/
Utilities.m
646 lines (547 loc) · 32.1 KB
/
Utilities.m
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
classdef Utilities < handle
%UTILITIES Static class that contains several methods for configurating
% and running the experiments. It allows experiments CPU parallelization.
% Examples of integration with HTCondor are provided src/condor folder.
%
% UTILITIES methods:
% runExperiments - setting and running experiments
% runExperimentFold - Launchs a single experiment fold
% configureExperiment - sets configuration of the several experiments
% results - creates experiments reports
%
% This file is part of ORCA: https://github.com/ayrna/orca
% Original authors: Pedro Antonio Gutiérrez, María Pérez Ortiz, Javier Sánchez Monedero
% Citation: If you use this code, please cite the associated paper http://www.uco.es/grupos/ayrna/orreview
% Copyright:
% This software is released under the The GNU General Public License v3.0 licence
% available at http://www.gnu.org/licenses/gpl-3.0.html
properties
end
methods (Static = true)
function [logsDir] = runExperiments(expFile, varargin)
% RUNEXPERIMENTS Function for setting and running the experiments
% [LOGSDIR] = RUNEXPERIMENTS(EXPFILE) runs
% experiments described in EXPFILE and returns the folder
% name LOGSDIR that stores all the results. LOGSDIR is
% generated based on the date and time of the system.
%
% [LOGSDIR] = RUNEXPERIMENTS(EXPFILE, options) runs
% experiments described in EXPFILE and returns the folder
% name LOGSDIR that stores all the results. Options are:
% - 'parallel': 'false' or 'true' to activate CPU parallel
% processing of databases's folds. Default is 'false'
% - 'numcores': default maximum number of cores or desired
% number. If parallel = 1 and numcores <2 it sets the number
% to maximum number of cores.
% - 'closepool': whether to close or not the pool after
% experiments. Default 'true'. Disabling it can speed
% up consecutive calls to runExperiments.
%
% Examples:
%
% Runs parallel folds with 3 workers:
% Utilities.runExperiments('tests/cvtests-30-holdout/kdlor.ini', 'parallel', 1, 'numcores', 3)
% Runs parallel folds with max workers:
% Utilities.runExperiments('tests/cvtests-30-holdout/kdlor.ini', 'parallel', 1)
% Runs parallel folds with max workers and do not close the
% pool:
% Utilities.runExperiments('tests/cvtests-30-holdout/kdlor.ini', 'parallel', 1, 'closepool', false)
% Utilities.runExperiments('tests/cvtests-30-holdout/svorim.ini', 'parallel', 1, 'closepool', false)
%
addpath(fullfile(fileparts(which('Utilities.m')),'../Measures'));
addpath(fullfile(fileparts(which('Utilities.m')),'../Algorithms'));
disp('Setting up experiments...');
% TODO: move ID generation to configureExperiment?
c = clock;
dirSuffix = [num2str(c(1)) '-' num2str(c(2)) '-' num2str(c(3)) '-' num2str(c(4)) '-' num2str(c(5)) '-' num2str(uint8(c(6)))];
logsDir = Utilities.configureExperiment(expFile,dirSuffix);
expFiles = dir([logsDir '/' 'exp-*']);
% Parse options.
op = Utilities.parseParArgs(varargin);
myExperiment = Experiment;
report_sum = zeros(numel(expFiles),1);
if op.parallel
Utilities.preparePool(op.numcores)
if (exist ('OCTAVE_VERSION', 'builtin') > 0)
logsCell = cell(numel(expFiles),1);
logsCell(:) = logsDir;
report_sum = parcellfun(op.numcores,@(varargin) Utilities.octaveParallelAuxFunction(varargin{:}),num2cell(expFiles),logsCell);
else
parfor i=1:numel(expFiles)
if ~strcmp(expFiles(i).name(end), '~')
report_sum(i) = Utilities.octaveParallelAuxFunction(expFiles(i), logsDir);
end
end
end
Utilities.closePool()
else
for i=1:numel(expFiles)
if ~strcmp(expFiles(i).name(end), '~')
%disp(['Running experiment ', expFiles(i).name]);
%myExperiment.launch([logsDir '/' expFiles(i).name]);
report_sum(i) = Utilities.octaveParallelAuxFunction(expFiles(i), logsDir);
end
end
end
disp('Calculating results...');
% If any ini file activates the flag, the results are processed
% with the 'report_sum = 1' flag.
