def test_spatiotemporal(path, subjects, conf_file, type_, **kwargs): conf = read_configuration(path, conf_file, type_) for arg in kwargs: conf[arg] = kwargs[arg] if arg == 'balance': balance = kwargs[arg] total_results = dict() data_path = conf['data_path'] conf['analysis_type'] = 'spatiotemporal' conf['analysis_task'] = type_ for subj in subjects: try: ds = load_dataset(data_path, subj, type_, **conf) except Exception, err: print err continue ds = detrend_dataset(ds, type_, **conf) if 'balance' in locals() and balance == True: if conf['label_included'] == 'all' and \ conf['label_dropped'] == 'none': ds = balance_dataset_timewise(ds, 'fixation') r = spatiotemporal(ds, **conf) total_results[subj] = r
def test_spatiotemporal(path, subjects, conf_file, type_, **kwargs): conf = read_configuration(path, conf_file, type_) for arg in kwargs: conf[arg] = kwargs[arg] if arg == 'balance': balance = kwargs[arg] total_results = dict() data_path = conf['data_path'] conf['analysis_type'] = 'spatiotemporal' conf['analysis_task'] = type_ for subj in subjects: try: ds = load_dataset(data_path, subj, type_, **conf) except Exception, err: print err continue ds = detrend_dataset(ds, type_, **conf) if 'balance' in locals() and balance == True: if conf['label_included'] == 'all' and \ conf['label_dropped'] == 'none': ds = balance_dataset_timewise(ds, 'fixation') r = spatiotemporal(ds, **conf) total_results[subj] = r
def pre_operations(self, **options): # dictionary of conditions used self.conditions = {k: options[k] for k in self._condition_names} logger.debug(self._default_conf.keys()) # On-fly change default options # A little bit dangerous!! for k, v in options.iteritems(): logger.debug(k) if k in self._default_conf.keys(): setattr(self, "_"+k, v) else: setattr(self, k, v) ds = self.ds_orig.copy() ds = change_target(ds, options['task']) ds = detrend_dataset(ds, self._data_type, **self._conf) ds = slice_dataset(ds, options['condition']) ds = normalize_dataset(ds, **self._conf) return ds
def test_clustering(path, subjects, analysis, conf_file, source='task', **kwargs): if source == 'task': target = 'rest' else: target = 'task' conf_src = read_configuration(path, conf_file, source) conf_tar = read_configuration(path, conf_file, target) ############################################## conf_src['label_included'] = 'all' conf_src['label_dropped'] = 'none' conf_src['mean_samples'] = 'True' ############################################## for arg in kwargs: conf_src[arg] = kwargs[arg] conf_tar[arg] = kwargs[arg] total_results = dict() data_path = conf_src['data_path'] for subj in subjects: try: ds_src = load_dataset(data_path, subj, source, **conf_src) ds_tar = load_dataset(data_path, subj, target, **conf_tar) except Exception, err: print err continue ds_src = detrend_dataset(ds_src, source, **conf_src) ds_tar = detrend_dataset(ds_tar, target, **conf_tar) if conf_src['label_included'] == 'all' and \ conf_src['label_dropped'] != 'fixation': ds_src = balance_dataset_timewise(ds_src, 'fixation') r = clustering_analysis(ds_src, ds_tar, analysis, **kwargs) total_results[subj] = r
