Added a skeleton of experiments.

.gitignore

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+devenv
+env
+
+.idea
+*.iml
+*.lock
+*.out
+
+AGModels/
+AutogluonModels/
+experiment/AutogluonModels/
+datasets/
+instanceSpace-master/
+imbalanced-ensemble/
+logs/
+openml_cache/
+test/
+config_spaces/classifiers_tpot/
+stash/
+
+__pycache__/
+*.pkl

InstanceSpace-master/.gitattributes

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+# Auto detect text files and perform LF normalization
+* text=auto

InstanceSpace-master/.github/ISSUE_TEMPLATE/bug_report.md

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+---
+name: Bug report
+about: Create a report to help us improve
+title: ''
+labels: ''
+assignees: ''
+
+---
+
+**Describe the bug**
+A clear and concise description of what the bug is.
+
+**To Reproduce**
+Steps to reproduce the behavior:
+1. Go to '...'
+2. Click on '....'
+3. Scroll down to '....'
+4. See error
+
+**Expected behavior**
+A clear and concise description of what you expected to happen.
+
+**Screenshots**
+If applicable, add screenshots to help explain your problem.
+
+**Desktop (please complete the following information):**
+ - OS: [e.g. iOS]
+ - Browser [e.g. chrome, safari]
+ - Version [e.g. 22]
+
+**Smartphone (please complete the following information):**
+ - Device: [e.g. iPhone6]
+ - OS: [e.g. iOS8.1]
+ - Browser [e.g. stock browser, safari]
+ - Version [e.g. 22]
+
+**Additional context**
+Add any other context about the problem here.

InstanceSpace-master/.github/ISSUE_TEMPLATE/feature_request.md

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+---
+name: Feature request
+about: Suggest an idea for this project
+title: ''
+labels: ''
+assignees: ''
+
+---
+
+**Is your feature request related to a problem? Please describe.**
+A clear and concise description of what the problem is. Ex. I'm always frustrated when [...]
+
+**Describe the solution you'd like**
+A clear and concise description of what you want to happen.
+
+**Describe alternatives you've considered**
+A clear and concise description of any alternative solutions or features you've considered.
+
+**Additional context**
+Add any other context or screenshots about the feature request here.

InstanceSpace-master/.gitignore

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+*.asv
+*.zip
+*.png
+*.pdf
+*.mat
+*.db
+*.csv
+old_versions/*.*
+old_versions
+*.json

InstanceSpace-master/CLOISTER.m

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+function out = CLOISTER(X, A, opts)
+% -------------------------------------------------------------------------
+% CLOISTER.m
+% -------------------------------------------------------------------------
+%
+% By: Mario Andres Munoz Acosta
+%     School of Mathematics and Statistics
+%     The University of Melbourne
+%     Australia
+%     2020
+%
+% -------------------------------------------------------------------------
+
+disp('  -> CLOISTER is using correlation to estimate a boundary for the space.');
+
+nfeats = size(X,2);
+[rho,pval] = corr(X);
+rho = rho.*(pval<opts.pval);
+
+Xbnds = [min(X); max(X)];
+% if no feature selection. then make a note in the boundary construction
+% that it won't work, because nfeats is soo large that de2bi wont be able
+% to make a matrix.
+idx = de2bi(0:2^nfeats-1)+1;
+ncomb = size(idx,1);
+Xedge = zeros(ncomb,nfeats);
+remove = false(ncomb,1);
+for i=1:ncomb
+   ind = sub2ind([2 nfeats],idx(i,:),1:nfeats);
+   Xedge(i,:) = Xbnds(ind)';
+   for j=1:nfeats
+       for k=j+1:nfeats
+           % Check for valid points give the correlation trend
+           if rho(j,k)>opts.cthres && sign(Xedge(i,j))~=sign(Xedge(i,k))
+               remove(i) = true;
+           elseif rho(j,k)<-opts.cthres && sign(Xedge(i,j))==sign(Xedge(i,k))
+               remove(i) = true;
+           end
+           if remove(i)
+               break;
+           end
+       end
+       if remove(i)
+           break;
+       end
+   end
+end
+Zedge = Xedge*A';
+Kedge = convhull(Zedge(:,1),Zedge(:,2));
+out.Zedge = Zedge(Kedge,:);
+
+try
+    Xecorr = Xedge(~remove,:);
+    Zecorr = Xecorr*A';
+    Kecorr = convhull(Zecorr(:,1),Zecorr(:,2));
+    out.Zecorr = Zecorr(Kecorr,:);
+catch
+    disp('  -> The acceptable correlation threshold was too strict.');
+    disp('  -> The features are weakely correlated.')
+    disp('  -> Please consider increasing it.');
+    out.Zecorr = out.Zedge;
+end
+disp('-------------------------------------------------------------------------');
+disp('  -> CLOISTER has completed.');
+

InstanceSpace-master/FILTER.m

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+function [subsetIndex,isDissimilar,isVISA] = FILTER(X,Y,Ybin,opts)
+
+[ninst,nalgos] = size(Y);
+nfeats = size(X,2);
+
+subsetIndex = false(ninst,1);
+isDissimilar = true(ninst,1);
+isVISA = false(ninst,1);
+gamma = sqrt(nalgos/nfeats)*opts.mindistance;
+
+for ii=1:ninst
+    if ~subsetIndex(ii)
+        for jj=ii+1:ninst
+            if ~subsetIndex(jj)
+                Dx = pdist2(X(ii,:),X(jj,:));
+                Dy = pdist2(Y(ii,:),Y(jj,:));
+                Db = all(Ybin(ii,:) & Ybin(jj,:));
+                if  Dx <= opts.mindistance
+                    isDissimilar(jj) = false;
+                    switch opts.type
+                        case 'Ftr'
+                            subsetIndex(jj) = true;
+                        case 'Ftr&AP'
+                            if Dy <= gamma
+                                subsetIndex(jj) = true;
+                                isVISA(jj) = false;
+                            else
+                                isVISA(jj) = true;
+                            end
+                        case 'Ftr&Good'
+                            if Db
+                                subsetIndex(jj) = true;
+                                isVISA(jj) = false;
+                            else
+                                isVISA(jj) = true;
+                            end
+                        case 'Ftr&AP&Good'
+                            if Db
+                                if Dy <= gamma
+                                    subsetIndex(jj) = true;
+                                    isVISA(jj) = false;
+                                else
+                                    isVISA(jj) = true;
+                                end
+                            else
+                                isVISA(jj) = true;
+                            end
+                        otherwise
+                            disp('Invalid flag!')
+                    end
+                end
+            end
+        end
+    end
+end
+
+% Assess the uniformity of the data
+D = squareform(pdist(X(~subsetIndex,:)));
+ninst = size(D,1);
+D(eye(ninst,'logical')) = NaN;
+nearest = min(D,[],2,'omitnan');
+model.data.unif = 1-(std(nearest)./mean(nearest));
+disp(['Uniformity of the instance subset: ' num2str(model.data.unif,4)]);
+
+end