report_sum_flag = any(report_sum);
% Train results (note last argument)
Utilities.results([logsDir '/' 'Results'],'report_sum', report_sum_flag, 'train', true);
% Test results
Utilities.results([logsDir '/' 'Results'], 'report_sum', report_sum_flag);
%rmpath('../Measures');
%rmpath('../Algorithms');
end
function [report_sum] = octaveParallelAuxFunction(experimentToRun,logsDir)
% OCTAVEPARALLELAUXFUNCTION Function for running one experiment file
% It is used in Octave because it Octave does not have parfor
% OCTAVEPARALLELAUXFUNCTION(EXPERIMENT,LOGSDIR) run the experiment
% named EXPERIMENT and contained in the folder LOGSDIR
if ~strcmp(experimentToRun.name(end), '~')
myExperiment = Experiment;
disp(['Running experiment ', experimentToRun.name]);
myExperiment.launch([logsDir '/' experimentToRun.name]);
report_sum = myExperiment.report_sum;
end
end
function results(experiment_folder,varargin)
% RESULTS Function for computing the results
% RESULTS(EXPERIMENT_FOLDER) computes results of predictions
% stored in EXPERIMENT_FOLDER. It generates CSV files with
% several performance metrics of the testing (generalization)
% predictions.
% * |mean-results_test.csv|: CSV file with datasets in files
% and performance metrics in columns. For each metric two columns
% are created (mean and standard deviation considering the _k_ folds
% of the experiment).
% * |mean-results_matrices_sum_test.csv|: CSV file with
% performance metrics calculated using the sum of all the
% confussion matrices of the _k_ experiments (as Weka does). Each column
% presents the performance of this single matrix.
%
% RESULTS(EXPERIMENT_FOLDER,'TRAIN', true) same as
% RESULTS(EXPERIMENT_FOLDER) but calculates performance in train
% data. It can be usefull to evaluate overfitting.
%
% See also MEASURES/MZE, MEASURES/MAE, MEASURES/AMAE, MEASURES/CCR,
% MEASURES/MMAE, MEASURES/GM, MEASURES/MS, MEASURES/Spearman,
% MEASURES/Tkendall, MEASURES/Wkappa
addpath(fullfile(fileparts(which('Utilities.m')),'../Measures'));
addpath(fullfile(fileparts(which('Utilities.m')),'../Algorithms'));
opt.train = false;
opt.report_sum = false;
opt = parsevarargs(opt, varargin);
experiments = dir(experiment_folder);
for i=1:numel(experiments)
if ~(any(strcmp(experiments(i).name, {'.', '..'}))) && experiments(i).isdir
disp([experiment_folder '/' experiments(i).name '/' 'dataset'])
fid = fopen([experiment_folder '/' experiments(i).name '/' 'dataset'],'r');
datasetPath = fgetl(fid);
fclose(fid);
if opt.train
predicted_files = dir([experiment_folder '/' experiments(i).name '/' 'Predictions' '/' 'train_*']);
else
predicted_files = dir([experiment_folder '/' experiments(i).name '/' 'Predictions' '/' 'test_*']);
end
% Check if we have a missing fold experiment.
% -2 is to compensate . and ..
predicted_files_train = dir([experiment_folder '/' experiments(i).name '/' 'Predictions' '/' 'train_*']);
predicted_files_test = dir([experiment_folder '/' experiments(i).name '/' 'Predictions' '/' 'test_*']);
if (numel(predicted_files_train)+numel(predicted_files_test)) ~= numel(dir(datasetPath)) -2
warning(sprintf('\n *********** \n The execution of some folds failed. Number of experiments differs from number of train-test files. \n *********** \n'))
end
time_files = dir([experiment_folder '/' experiments(i).name '/' 'Times' '/' '*.*']);
hyp_files = dir([experiment_folder '/' experiments(i).name '/' 'OptHyperparams' '/' '*.*']);
if opt.train
guess_files = dir([experiment_folder '/' experiments(i).name '/' 'Guess' '/' 'train_*']);
else
guess_files = dir([experiment_folder '/' experiments(i).name '/' 'Guess' '/' 'test_*']);
end
% Discard "." and ".."