def test_clustering(path, subjects, analysis, conf_file, source='task', **kwargs): if source == 'task': target = 'rest' else: target = 'task' conf_src = read_configuration(path, conf_file, source) conf_tar = read_configuration(path, conf_file, target) ############################################## conf_src['label_included'] = 'all' conf_src['label_dropped'] = 'none' conf_src['mean_samples'] = 'True' ############################################## for arg in kwargs: conf_src[arg] = kwargs[arg] conf_tar[arg] = kwargs[arg] total_results = dict() data_path = conf_src['data_path'] for subj in subjects: try: ds_src = load_dataset(data_path, subj, source, **conf_src) ds_tar = load_dataset(data_path, subj, target, **conf_tar) except Exception, err: print err continue ds_src = detrend_dataset(ds_src, source, **conf_src) ds_tar = detrend_dataset(ds_tar, target, **conf_tar) if conf_src['label_included'] == 'all' and \ conf_src['label_dropped'] != 'fixation': ds_src = balance_dataset_timewise(ds_src, 'fixation') r = clustering_analysis(ds_src, ds_tar, analysis, **kwargs) total_results[subj] = r
def load_subjectwise_ds(path, subjects, conf_file, task, extra_sa=None, **kwargs): """ extra_sa: dict or None, sample attributes added to the final dataset, they should be the same length as the subjects. subject: either a list of subjects or a csv file """ conf = read_configuration(os.path.join(path, conf_file), task) conf.update(kwargs) logger.debug(conf) data_path = conf['data_path'] if isinstance(subjects, str): subjects, extra_sa = load_subject_file(subjects) logger.info('Merging subjects from '+data_path) for i, subj in enumerate(subjects): ds = load_dataset(data_path, subj, task, **conf) ds = detrend_dataset(ds, task, **conf) ds = normalize_dataset(ds, **conf) # add extra samples if extra_sa != None: for k, v in extra_sa.iteritems(): if len(v) == len(subjects): ds.sa[k] = [v[i] for _ in range(ds.samples.shape[0])] # First subject if i == 0: ds_merged = ds.copy() else: ds_merged = vstack((ds_merged, ds)) ds_merged.a.update(ds.a) del ds return ds_merged, ['group'], conf
def _test_spatial(path, subjects, conf_file, type_, **kwargs): warnings.warn("Deprecated use test_spatial.", DeprecationWarning) conf = read_configuration(path, conf_file, type_) conf['analysis_type'] = 'spatial' conf['analysis_task'] = type_ for arg in kwargs: conf[arg] = kwargs[arg] total_results = dict() data_path = conf['data_path'] summarizers = [rs.DecodingSummarizer()] savers = [rs.DecodingSaver()] result = rs.ResultsCollection(conf, path, summarizers) for subj in subjects: print '------' try: ds = load_dataset(data_path, subj, type_, **conf) except Exception as err: print err continue ds = detrend_dataset(ds, type_, **conf) balancer = balance_dataset(**conf) for i, ds_ in enumerate(balancer.generate(ds)): logger.info("Balanced dataset n. %d" % (i + 1)) subj_ = "%s_%03d" % (subj, i + 1) ds_ = normalize_dataset(ds_, **conf) logger.info(ds_.summary()) r = spatial(ds_, **conf) total_results[subj_] = r subj_result = rs.SubjectResult(subj_, r, savers) result.add(subj_result) #result.save() result.summarize() conf['classes'] = np.unique(ds.targets) #save_results() #save_results(path, total_results, conf) return total_results, subj_result
def _test_spatial(path, subjects, conf_file, type_, **kwargs): warnings.warn("Deprecated use test_spatial.", DeprecationWarning) conf = read_configuration(path, conf_file, type_) conf['analysis_type'] = 'spatial' conf['analysis_task'] = type_ for arg in kwargs: conf[arg] = kwargs[arg] total_results = dict() data_path = conf['data_path'] summarizers = [rs.DecodingSummarizer()] savers = [rs.DecodingSaver()] result = rs.ResultsCollection(conf, path, summarizers) for subj in subjects: print '------' try: ds = load_dataset(data_path, subj, type_, **conf) except Exception, err: print err continue ds = detrend_dataset(ds, type_, **conf) balancer = balance_dataset(**conf) for i, ds_ in enumerate(balancer.generate(ds)): logger.info("Balanced dataset n. %d" % (i+1)) subj_ = "%s_%03d" % (subj, i+1) ds_ = normalize_dataset(ds_, **conf) logger.info(ds_.summary()) r = spatial(ds_, **conf) total_results[subj_] = r subj_result = rs.SubjectResult(subj_, r, savers) result.add(subj_result)
