGens)
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%% 1) Create and Evaluate Children
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% Create children which satisfy geometric constraints for validity
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nMissing = p.nChildren; children = [];
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%% Add Children to Map
48-
[replaced, replacement] = nicheCompete(children,fitness,map,d);
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while nMissing > 0
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indPool = createChildren(map, nMissing, p, d);
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validFunction = @(genomes) feval(d.validate, genomes, d);
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[validChildren,~,nMissing] = getValidInds(indPool, validFunction, nMissing);
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children = [children; validChildren]; %#ok<AGROW>
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end
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[fitness, values] = fitnessFunction(children); %% TODO: Speed up without anonymous functions
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%% 2) Add Children to Map
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[replaced, replacement] = nicheCompete(children,fitness,map,d);
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map = updateMap(replaced,replacement,map,fitness,children,...
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values,d.extraMapValues);
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%% View New Map
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% Improvement Stats
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percImproved(iGen) = length(replaced)./p.nChildren; %#ok<AGROW>
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% View Illuminatiom Progress?
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if p.display.illu && ~mod(iGen,p.display.illuMod)
54-
set(h(2),'CData',flip(map.fitness),...
55-
'AlphaData',~isnan(flip(map.fitness)))
56-
colormap(h(1),parula(16));
57-
drawnow;
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set(h(2),'CData',flip(map.fitness),'AlphaData',~isnan(flip(map.fitness)))
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colormap(h(1),parula(16)); drawnow;
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end
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60-
iGen = iGen+1; if ~mod(iGen,100);disp([char(9) 'Illumination Generation: ' int2str(iGen)]);end;
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iGen = iGen+1; if ~mod(iGen,2^5);disp([char(9) 'Illumination Generation: ' int2str(iGen) ' - Improved: ' num2str(percImproved(end)*100) '%']);end;
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end
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if percImproved(end) > 0.05; disp('Warning: MAP-Elites finished while still making improvements ( >5% / generation )');end
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%------------- END OF CODE --------------

‎mapElites/nicheCompete.m

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@@ -1,4 +1,4 @@
1-
function [replaced, replacement] = nicheCompete(newInds,fitness,map,d)
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function [replaced, replacement, mapLinIndx] = nicheCompete(newInds,fitness,map,d)
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%nicheCompete - results of competition with map's existing elites
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%
44
% Syntax: [replaced, replacement] = nicheCompete(newInds,fitness,map,p)
@@ -12,6 +12,7 @@
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% Outputs:
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% replaced - [NX1] - Linear index of map cells to recieve replacements
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% replacement - [NX1] - Index of newInds to replace current elites in niche
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% mapLinIndx - [NX1] - Index of current elites (for rank testing only)
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%
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% Example:
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%
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[bestIndex, bestBin] = getBestPerCell(newInds,fitness,d, map.edges);
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mapLinIndx = sub2ind(d.featureRes,bestBin(:,1),bestBin(:,2));
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% Nan features?
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% A 'nan' genome sneaks in -- apparently after precise evaluation
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if any(isnan(mapLinIndx)); save('nanFeatureError'); end
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3137
% Compare to already existing samples
32-
improvement = ~(fitness (bestIndex) >= map.fitness(mapLinIndx)); % comparisons to NaN are always false
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improvement = ~(fitness (bestIndex) >= map.fitness(mapLinIndx)); % comparisons to NaN are always false
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improvement(isnan(fitness(bestIndex))) = false;
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replacement = bestIndex (improvement);
3541
replaced = mapLinIndx(improvement);

‎runSail.m

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@@ -1,73 +1,60 @@
1-
%runSail - Example usage script of sail function
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% Running sail without arguments will return a hyperparameter struct of
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%RUNSAIL - Example usage script of sail function
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% Calling sail without arguments will return a hyperparameter struct of
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% default values. These defaults can be changed in
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% /sail/defaultParamSet.m
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%
6-
% Running sail with a parameter struct as input will run the algorithm
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% Calling sail with a parameter and domain struct as input will run the
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% algorithm.
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%
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%
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% Other m-files required: defaultParamSet, sail, mapElites
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% Other submodules required: gpml-wrapper
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% Other m-files required: /sail/defaultParamSet.m, <domainName>_Domain.m
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% Other submodules required: gpml
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% For domain requirements see domains/<domainName>/Content.m
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%
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%
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% See also: mapElites, sail
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% See also: hpcSail, sail
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% Author: Adam Gaier
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% Bonn-Rhein-Sieg University of Applied Sciences (HBRS)
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% email: adam.gaier@h-brs.de
18-
% Nov 2016; Last revision: 01-Aag-2017
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% Nov 2016; Last revision: 04-Dec-2017
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%------------- BEGIN CODE --------------
21-
22-
p = sail;
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% Adjust hyperparameters
25-
p.nInitialSamples = 50;
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p.nAdditionalSamples= 10;
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p.nTotalSamples = 150;
28-
p.nChildren = 100;
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p.nGens = 500;
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% Clean up workspace and add relevant files to path
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clear;
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currentPath = mfilename('fullpath');
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addpath(genpath(currentPath(1:end-length(mfilename))));
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27+
% Algorithm hyperparameters
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p = sail; % load default hyperparameters
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30+
% Edit Hyperparameters
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p.nInitialSamples = 100;
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p.nTotalSamples = 200;
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p.nChildren = 2^5;
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p.nGens = 2^6;
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31-
p.display.illu = false;
32-
p.display.illuMod = 100;
33-
p.display.figs = true;
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p.data.mapEval = false; % produce intermediate prediction maps?
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p.data.mapEvalMod = 50; % how often? (in samples)
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35-
p.data.outSave = false;
36-
p.data.mapEval = true;
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p.data.mapEvalMod = 50;
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% Domain
40+
%d = parsec_Domain;
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d = ffdFoil_Domain;
42+
%d = velo_Domain('encoding','ffd'); d.preciseEvaluate = 'velo_DummyPreciseEvaluate';
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39-
40-
% % % % % % % % % % % % % % %
41-
tic;
42-
%d = velo_Domain;
43-
d = af_Domain;
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%% Run SAIL
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runTime = tic;
4446
output = sail(p,d);
45-
disp(['Runtime: ' seconds2human(toc)]);
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47-
%% Create Prediction Map from produced surrogate
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% Adjust hyperparameters
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p.nChildren = 250;
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p.nGens = 500;
51-
52-
p.display.illu = true; p.display.illuMod = 100;
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predMap = createPredictionMap(output.model,p,d,'featureRes',[50 50]);
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viewMap(predMap.fitness, d, predMap.edges) % View Prediction Map
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47+
disp(['Runtime: ' seconds2human(toc(runTime))]);
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%% Create New Prediction Map from produced surrogate
50+
%
51+
% % Adjust hyperparameters
52+
% p.nGens = 2*p.nGens;
53+
%
54+
% [predMap, percImproved] = createPredictionMap(...
55+
% output.model,... % Model for evaluation
56+
% output.model{1}.trainInput,... % Initial solutions
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% p,d,'featureRes',[25 25]); % Hyperparameters
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%
59+
% save('sailTest.mat','output','p','d','predMap','rTest');
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‎sail/createPredictionMap.m