if ~(exist ('OCTAVE_VERSION', 'builtin') > 0)
time_files = time_files(3:numel(time_files));
hyp_files = hyp_files(3:numel(hyp_files));
end
if opt.train
real_files = dir([datasetPath '/' 'train_*']);
else
real_files = dir([datasetPath '/' 'test_*']);
end
act = cell(1, numel(predicted_files));
pred = cell(1, numel(predicted_files));
proj = cell(1, numel(guess_files));
times = zeros(3,numel(predicted_files));
param = [];
for j=1:numel(predicted_files)
pred{j} = importdata([experiment_folder '/' experiments(i).name '/' 'Predictions' '/' predicted_files(j).name]);
times(:,j) = importdata([experiment_folder '/' experiments(i).name '/' 'Times' '/' time_files(j).name]);
proj{j} = importdata([experiment_folder '/' experiments(i).name '/' 'Guess' '/' guess_files(j).name]);
if ~isempty(hyp_files)
struct_hyperparams(j) = importdata([experiment_folder '/' experiments(i).name '/' 'OptHyperparams' '/' hyp_files(j).name],',');
for z = 1:numel(struct_hyperparams(j).data)
param(z,j) = struct_hyperparams(j).data(z);
end
end
actual = importdata([datasetPath '/' real_files(j).name]);
act{j} = actual(:,end);
end
names = {'Dataset', 'Acc', 'GM', 'MS', 'MAE', 'AMAE', 'MMAE','RSpearman', 'Tkendall', 'Wkappa', 'TrainTime', 'TestTime', 'CrossvalTime'};
if ~isempty(hyp_files)
for j=1:numel(struct_hyperparams(1).textdata)
names{numel(names)+1} = struct_hyperparams(1).textdata{j};
end
end
if exist ('OCTAVE_VERSION', 'builtin') > 0
accs = cell2mat(cellfun(@(varargin) CCR.calculateMetric(varargin{:}), act, pred, 'UniformOutput', false)) * 100;
gms = cell2mat(cellfun(@(varargin) GM.calculateMetric(varargin{:}), act, pred, 'UniformOutput', false)) * 100;
mss = cell2mat(cellfun(@(varargin) MS.calculateMetric(varargin{:}), act, pred, 'UniformOutput', false)) * 100;
maes = cell2mat(cellfun(@(varargin) MAE.calculateMetric(varargin{:}), act, pred, 'UniformOutput', false));
amaes = cell2mat(cellfun(@(varargin) AMAE.calculateMetric(varargin{:}), act, pred, 'UniformOutput', false));
maxmaes = cell2mat(cellfun(@(varargin) MMAE.calculateMetric(varargin{:}), act, pred, 'UniformOutput', false));
spearmans = cell2mat(cellfun(@(varargin) Spearman.calculateMetric(varargin{:}), act, pred, 'UniformOutput', false));
kendalls = cell2mat(cellfun(@(varargin) Tkendall.calculateMetric(varargin{:}), act, pred, 'UniformOutput', false));
wkappas = cell2mat(cellfun(@(varargin) Wkappa.calculateMetric(varargin{:}), act, pred, 'UniformOutput', false));
else
accs = cell2mat(cellfun(@CCR.calculateMetric, act, pred, 'UniformOutput', false)) * 100;
gms = cell2mat(cellfun(@GM.calculateMetric, act, pred, 'UniformOutput', false)) * 100;
mss = cell2mat(cellfun(@MS.calculateMetric, act, pred, 'UniformOutput', false)) * 100;
maes = cell2mat(cellfun(@MAE.calculateMetric, act, pred, 'UniformOutput', false));
amaes = cell2mat(cellfun(@AMAE.calculateMetric, act, pred, 'UniformOutput', false));
maxmaes = cell2mat(cellfun(@MMAE.calculateMetric, act, pred, 'UniformOutput', false));
spearmans = cell2mat(cellfun(@Spearman.calculateMetric, act, pred, 'UniformOutput', false));
kendalls = cell2mat(cellfun(@Tkendall.calculateMetric, act, pred, 'UniformOutput', false));
wkappas = cell2mat(cellfun(@Wkappa.calculateMetric, act, pred, 'UniformOutput', false));
end
results_matrix = [accs; gms; mss; maes; amaes; maxmaes; spearmans; kendalls; wkappas; times(1,:); times(2,:); times(3,:)];
if ~isempty(hyp_files)
for j=1:numel(struct_hyperparams(1).textdata)