def test_searchlight(path, subjects, conf_file, type_, **kwargs): conf = read_configuration(path, conf_file, type_) for arg in kwargs: conf[arg] = kwargs[arg] conf['analysis_type'] = 'searchlight' conf['analysis_task'] = type_ total_results = dict() data_path = conf['data_path'] # summarizers = [rs.SearchlightSummarizer()] savers = [rs.SearchlightSaver()] result = rs.ResultsCollection(conf, path, summarizers) # for subj in subjects: ds = load_dataset(data_path, subj, type_, **conf) ds = detrend_dataset(ds, type_, **conf) r = searchlight(ds, **kwargs) subj_result = rs.SubjectResult(subj, r, savers) total_results[subj] = r result.add(subj_result) result.summarize() conf['classes'] = np.unique(ds.targets) #save_results() #save_results(path, total_results, conf) return result, r, subj_result
def test_searchlight(path, subjects, conf_file, type_, **kwargs): conf = read_configuration(path, conf_file, type_) for arg in kwargs: conf[arg] = kwargs[arg] conf['analysis_type'] = 'searchlight' conf['analysis_task'] = type_ total_results = dict() data_path = conf['data_path'] # summarizers = [rs.SearchlightSummarizer()] savers = [rs.SearchlightSaver()] result = rs.ResultsCollection(conf, path, summarizers) # for subj in subjects: ds = load_dataset(data_path, subj, type_, **conf) ds = detrend_dataset(ds, type_, **conf) r = searchlight(ds, **kwargs) subj_result = rs.SubjectResult(subj, r, savers) total_results[subj] = r result.add(subj_result) result.summarize() conf['classes'] = np.unique(ds.targets) #save_results() #save_results(path, total_results, conf) return result, r, subj_result
def _group_transfer_learning(path, subjects, analysis, conf_file, source='task', analysis_type='single', **kwargs): if source == 'task': target = 'rest' else: if source == 'rest': target = 'task' if source == 'saccade': target = 'face' else: if source == 'face': target = 'saccade' ############################################## ############################################## ## conf_src['label_included'] = 'all' ## ## conf_src['label_dropped'] = 'none' ## ## conf_src['mean_samples'] = 'False' ## ############################################## ############################################## if analysis_type == 'group': if path.__class__ == conf_file.__class__ == list: ds_src, s, conf_src = sources_merged_ds(path, subjects, conf_file, source, **kwargs) conf_src['permutations'] = 0 else: print 'In group analysis path, subjects and conf_file must be lists: \ Check configuration file and/or parameters!!' return 0 else: conf_src = read_configuration(path, conf_file, source) for arg in conf_src: if arg == 'map_list': map_list = conf_src[arg].split(',') r_group = spatial(ds_src, **conf_src) total_results = dict() total_results['group'] = r_group clf = r_group['classifier'] for subj_, conf_, path_ in zip(subjects, conf_file, path): for subj in subj_: print '-----------' r = dict() if len(subj_) > 1: conf_tar = read_configuration(path_, conf_, target) for arg in kwargs: conf_tar[arg] = kwargs[arg] data_path = conf_tar['data_path'] try: ds_tar = load_dataset(data_path, subj, target, **conf_tar) except Exception, err: print err continue ds_tar = detrend_dataset(ds_tar, target, **conf_tar) if conf_src['label_included'] == 'all' and \ conf_src['label_dropped'] != 'fixation': print 'Balancing dataset...' ds_src = balance_dataset_timewise(ds_src, 'fixation') predictions = clf.predict(ds_tar) pred = np.array(predictions) targets = ds_tar.targets for arg in r_group.keys(): r[arg] = copy.copy(r_group[arg]) r['targets'] = targets r['predictions'] = predictions r['fclf'] = clf