def do_cross_validation():
summaries = []
for pipeline in pipelines:
for (classifier, classifier_name) in classifiers:
print 'Using pipeline %s with classifier %s' % (pipeline.get_name(), classifier_name)
scores = []
for target in targets:
print 'Processing %s (classifier %s)' % (target, classifier_name)
task_core = TaskCore(cached_data_loader=cached_data_loader, data_dir=data_dir,
target=target, pipeline=pipeline,
# target=target, pipeline=pipeline,
classifier_name=classifier_name, classifier=classifier,
normalize=should_normalize(classifier), gen_preictal=pipeline.gen_preictal,
cv_ratio=cv_ratio)
data = CrossValidationScoreTask(task_core).run()
score = data.score
scores.append(score)
print '%.3f' % score
if len(scores) > 0:
name = pipeline.get_name() + '_' + classifier_name
summary = get_score_summary(name, scores)
summaries.append((summary, np.mean(scores)))
print summary
print_results(summaries)
def do_cross_validation():
for pipeline in pipelines:
for (classifier, classifier_name) in classifiers:
print 'Using pipeline %s with classifier %s' % (
pipeline.get_name(), classifier_name)
scores = []
for target in targets:
print 'Processing %s (classifier %s)' % (target,
classifier_name)
task_core = TaskCore(
cached_data_loader=cached_data_loader,
data_dir=data_dir,
target=target,
pipeline=pipeline,
classifier_name=classifier_name,
classifier=classifier,
normalize=should_normalize(classifier),
gen_ictal=pipeline.gen_ictal,
cv_ratio=cv_ratio)
data = CrossValidationScoreTask(task_core).run()
score = data.score
scores.append(score)
print target, 'Seizure_AUC=', data.S_auc, 'Early_AUC=', data.E_auc
def do_cross_validation():
summaries = []
for pipeline in pipelines:
for (classifier, classifier_name) in classifiers:
print('Using pipeline %s with classifier %s' %
(pipeline.get_name(), classifier_name))
scores = []
S_scores = []
E_scores = []
for target in targets:
print('Processing %s (classifier %s)' %
(target, classifier_name))
task_core = TaskCore(
cached_data_loader=cached_data_loader,
data_dir=data_dir,
target=target,
pipeline=pipeline,
classifier_name=classifier_name,
classifier=classifier,
normalize=should_normalize(classifier),
gen_ictal=pipeline.gen_ictal,
cv_ratio=cv_ratio)
data = CrossValidationScoreTask(task_core).run()
score = data.score
scores.append(score)
print('%.3f' % score, 'S=%.4f' % data.S_auc,
'E=%.4f' % data.E_auc)
S_scores.append(data.S_auc)
E_scores.append(data.E_auc)
if len(scores) > 0:
name = pipeline.get_name() + '_' + classifier_name
summary = get_score_summary(name, scores)
summaries.append((summary, np.mean(scores)))
print(summary)
if len(S_scores) > 0:
name = pipeline.get_name() + '_' + classifier_name
summary = get_score_summary(name, S_scores)
print('S', summary)
if len(E_scores) > 0:
name = pipeline.get_name() + '_' + classifier_name
summary = get_score_summary(name, E_scores)
print('E', summary)
print_results(summaries)