results_matrix = [results_matrix ; param(j,:) ];
end
end
results_matrix = results_matrix';
% Results for the independent dataset
if opt.train
fid = fopen([experiment_folder '/' experiments(i).name '/' 'results_train.csv'],'w');
else
fid = fopen([experiment_folder '/' experiments(i).name '/' 'results_test.csv'],'w');
end
for h = 1:numel(names)
fprintf(fid, '%s,', names{h});
end
fprintf(fid,'\n');
for h = 1:size(results_matrix,1)
fprintf(fid, '%s,', real_files(h).name);
for z = 1:size(results_matrix,2)
fprintf(fid, '%f,', results_matrix(h,z));
end
fprintf(fid,'\n');
end
fclose(fid);
% Confusion matrices and sum of confusion matrices
if opt.report_sum
if opt.train
fid = fopen([experiment_folder '/' experiments(i).name '/' 'matrices_train.txt'],'w');
else
fid = fopen([experiment_folder '/' experiments(i).name '/' 'matrices_test.txt'],'w');
end
J = length(unique(act{1}));
cm_sum = zeros(J);
for h = 1:size(results_matrix,1)
fprintf(fid, '%s\n----------\n', real_files(h).name);
cm = confusionmat(act{h},pred{h});
cm_sum = cm_sum + cm;
for ii = 1:size(cm,1)
for jj = 1:size(cm,2)
fprintf(fid, '%d ', cm(ii,jj));
end
fprintf(fid, '\n');
end
end
fclose(fid);
% Calculate metrics with the sum of confusion matrices
accs_sum = CCR.calculateMetric(cm_sum) * 100;
gms_sum = GM.calculateMetric(cm_sum) * 100;
mss_sum = MS.calculateMetric(cm_sum) * 100;
maes_sum = MAE.calculateMetric(cm_sum);
amaes_sum = AMAE.calculateMetric(cm_sum);
maxmaes_sum = MMAE.calculateMetric(cm_sum);
spearmans_sum = Spearman.calculateMetric(cm_sum);
kendalls_sum = Tkendall.calculateMetric(cm_sum);
wkappas_sum = Wkappa.calculateMetric(cm_sum);
results_matrix_sum = [accs_sum; gms_sum; mss_sum; maes_sum; amaes_sum; maxmaes_sum; spearmans_sum; kendalls_sum; wkappas_sum; sum(times(1,:)); sum(times(2,:)); sum(times(3,:))];
results_matrix_sum = results_matrix_sum';
end
means = mean(results_matrix,1);
stdev = std(results_matrix,0,1);
if opt.train
if ~exist([experiment_folder '/' 'mean-results_train.csv'],'file')
add_head = 1;
else
add_head = 0;
end
fid = fopen([experiment_folder '/' 'mean-results_train.csv'],'at');
else
if ~exist([experiment_folder '/' 'mean-results_test.csv'],'file')
add_head = 1;
else
add_head = 0;
end
fid = fopen([experiment_folder '/' 'mean-results_test.csv'],'at');
end
if add_head
fprintf(fid, 'Dataset-Experiment,');
for h = 2:numel(names)
fprintf(fid, 'Mean%s,Std%s,', names{h},names{h});
end
fprintf(fid,'\n');
end
fprintf(fid, '%s,', experiments(i).name);
for h = 1:numel(means)
fprintf(fid, '%f,%f,', means(h), stdev(h));
end
fprintf(fid,'\n');
fclose(fid);
% Confusion matrices and sum of confusion matrices
if opt.report_sum
if opt.train
fid = fopen([experiment_folder '/' 'mean-results_matrices_sum_train.csv'],'at');
else
fid = fopen([experiment_folder '/' 'mean-results_matrices_sum_test.csv'],'at');
end
if add_head
fprintf(fid, 'Dataset-Experiment,');
for h = 2:numel(names)
fprintf(fid, '%s,', names{h});
end
fprintf(fid,'\n');
end
fprintf(fid, '%s,', experiments(i).name);
for h = 1:numel(results_matrix_sum)
fprintf(fid, '%f,', results_matrix_sum(h));
end
fprintf(fid,'\n');
fclose(fid);
end
end
end
rmpath(fullfile(fileparts(which('Utilities.m')),'../Measures'));
rmpath(fullfile(fileparts(which('Utilities.m')),'../Algorithms'));
end
function logsDir = configureExperiment(expFile,dirSuffix)
% CONFIGUREEXPERIMENT Function for setting the configuration of the
% different experiments.