c_m = ConfusionMatrix(predictions=pred, targets=targets) c_m.compute() r['confusion_target'] = c_m print c_m tr_pred = similarity_measure_mahalanobis(ds_tar, ds_src, r) r['mahalanobis_similarity'] = tr_pred #print tr_pred c_mat_mahala = ConfusionMatrix(predictions=tr_pred.T[1], targets=tr_pred.T[0]) c_mat_mahala.compute() r['confusion_mahala'] = c_mat_mahala d_prime, beta, c, c_new = signal_detection_measures(pred, targets, map_list) r['d_prime'] = d_prime print d_prime r['beta'] = beta r['c'] = c r['confusion_total'] = c_new ''' d_prime_maha, c_new_maha = d_prime_statistics(tr_pred.T[1], tr_pred.T[0], map_list) r['d_prime_maha'] = d_prime_maha r['confusion_tot_maha'] = c_new_maha ''' total_results[subj] = r
def get_merged_ds(path, subjects, conf_file, source='task', dim=3, **kwargs): #Mettere source e target nel conf! if source == 'task': target = 'rest' else: if source == 'rest': target = 'task' if source == 'saccade': target = 'face' else: if source == 'face': target = 'saccade' ds_merged_list = [] conf_src = read_configuration(path, conf_file, source) conf_tar = read_configuration(path, conf_file, target) ############################################## ############################################## ## conf_src['label_included'] = 'all' ## ## conf_src['label_dropped'] = 'none' ## ## conf_src['mean_samples'] = 'False' ## ############################################## ############################################## for arg in kwargs: conf_src[arg] = kwargs[arg] conf_tar[arg] = kwargs[arg] data_path = conf_src['data_path'] for subj in subjects: print '--------' try: ds_src = load_dataset(data_path, subj, source, **conf_src) ds_tar = load_dataset(data_path, subj, target, **conf_tar) except Exception, err: print err continue ds_src = detrend_dataset(ds_src, source, **conf_src) ds_tar = detrend_dataset(ds_tar, target, **conf_tar) if dim == 4: duration = np.min([e['duration'] for e in ds_src.a.events]) ds_tar = build_events_ds(ds_tar, duration, overlap=duration-1) ds_src = load_spatiotemporal_dataset(ds_src, duration=duration) print ds_src.samples.shape print ds_tar.samples.shape ds_src.sa['task'] = [source for s in range(ds_src.samples.shape[0])] ds_tar.sa['task'] = [target for s in range(ds_tar.samples.shape[0])] ds_merged = vstack((ds_src, ds_tar)) ds_merged.a.update(ds_src.a) print ds_merged.sa.task ds_merged_list.append(ds_merged) '''
def test_transfer_learning(path, subjects, analysis, conf_file, source='task', \ analysis_type='single', calculateSimilarity='True', **kwargs): if source == 'task': target = 'rest' else: if source == 'rest': target = 'task' if source == 'saccade': target = 'face' else: if source == 'face': target = 'saccade' p = kwargs['p'] ############################################## ############################################## ## conf_src['label_included'] = 'all' ## ## conf_src['label_dropped'] = 'none' ## ## conf_src['mean_samples'] = 'False' ## ############################################## ############################################## if analysis_type == 'group': if path.__class__ == conf_file.__class__ == list: ds_src, _, conf_src = sources_merged_ds(path, subjects, conf_file, source, **kwargs) ds_tar, subjects, conf_tar = sources_merged_ds( path, subjects, conf_file, target, **kwargs) conf_src['permutations'] = 0 conf_tar['permutations'] = 0 else: print 'In group analysis path, subjects and conf_file must be lists: \ Check configuration file and/or parameters!!' return 0 else: conf_src = read_configuration(path, conf_file, source) conf_tar = read_configuration(path, conf_file, target) for arg in kwargs: conf_src[arg] = kwargs[arg] conf_tar[arg] = kwargs[arg] data_path = conf_src['data_path'] conf_src['analysis_type'] = 'transfer_learning' conf_src['analysis_task'] = source conf_src['analysis_func'] = analysis.func_name for arg in conf_src: if arg == 'map_list': map_list = conf_src[arg].split(',') if arg == 'p_dist': p = float(conf_src[arg]) print p total_results = dict() summarizers = [ rs.CrossDecodingSummarizer(), rs.SimilaritySummarizer(), rs.DecodingSummarizer(), rs.SignalDetectionSummarizer(), ] savers = [ rs.CrossDecodingSaver(), rs.SimilaritySaver(), rs.DecodingSaver(), rs.SignalDetectionSaver(), ] collection = rs.ResultsCollection(conf_src, path, summarizers) for subj in subjects: print '-------------------' if (len(subjects) > 1) or (subj != 'group'): try: ds_src = load_dataset(data_path, subj, source, **conf_src) ds_tar = load_dataset(data_path, subj, target, **conf_tar) except Exception, err: print err continue # Evaluate if is correct to do further normalization after merging two ds. ds_src = detrend_dataset(ds_src, source, **conf_src) ds_tar = detrend_dataset(ds_tar, target, **conf_tar) if conf_src['label_included'] == 'all' and \ conf_src['label_dropped'] != 'fixation': print 'Balancing dataset...' ds_src = balance_dataset_timewise(ds_src, 'fixation') # Make cross-decoding r = transfer_learning(ds_src, ds_tar, analysis, **conf_src) # Now we have cross-decoding results we could process it pred = np.array(r['classifier'].ca.predictions) targets = r['targets'] c_m = ConfusionMatrix(predictions=pred, targets=targets) c_m.compute() r['confusion_target'] = c_m c_new = cross_decoding_confusion(pred, targets, map_list) r['confusion_total'] = c_new print c_new # Similarity Analysis if calculateSimilarity == 'True': if 'p' not in locals(): print 'Ciao!' mahala_data = similarity_measure(r['ds_tar'], r['ds_src'], r, p_value=p, method='mahalanobis') #r['mahalanobis_similarity'] = mahala_data for k_, v_ in mahala_data.items(): r[k_] = v_ r['confusion_mahala'] = mahala_data['confusion_mahalanobis'] else: #r['mahalanobis_similarity'] = [] r['confusion_mahala'] = 'Null' # Signal Detection Theory Analysis sdt_res = signal_detection_measures(c_new) for k_, v_ in sdt_res.items(): r[k_] = v_ ''' Same code of: r['d_prime'] = d_prime r['beta'] = beta r['c'] = c ''' total_results[subj] = r subj_result = rs.SubjectResult(subj, r, savers=savers) collection.add(subj_result)
def test_transfer_learning(path, subjects, analysis, conf_file, source='task', \ analysis_type='single', calculateSimilarity='True', **kwargs): if source == 'task': target = 'rest' else: if source == 'rest': target = 'task' if source == 'saccade': target = 'face' else: if source == 'face': target = 'saccade' p = kwargs['p'] ############################################## ############################################## ## conf_src['label_included'] = 'all' ## ## conf_src['label_dropped'] = 'none' ## ## conf_src['mean_samples'] = 'False' ## ############################################## ############################################## if analysis_type == 'group': if path.__class__ == conf_file.__class__ == list: ds_src, _, conf_src = sources_merged_ds(path, subjects, conf_file, source, **kwargs) ds_tar, subjects, conf_tar = sources_merged_ds(path, subjects, conf_file, target, **kwargs) conf_src['permutations'] = 0 conf_tar['permutations'] = 0 else: print 'In group analysis path, subjects and conf_file must be lists: \ Check configuration file and/or parameters!!' return 0 else: conf_src = read_configuration(path, conf_file, source) conf_tar = read_configuration(path, conf_file, target) for arg in kwargs: conf_src[arg] = kwargs[arg] conf_tar[arg] = kwargs[arg] data_path = conf_src['data_path'] conf_src['analysis_type'] = 'transfer_learning' conf_src['analysis_task'] = source conf_src['analysis_func'] = analysis.func_name for arg in conf_src: if arg == 'map_list': map_list = conf_src[arg].split(',') if arg == 'p_dist': p = float(conf_src[arg]) print p total_results = dict() summarizers = [rs.CrossDecodingSummarizer(), rs.SimilaritySummarizer(), rs.DecodingSummarizer(), rs.SignalDetectionSummarizer(), ] savers = [rs.CrossDecodingSaver(), rs.SimilaritySaver(), rs.DecodingSaver(), rs.SignalDetectionSaver(), ] collection = rs.ResultsCollection(conf_src, path, summarizers) for subj in subjects: print '-------------------' if (len(subjects) > 1) or (subj != 'group'): try: ds_src = load_dataset(data_path, subj, source, **conf_src) ds_tar = load_dataset(data_path, subj, target, **conf_tar) except Exception, err: print err continue # Evaluate if is correct to do further normalization after merging two ds. ds_src = detrend_dataset(ds_src, source, **conf_src) ds_tar = detrend_dataset(ds_tar, target, **conf_tar) if conf_src['label_included'] == 'all' and \ conf_src['label_dropped'] != 'fixation': print 'Balancing dataset...' ds_src = balance_dataset_timewise(ds_src, 'fixation') # Make cross-decoding r = transfer_learning(ds_src, ds_tar, analysis, **conf_src) # Now we have cross-decoding results we could process it pred = np.array(r['classifier'].ca.predictions) targets = r['targets'] c_m = ConfusionMatrix(predictions=pred, targets=targets) c_m.compute() r['confusion_target'] = c_m c_new = cross_decoding_confusion(pred, targets, map_list) r['confusion_total'] = c_new print c_new # Similarity Analysis if calculateSimilarity == 'True': if 'p' not in locals(): print 'Ciao!' mahala_data = similarity_measure(r['ds_tar'], r['ds_src'], r, p_value=p, method='mahalanobis') #r['mahalanobis_similarity'] = mahala_data for k_,v_ in mahala_data.items(): r[k_] = v_ r['confusion_mahala'] = mahala_data['confusion_mahalanobis'] else: #r['mahalanobis_similarity'] = [] r['confusion_mahala'] = 'Null' # Signal Detection Theory Analysis sdt_res = signal_detection_measures(c_new) for k_,v_ in sdt_res.items(): r[k_] = v_ ''' Same code of: r['d_prime'] = d_prime r['beta'] = beta r['c'] = c ''' total_results[subj] = r subj_result = rs.SubjectResult(subj, r, savers=savers) collection.add(subj_result)
for subj in subjects[:1]: conf = read_configuration(path, conf_file, task) # Load dataset ds_orig = load_dataset(path, subj, task, roi_labels={'conjunction': mask}, **conf) # Change the target ds_orig = change_target(ds_orig, target) # Process dataset ds_orig = detrend_dataset(ds_orig, task, **conf) # Balance dataset balancer = balance_dataset(balancer__count=5, **conf) for ev in evidences: for slice_condition in frames: selected_variables.update({ 'frame': [1, 2, 3, 4, 5], 'evidence': [ev] }) print selected_variables ds = slice_dataset(ds_orig, selected_variables)
def get_merged_ds(path, subjects, conf_file, source='task', dim=3, **kwargs): #Mettere source e target nel conf! if source == 'task': target = 'rest' else: if source == 'rest': target = 'task' if source == 'saccade': target = 'face' else: if source == 'face': target = 'saccade' ds_merged_list = [] conf_src = read_configuration(path, conf_file, source) conf_tar = read_configuration(path, conf_file, target) ############################################## ############################################## ## conf_src['label_included'] = 'all' ## ## conf_src['label_dropped'] = 'none' ## ## conf_src['mean_samples'] = 'False' ## ############################################## ############################################## for