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function predMap = createPredictionMap(gpModels,p,d, varargin)
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function [predMap, percImproved, rTest] = createPredictionMap(gpModels,observation,p,d, varargin)
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%createPredictionMap - Produce prediction map from surrogate model
33
%
4-
% Syntax: predictionMap = createPredictionMap(gpModels,p,d)
4+
% Syntax: predictionMap = createPredictionMap(gpModels,observation,p,d)
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%
66
% Inputs:
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% gpModels - GP model produced by SAIL
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% p - SAIL hyperparameter struct
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% d - Domain definition struct
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% gpModels - [cell] GP model(s) produced by SAIL
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% observation- [N X GenomeLength] Known inputs
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% p - [struct] SAIL hyperparameters
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% d - [struct] Domain definition
1011
%
1112
% Outputs:
1213
% predMap - prediction map
13-
% .fitness [Rows X Columns]
14-
% .genes [Rows X Columns X GenomeLength]
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% .'otherVals' [Rows X Columns]
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% .fitness [FeatureRes(1) X FeatureRes(2)]
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% .genes [FeatureRes(1) X FeatureRes(2) X GenomeLength]
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% .'otherVals' [FeatureRes(1) X FeatureRes(2)]
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% percImproved [1 X nGens] - percentage of children which improved on elites
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%
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% Example:
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% p = sail;
19-
% d = af_Domain;
19+
% Example:
20+
% p = sail; d = af_Domain;
2021
% output = sail(d,p);
21-
% predMap = createPredictionMap(output.model,p,d,'featureRes',[50 50]);
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% viewMap(predMap.fitness,d, predMap.edges)
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% predMap = createPredictionMap(output.model,output.model{1}.trainInput,p,d,'featureRes',[50 50]);
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% viewMap(predMap.fitness,d)
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%
2425
% Other m-files required: mapElites nicheCompete updateMap d.createAcqFunction
2526
%
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2829
% Author: Adam Gaier
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% Bonn-Rhein-Sieg University of Applied Sciences (BRSU)
3031
% email: adam.gaier@h-brs.de
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% Jun 2017; Last revision: 03-Aug-2017
32+
% Jun 2017; Last revision: 01-Sep-2017
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34+
%% TODO: if no observations given take them from the gpModels
3335
%------------- INPUT PARSING -----------
3436
parse = inputParser;
3537
parse.addRequired('gpModels');
3638
parse.addRequired('p');
3739
parse.addRequired('d');
38-
parse.addOptional('nChildren',p.nChildren);
39-
parse.addOptional('nGens',p.nGens);
40-
parse.addOptional('featureRes',d.featureRes);
40+
parse.addRequired('observation');
41+
parse.addOptional('nChildren' ,p.nChildren);
42+
parse.addOptional('nGens' ,p.nGens);
43+
parse.addOptional('featureRes' ,d.featureRes);
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42-
parse.parse(gpModels,p,d,varargin{:});
45+
parse.parse(gpModels, observation, p, d, varargin{:});
4346
p.nChildren = parse.Results.nChildren;
4447
p.nGens = parse.Results.nGens;
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d.featureRes = parse.Results.featureRes;
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5154
acqFunction = feval(d.createAcqFunction,gpModels,d);
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5356
% Seed map with precisely evaluated solutions
54-
observation = gpModels{1}.trainInput;
5557
[fitness,predValues] = acqFunction(observation);
5658
predMap = createMap(d.featureRes, d.dof, d.extraMapValues);
5759
[replaced, replacement] = nicheCompete(observation, fitness, predMap, d);
5860
predMap = updateMap(replaced,replacement,predMap,fitness,observation,...
59-
predValues,d.extraMapValues);
61+
predValues,d.extraMapValues);
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6163
% Illuminate based on surrogate models
62-
predMap = mapElites(acqFunction,predMap,p,d);
64+
[predMap, percImproved] = mapElites(acqFunction,predMap,p,d);
65+
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%------------- END OF CODE --------------

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