% LOGSDIR = CONFIGUREEXPERIMENT(EXPFILE,DIRSUFFIX) parses EXPFILE and
% generates single experiment files describing individual experiment
% of each fold. It also creates folders to store predictions
% and models for all the partitions. All the resources are
% created int exp-DIRSUFFIX folder.
if( ~(exist(expFile,'file')))
error('The file %s does not exist\n',expFile);
end
logsDir = ['Experiments' '/' 'exp-' dirSuffix];
resultsDir = [logsDir '/' 'Results'];
if ~exist('Experiments','dir')
mkdir('Experiments');
end
mkdir(logsDir);
mkdir(resultsDir);
% Load and parse conf file
cObj = Config(expFile);
num_experiment = numel(cObj.exps);
for e = 1:num_experiment
expObj = cObj.exps{e};
id_experiment = expObj.expId;
directory = expObj.general('basedir');
if ~(exist(directory,'dir'))
error('Datasets directory "%s" does not exist', directory)
end
datasets = expObj.general('datasets');
conf_file = [logsDir '/' 'exp-' id_experiment];
[matchstart,matchend,tokenindices,matchstring,tokenstring,tokenname,datasetsList] = regexpi(datasets,',');
% Check that all datasets partitions are accesible
% The method checkDatasets calls error
Utilities.checkDatasets(directory, datasets);
[train, test] = Utilities.processDirectory(directory,datasetsList);
% Generate one config file and corresponding directories
% for each fold.
for i=1:numel(train)
aux_directory = [resultsDir '/' datasetsList{i} '-' id_experiment];
mkdir(aux_directory);
mkdir([aux_directory '/' 'OptHyperparams']);
mkdir([aux_directory '/' 'Times']);
mkdir([aux_directory '/' 'Models']);
mkdir([aux_directory '/' 'Predictions']);
mkdir([aux_directory '/' 'Guess']);
file = [resultsDir '/' datasetsList{i} '-' id_experiment '/' 'dataset'];
fich = fopen(file,'w');
fprintf(fich, [directory '/' datasetsList{i} '/' 'matlab']);
fclose(fich);
runfolds = numel(train{i});
for j=1:runfolds
iniFile = [conf_file '-' datasetsList{i} '-' num2str(j) '.ini'];
expObj.general('directory') = [directory '/' datasetsList{i} '/' 'matlab'];
expObj.general('train') = train{i}(j).name;
expObj.general('test') = test{i}(j).name ;
expObj.general('results') = [resultsDir '/' datasetsList{i} '-' id_experiment];
expObj.writeIni(iniFile);
end
end
end
end
function runExperimentFold(confFile)
% RUNEXPERIMENTFOLD(CONFFILE) launch a single experiment described in
% file CONFFILE
addpath(fullfile(fileparts(which('Utilities.m')),'../Measures'));
addpath(fullfile(fileparts(which('Utilities.m')),'../Algorithms'));
auxiliar = Experiment;
auxiliar.launch(confFile);
rmpath(fullfile(fileparts(which('Utilities.m')),'../Measures'));
rmpath(fullfile(fileparts(which('Utilities.m')),'../Algorithms'));
end
end
methods(Static = true, Access = private)
function [trainFileNames, testFileNames] = processDirectory(directory, dataSetNames)
% PROCESSDIRECTORY Function to get all the train and test pair of
% files of dataset's folds
% [TRAINFILENAMES, TESTFILENAMES] = PROCESSDIRECTORY(DIRECTORY, DATASETNAMES)
% process comma separated list of datasets names in DATASETNAMES.
% All the dataset's folders need to be stored in DIRECTORY.
% Returns all the pairs of train-test files in TRAINFILENAMES and
% TESTFILENAMES.