arg in kwargs: conf_src[arg] = kwargs[arg] conf_tar[arg] = kwargs[arg] data_path = conf_src['data_path'] for subj in subjects: print '--------' try: ds_src = load_dataset(data_path, subj, source, **conf_src) ds_tar = load_dataset(data_path, subj, target, **conf_tar) except Exception, err: print err continue ds_src = detrend_dataset(ds_src, source, **conf_src) ds_tar = detrend_dataset(ds_tar, target, **conf_tar) if dim == 4: duration = np.min([e['duration'] for e in ds_src.a.events]) ds_tar = build_events_ds(ds_tar, duration, overlap=duration - 1) ds_src = load_spatiotemporal_dataset(ds_src, duration=duration) print ds_src.samples.shape print ds_tar.samples.shape ds_src.sa['task'] = [source for s in range(ds_src.samples.shape[0])] ds_tar.sa['task'] = [target for s in range(ds_tar.samples.shape[0])] ds_merged = vstack((ds_src, ds_tar)) ds_merged.a.update(ds_src.a) print ds_merged.sa.task ds_merged_list.append(ds_merged) '''
def _group_transfer_learning(path, subjects, analysis, conf_file, source='task', analysis_type='single', **kwargs): if source == 'task': target = 'rest' else: if source == 'rest': target = 'task' if source == 'saccade': target = 'face' else: if source == 'face': target = 'saccade' ############################################## ############################################## ## conf_src['label_included'] = 'all' ## ## conf_src['label_dropped'] = 'none' ## ## conf_src['mean_samples'] = 'False' ## ############################################## ############################################## if analysis_type == 'group': if path.__class__ == conf_file.__class__ == list: ds_src, s, conf_src = sources_merged_ds(path, subjects, conf_file, source, **kwargs) conf_src['permutations'] = 0 else: print 'In group analysis path, subjects and conf_file must be lists: \ Check configuration file and/or parameters!!' return 0 else: conf_src = read_configuration(path, conf_file, source) for arg in conf_src: if arg == 'map_list': map_list = conf_src[arg].split(',') r_group = spatial(ds_src, **conf_src) total_results = dict() total_results['group'] = r_group clf = r_group['classifier'] for subj_, conf_, path_ in zip(subjects, conf_file, path): for subj in subj_: print '-----------' r = dict() if len(subj_) > 1: conf_tar = read_configuration(path_, conf_, target) for arg in kwargs: conf_tar[arg] = kwargs[arg] data_path = conf_tar['data_path'] try: ds_tar = load_dataset(data_path, subj, target, **conf_tar) except Exception, err: print err continue ds_tar = detrend_dataset(ds_tar, target, **conf_tar) if conf_src['label_included'] == 'all' and \ conf_src['label_dropped'] != 'fixation': print 'Balancing dataset...' ds_src = balance_dataset_timewise(ds_src, 'fixation') predictions = clf.predict(ds_tar) pred = np.array(predictions) targets = ds_tar.targets for arg in r_group.keys(): r[arg] = copy.copy(r_group[arg]) r['targets'] = targets r['predictions'] = predictions r['fclf'] = clf c_m = ConfusionMatrix(predictions=pred, targets=targets) c_m.compute() r['confusion_target'] = c_m print c_m tr_pred = similarity_measure_mahalanobis(ds_tar, ds_src, r) r['mahalanobis_similarity'] = tr_pred #print tr_pred c_mat_mahala = ConfusionMatrix(predictions=tr_pred.T[1], targets=tr_pred.T[0]) c_mat_mahala.compute() r['confusion_mahala'] = c_mat_mahala d_prime, beta, c, c_new = signal_detection_measures( pred, targets, map_list) r['d_prime'] = d_prime print d_prime r['beta'] = beta r['c'] = c r['confusion_total'] = c_new ''' d_prime_maha, c_new_maha = d_prime_statistics(tr_pred.T[1], tr_pred.T[0], map_list) r['d_prime_maha'] = d_prime_maha r['confusion_tot_maha'] = c_new_maha ''' total_results[subj] = r