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annotate core/magnatagatune/tests_evals/rbm_subspace/write_mat_results_ISMIR13RBM.m @ 0:e9a9cd732c1e tip

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first hg version after svn
author wolffd
date Tue, 10 Feb 2015 15:05:51 +0000
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wolffd@0 1 function [out, stats] = write_mat_results_ISMIR13RBM(dirin,fileout)
wolffd@0 2 % combine the test results from the directories supplied,
wolffd@0 3 % and recombine the bins to a coherent dataset
wolffd@0 4 %
wolffd@0 5 % allows for distinguishing different hidden State numbers
wolffd@0 6
wolffd@0 7 % folders = {'130505_svm_disttest_subsconv_rbm_largegrid_21_r2385','130505_svm_disttest_subsconv_rbm_largegrid_22_r2385','130505_svm_disttest_subsconv_rbm_largegrid_23_r2385','130505_svm_disttest_subsconv_rbm_largegrid_24_r2385','130506_svm_disttest_subsconv_rbm_largegrid_25_r2385'};
wolffd@0 8
wolffd@0 9
wolffd@0 10
wolffd@0 11 features = [];
wolffd@0 12 show = 1;
wolffd@0 13
wolffd@0 14 if nargin == 0
wolffd@0 15 dirin{1} = './';
wolffd@0 16 end
wolffd@0 17
wolffd@0 18 global comparison;
wolffd@0 19 global comparison_ids;
wolffd@0 20
wolffd@0 21 newout = [];
wolffd@0 22 thisdir = pwd;
wolffd@0 23 % loop through al lthe result directories and
wolffd@0 24 for diri = 1:numel(dirin)
wolffd@0 25
wolffd@0 26 % ---
wolffd@0 27 % go to directory and locate file
wolffd@0 28 % ---
wolffd@0 29 cd(dirin{diri});
wolffd@0 30
wolffd@0 31 u = dir();
wolffd@0 32 u = {u.name};
wolffd@0 33 [idx, strpos] = substrcellfind(u, '_finalresults.mat', 1);
wolffd@0 34
wolffd@0 35 if numel(idx) < 1
wolffd@0 36 error 'This directory contains no valid test data';
wolffd@0 37 end
wolffd@0 38
wolffd@0 39 % just one or more tests in this folder?
wolffd@0 40 if exist('file','var') && isnumeric(file)
wolffd@0 41 cprint(1, 'loading one result file');
wolffd@0 42 file = u{idx(file)};
wolffd@0 43 data = load(file);
wolffd@0 44 sappend(out,data.out);
wolffd@0 45 else
wolffd@0 46 for filei = 1:numel(idx)
wolffd@0 47 cprint(1, 'loading result file %i of %i',filei, numel(idx));
wolffd@0 48 file = u{idx(filei)};
wolffd@0 49 data = load(file);
wolffd@0 50 newout = sappend(newout,data.out);
wolffd@0 51 end
wolffd@0 52 end
wolffd@0 53 % reset act directory
wolffd@0 54 cd(thisdir);
wolffd@0 55 end
wolffd@0 56
wolffd@0 57
wolffd@0 58 % ---
wolffd@0 59 % filter C values!
wolffd@0 60 % ---
wolffd@0 61 allcs = zeros(numel(newout),1);
wolffd@0 62 for i=1:numel(newout)
wolffd@0 63 allcs(i) = newout(i).trainparams.C;
wolffd@0 64 end
wolffd@0 65 valididx = find(allcs == 1); % select C!
wolffd@0 66 newout = newout(valididx);
wolffd@0 67
wolffd@0 68
wolffd@0 69 % bundle all datasets
wolffd@0 70 fout = sameparamsubset(newout, 'dataset','');
wolffd@0 71 out = [];
wolffd@0 72 for ci=1:numel(fout)
wolffd@0 73 filteredout = fout{ci};
wolffd@0 74
wolffd@0 75
wolffd@0 76 ok_test = zeros(2, numel(filteredout));
wolffd@0 77 ok_train = zeros(2, numel(filteredout));
wolffd@0 78 ok_config = [];
wolffd@0 79
wolffd@0 80
wolffd@0 81 tmpout = filteredout(1);
wolffd@0 82 % cycle over all test sets and get new means
wolffd@0 83 for i=1:numel(filteredout)
wolffd@0 84 ok_test(:,i) = filteredout(i).mean_ok_test;
wolffd@0 85 ok_train(:,i) = filteredout(i).mean_ok_train;
wolffd@0 86 end
wolffd@0 87
wolffd@0 88 % save the stuff
wolffd@0 89 tmpout.mean_ok_test = mean(ok_test,2);
wolffd@0 90 tmpout.var_ok_test = var(ok_test,0,2);
wolffd@0 91 tmpout.mean_ok_train = mean(ok_train,2);
wolffd@0 92 tmpout.var_ok_train = var(ok_train,0,2);
wolffd@0 93
wolffd@0 94 tmpout.ok_test = ok_test;