% [TRAINFILENAMES, TESTFILENAMES] = PROCESSDIRECTORY(DIRECTORY,
% 'all') process all datasets in DIRECTORY.
dbs = dir(directory);
dbs(2) = [];
dbs(1) = [];
validDataSets = 1;
trainFileNames = cell(numel(dataSetNames),1);
testFileNames = cell(numel(dataSetNames),1);
for j=1:numel(dataSetNames)
dsdirectory = [directory '/' dataSetNames{j}];
if(isdir(dsdirectory))
file_expr = [dsdirectory '/' 'matlab' '/' 'train_' dataSetNames{j} '.*'];
trainFileNames{validDataSets} = dir(file_expr);
file_expr = [dsdirectory '/' 'matlab' '/' 'test_' dataSetNames{j} '.*'];
testFileNames{validDataSets} = dir(file_expr);
validDataSets = validDataSets + 1;
end
end
end
function checkDatasets(basedir, datasets)
% CHECKDATASETS Test datasets are accessible and with expected
% names. Launch error in case a dataset is not found.
% CHECKDATASETS(BASEDIR, DATASETS) tests all DATASETS (comma
% separated list of datasets) in directory BASEDIR.
if ~exist(basedir,'dir')
error('Datasets directory "%s" does not exist', basedir)
end
dsdirsCell = regexp(datasets, '((\w|-|_)+(\w*))','tokens');
for i=1:length(dsdirsCell) % skip . and ..
dsName = dsdirsCell{i};
dsName = dsName{:};
if ~exist([basedir '/' dsName],'dir')
error('Dataset directory "%s" does not exist', [basedir '/' dsName])
end
dsTrainFiles = dir([basedir '/' dsName '/matlab/train*']);
% Test every train file has a test file
for f=1:length(dsTrainFiles)
trainName = [basedir '/' dsName '/matlab/' dsTrainFiles(f).name];
testName = strrep(trainName, 'train', 'test');
try
trainData = load(trainName);
testData = load(testName);
catch
error('Cannot read train and test files "%s", "%s"', trainName, testName)
end
if size(trainData,2) ~= size(testData,2)
error('Train and test data dimensions do not agree for dataset "%s"', dsName)
end
end
end
end
function preparePool(numcores)
%PREPAREPOOL(NUMCORES) creates a pool of workers. Function to
%abstract code from different matlab versions. Adapt the pool
%to the desired number of cores. If there is a current pool with
%desired number of cores do not open again to save time
if (exist ('OCTAVE_VERSION', 'builtin') > 0)
maximum_ncores = nproc;
else
maximum_ncores = feature('numCores');
end
% Adjust number of cores
if numcores > maximum_ncores
disp(['Number of cores was too high and was set up to the maximum available: ' num2str(feature('numCores')) ])
numcores = maximum_ncores;
end
% Check size of the pool
if (exist ('OCTAVE_VERSION', 'builtin') > 0)
pkg load parallel;
else
if verLessThan('matlab', '8.3')
poolsize = matlabpool('size');
if poolsize > 0
if poolsize ~= numcores
matlabpool close;
matlabpool(numcores);
end
else
matlabpool(numcores);
end
else
poolobj = gcp('nocreate'); % If no pool, do not create new one.
if ~isempty(poolobj)
if poolobj.NumWorkers ~= numcores
numcores = poolobj.NumWorkers;
delete(gcp('nocreate'))
parpool(numcores);
end
else
parpool(numcores);
end
end
end
end
function closePool()
if (exist ('OCTAVE_VERSION', 'builtin') > 0)
pkg unload parallel;
else
if verLessThan('matlab', '8.3')
isOpen = matlabpool('size') > 0;
if isOpen
matlabpool close;
end
else
delete(gcp('nocreate'))
end
end
end
function options = parseParArgs(varargin)
%OPTIONS = PARSEPARARGS(VARARGIN) parses parallelization
%options with are:
% - 'parallel': 'false' or 'true' to activate, default 'false'
% - 'numcores': default maximum number of cores or desired
% number. If parallel = 1 and numcores <2 it sets the number
% to maximum number of cores.
% - 'closepool': whether to close or not the pool after
% experiments. Default 'true'
% Solution adapted from https://stackoverflow.com/questions/2775263/how-to-deal-with-name-value-pairs-of-function-arguments-in-matlab#2776238
if (exist ('OCTAVE_VERSION', 'builtin') > 0)
maximum_ncores = nproc;
else
maximum_ncores = feature('numCores');
end
options = struct('parallel',false,'numcores',maximum_ncores,'closepool',true);
varargin = varargin{:};
if ~isempty(varargin)
options = parsevarargs(options, varargin);
if options.parallel && options.numcores <2
disp('Number of cores to low, setting to default number of cores')
options.numcores = maximum_ncores;
end
end
end
end
end