diff misc/RandomForestEMFeatureSelection.m @ 2:985cd163ba54

adding old matlab data some datasets

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/misc/RandomForestEMFeatureSelection.m	Thu Feb 09 16:48:03 2017 +0000
@@ -0,0 +1,80 @@
+clearvars;
+load('AdobeStratified.mat');
+morefeatures = true;
+idxvar = (1:1450);
+count = 1;
+featuredata = struct('IdxVar', [], 'FeatureNamesRanked', {}, 'FeatureImportance', [], 'OOBError', [], 'LastOOBError', [], 'EMClusters', [], 'AIC', [], 'PreviousAIC', []); 
+
+while(morefeatures)
+    DataTrain = DataTrain(:, idxvar);
+    FeatureNames = FeatureNames(idxvar);
+    idxvar = (1:length(FeatureNames));
+    fprintf('\n Growing a Random Forest of 200 trees using %i features\n',length(idxvar))
+    
+    rng(1945,'twister')
+    tic
+    options = statset('UseParallel', true);
+    b = TreeBagger(200, DataTrain, LabelsTrain,'OOBVarImp','On', 'SampleWithReplacement', 'Off', 'FBoot', 0.632, 'Options', options);
+    toc
+    
+    oobErr = oobError(b);
+    LastoobErr = oobErr(end);
+    
+    fprintf('\n The cumulative OOB Error at 200 trees is %f\n', LastoobErr);
+    
+    Indices = crossvalind('Kfold', size(DataTrain, 1), 10);
+    
+    AICInitial = 1e16;
+    AICNext = -1e16;
+    AICAvg = zeros(10, 1);
+    NumClusters = 1;
+    
+    while(AICNext <= AICInitial)
+        
+        if(NumClusters ~= 1)
+            AICInitial = AICNext; 
+        end
+        NumClusters = NumClusters + 1;
+        
+        fprintf('\n Performing EM using 10 fold CV and %i clusters and %i features\n', NumClusters, length(idxvar))
+        
+        for i = 1:10
+           
+            emidx = (Indices == i); emidx = ~emidx;
+            
+            EMDataTrain = DataTrain(emidx, :);
+            GMModelCV = fitgmdist(EMDataTrain, NumClusters, 'RegularizationValue', 1e-5);
+            AICAvg(i) = GMModelCV.AIC;
+        end
+        
+        AICNext = mean(AICAvg);
+        fprintf('The average AIC was %f\n', AICNext);
+    end
+    
+    FI = b.OOBPermutedVarDeltaError;
+    
+    [FI,I]=sort(FI, 'descend');
+    idxvar = idxvar(I);
+    FeatureNamesRanked = FeatureNames(I);
+    
+    featuredata(count).IdxVar = idxvar;
+    featuredata(count).FeatureNamesRanked = FeatureNamesRanked;
+    featuredata(count).FeatureImportance = FI;
+    featuredata(count).OOBError = oobErr;
+    featuredata(count).LastOOBError = LastoobErr;
+    featuredata(count).EMClusters = NumClusters;
+    featuredata(count).AIC = AICNext;
+    featuredata(count).PreviousAIC = AICInitial;    
+    
+    idxRemove = round((length(idxvar) / 100)* 1);
+    fprintf('\n %i features will be removed.\n', idxRemove)
+    idxRemove = (length(idxvar) - idxRemove);
+    idxvar = idxvar(1:idxRemove);
+    count = count + 1;
+    
+    save('Results1Percent.mat', 'featuredata');
+    
+    if(length(idxvar) == 2)
+        morefeatures = false;
+    end
+end
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