wolffd@0 95 tmpout.ok_train = ok_train;
wolffd@0 96
wolffd@0 97 % put it in output structure
wolffd@0 98 out = sappend(out,tmpout);
wolffd@0 99 end
wolffd@0 100
wolffd@0 101 % ---
wolffd@0 102 % show results
wolffd@0 103 % ---
wolffd@0 104 if numel([out.mean_ok_test]) > 1 && show
wolffd@0 105
wolffd@0 106 % plot means % plot std = sqrt(var) % plot training results
wolffd@0 107 figure;
wolffd@0 108 boxplot([out.mean_ok_test], sqrt([out.var_ok_test]), [out.mean_ok_train]);
wolffd@0 109 title (sprintf('Performance for all configs'));
wolffd@0 110 end
wolffd@0 111
wolffd@0 112
wolffd@0 113
wolffd@0 114 % ---
wolffd@0 115 % write max. test success
wolffd@0 116 % ---
wolffd@0 117 mean_ok_test = [out.mean_ok_test];
wolffd@0 118 [val, idx] = max(mean_ok_test(1,:));
wolffd@0 119 if show
wolffd@0 120 fprintf(' --- Maximal test set success: nr. %d, %3.2f percent. train result %3.2f percent. --- \n', idx, val * 100,out(idx).mean_ok_train(1,:)*100)
wolffd@0 121 end
wolffd@0 122
wolffd@0 123
wolffd@0 124 % ---
wolffd@0 125 % write max. test/train success
wolffd@0 126 % ---
wolffd@0 127 mean_ok_train = [out.mean_ok_train];
wolffd@0 128 [val, idx] = max(mean_ok_train(1,:));
wolffd@0 129 if show
wolffd@0 130 fprintf(' --- Maximal train set success: nr. %d, %3.2f percent, test result %3.2f percent. --- \n', idx, val * 100,out(idx).mean_ok_test(1,:)*100)
wolffd@0 131 end
wolffd@0 132
wolffd@0 133 % save this summary
wolffd@0 134 save([hash(strcat(dirin{:}),'md5') '_summary'], 'out');
wolffd@0 135
wolffd@0 136
wolffd@0 137
wolffd@0 138 % ---
wolffd@0 139 % build index of these feature configurations without num_hid
wolffd@0 140 % ---
wolffd@0 141 [fout2,param_hash,idxhash] = sameparamsubset(out, '','rbm_hidNum');
wolffd@0 142 for ci = 1:numel(fout2)
wolffd@0 143
wolffd@0 144 % search for the training param index
wolffd@0 145 found = find(idxhash{ci}== idx);
wolffd@0 146 if ~isempty(found)
wolffd@0 147 out_withsameconfig = fout2{ci};
wolffd@0 148 break;
wolffd@0 149 end
wolffd@0 150 end
wolffd@0 151
wolffd@0 152
wolffd@0 153 end
wolffd@0 154
wolffd@0 155 function [out, param_hash,idx] = sameparamsubset(in, ignoret,ignoref)
wolffd@0 156 % ---
wolffd@0 157 % build index of all existing configurations
wolffd@0 158 % ---
wolffd@0 159 param_hash = cell(numel(in),1);
wolffd@0 160 for i=1:numel(in)
wolffd@0 161 params = struct('trainparams',in(i).trainparams, ...
wolffd@0 162 'fparams',in(i).fparams);
wolffd@0 163
wolffd@0 164 % remove the dataset param
wolffd@0 165 if ~isempty(ignoret)
wolffd@0 166 params.trainparams = rmfield(params.trainparams,ignoret);
wolffd@0 167 end
wolffd@0 168 if ~isempty(ignoref)
wolffd@0 169 params.fparams = rmfield(params.fparams,ignoref);
wolffd@0 170 end
wolffd@0 171
wolffd@0 172 phash = hash(xml_format(params),'md5');
wolffd@0 173 param_hash{i} = phash;
wolffd@0 174 end
wolffd@0 175
wolffd@0 176 % ---
wolffd@0 177 % recombine the data for different datasets!
wolffd@0 178 % ---
wolffd@0 179 cvals = unique(param_hash);
wolffd@0 180
wolffd@0 181 out = {};
wolffd@0 182 for ci=1:numel(cvals)
wolffd@0 183 idx{ci} = strcellfind(param_hash,cvals(ci),1);
wolffd@0 184 out{ci} = in(idx{ci});
wolffd@0 185 end
wolffd@0 186
wolffd@0 187 end
wolffd@0 188
wolffd@0 189 function boxplot(mean, std, train);
wolffd@0 190
wolffd@0 191 bar([train; mean]', 1.5);
wolffd@0 192 hold on;
wolffd@0 193 errorbar(1:size(mean,2), mean(1,:), std(1,:),'.');
wolffd@0 194 % plot(train,'rO');
wolffd@0 195 colormap(spring);
wolffd@0 196 axis([0 size(mean,2)+1 max(0, min(min([train mean] - 0.1))) max(max([train mean] + 0.1))]);
wolffd@0 197 end