def run_make_submission(settings, targets_and_pipelines, split_ratio):
pool = Pool(settings.N_jobs)
for i, (target, pipeline, feature_masks, classifier, classifier_name) in enumerate(targets_and_pipelines):
for j, feature_mask in enumerate(feature_masks):
progress_str = 'T=%d/%d M=%d/%d' % (i+1, len(targets_and_pipelines), j+1, len(feature_masks))
pool.apply_async(make_submission_predictions, [settings, target, pipeline, classifier, classifier_name],
{'feature_mask': feature_mask, 'progress_str': progress_str, 'quiet': True})
pool.close()
pool.join()
guesses = ['clip,preictal']
num_masks = None
classifier_names = []
for target, pipeline, feature_masks, classifier, classifier_name in targets_and_pipelines:
classifier_names.append(classifier_name)
if num_masks is None:
num_masks = len(feature_masks)
else:
assert num_masks == len(feature_masks)
test_predictions = []
for feature_mask in feature_masks:
data = make_submission_predictions(settings, target, pipeline, classifier, classifier_name, feature_mask=feature_mask)
test_predictions.append(data.mean_predictions)
predictions = np.mean(test_predictions, axis=0)
guesses += make_csv_for_target_predictions(target, predictions)
output = '\n'.join(guesses)
write_submission_file(settings, output, 'ensemble n=%d split_ratio=%s' % (num_masks, split_ratio), None, str(classifier_names), targets_and_pipelines)
def run_make_submission(settings, targets_and_pipelines, split_ratio):
pool = Pool(settings.N_jobs)
for i, (target, pipeline, feature_masks, classifier, classifier_name) in enumerate(targets_and_pipelines):
for j, feature_mask in enumerate(feature_masks):
progress_str = 'T=%d/%d M=%d/%d' % (i+1, len(targets_and_pipelines), j+1, len(feature_masks))
pool.apply_async(make_submission_predictions, [settings, target, pipeline, classifier, classifier_name],
{'feature_mask': feature_mask, 'progress_str': progress_str, 'quiet': True})
pool.close()
pool.join()
guesses = ['clip,preictal']
num_masks = None
classifier_names = []
for target, pipeline, feature_masks, classifier, classifier_name in targets_and_pipelines:
classifier_names.append(classifier_name)
if num_masks is None:
num_masks = len(feature_masks)
else:
assert num_masks == len(feature_masks)
test_predictions = []
for feature_mask in feature_masks:
data = make_submission_predictions(settings, target, pipeline, classifier, classifier_name, feature_mask=feature_mask)
test_predictions.append(data.mean_predictions)
predictions = np.mean(test_predictions, axis=0)
guesses += make_csv_for_target_predictions(target, predictions)
output = '\n'.join(guesses)
write_submission_file(settings, output, 'ensemble n=%d split_ratio=%s' % (num_masks, split_ratio), None, str(classifier_names), targets_and_pipelines)
def run_make_submission(settings, targets_and_pipelines, classifier,
classifier_name):
pool = Pool(settings.N_jobs)
for i, (target, pipeline,
feature_masks) in enumerate(targets_and_pipelines):
for j, feature_mask in enumerate(feature_masks):
progress_str = 'T=%d/%d M=%d/%d' % (
i + 1, len(targets_and_pipelines), j + 1, len(feature_masks))
pool.apply_async(
make_submission_predictions,
[settings, target, pipeline, classifier, classifier_name], {
'feature_mask': feature_mask,
'quiet': True,
'progress_str': progress_str
})
pool.close()
pool.join()
guesses = ['clip,preictal']
for target, pipeline, feature_masks in targets_and_pipelines:
test_predictions = []
for feature_mask in feature_masks:
data = make_submission_predictions(settings,
target,
pipeline,
classifier,
classifier_name,
feature_mask=feature_mask)
test_predictions.append(data.mean_predictions)
predictions = np.mean(test_predictions, axis=0)
guesses += make_csv_for_target_predictions(target, predictions)
output = '\n'.join(guesses)
submission_targets_and_pipelines = [
(target, pipeline, feature_masks, classifier, classifier_name)
for target, pipeline, feature_masks in targets_and_pipelines
]
write_submission_file(settings, output, None, None, classifier_name,
submission_targets_and_pipelines)
def run_make_submission(settings, targets_and_pipelines, classifier, classifier_name):
pool = Pool(settings.N_jobs)
for i, (target, pipeline, feature_masks) in enumerate(targets_and_pipelines):
for j, feature_mask in enumerate(feature_masks):
progress_str = 'T=%d/%d M=%d/%d' % (i+1, len(targets_and_pipelines), j+1, len(feature_masks))
pool.apply_async(make_submission_predictions, [settings, target, pipeline, classifier, classifier_name], {'feature_mask': feature_mask, 'quiet': True, 'progress_str': progress_str})
pool.close()
pool.join()
guesses = ['clip,preictal']
for target, pipeline, feature_masks in targets_and_pipelines:
test_predictions = []
for feature_mask in feature_masks:
data = make_submission_predictions(settings, target, pipeline, classifier, classifier_name, feature_mask=feature_mask)
test_predictions.append(data.mean_predictions)
predictions = np.mean(test_predictions, axis=0)
guesses += make_csv_for_target_predictions(target, predictions)
output = '\n'.join(guesses)
submission_targets_and_pipelines = [(target, pipeline, feature_masks, classifier, classifier_name)
for target, pipeline, feature_masks in targets_and_pipelines]
write_submission_file(settings, output, None, None, classifier_name, submission_targets_and_pipelines)