def run_prepare_data(settings, targets, pipelines, train=True, test=False, quiet=False):
for pipeline in pipelines:
for target in targets:
print 'Preparing data for', target
if train:
check_training_data_loaded(settings, target, pipeline, quiet=quiet)
if test:
check_test_data_loaded(settings, target, pipeline, quiet=quiet)
def run_prepare_data(settings, targets, pipelines, train=True, test=False, quiet=False):
for pipeline in pipelines:
for target in targets:
print('Preparing data for', target)
if train:
check_training_data_loaded(settings, target, pipeline, quiet=quiet)
if test:
check_test_data_loaded(settings, target, pipeline, quiet=quiet)
def run_prepare_data_for_cross_validation(settings,
targets,
pipelines,
quiet=False):
if not quiet: print '\n'.join([p.get_name() for p in pipelines])
for i, pipeline in enumerate(pipelines):
for j, target in enumerate(targets):
if not quiet:
print 'Running prepare data', 'P=%d/%d T=%d/%d' % (
i + 1, len(pipelines), j + 1, len(targets))
check_training_data_loaded(settings, target, pipeline)
def run_prepare_data(settings, targets_and_pipelines, train=True, test=False):
for target, pipeline, feature_masks in targets_and_pipelines:
if train:
check_training_data_loaded(settings, target, pipeline)
if test:
check_test_data_loaded(settings, target, pipeline)
def run_prepare_data(settings, targets_and_pipelines, train=True, test=False):
for target, pipeline, feature_masks in targets_and_pipelines:
if train:
check_training_data_loaded(settings, target, pipeline)
if test:
check_test_data_loaded(settings, target, pipeline)
def run_prepare_data_for_submission(settings, targets, pipelines):
for pipeline in pipelines:
for target in targets:
print 'Running %s pipeline %s' % (target, pipeline.get_name())
check_training_data_loaded(settings, target, pipeline)
check_test_data_loaded(settings, target, pipeline)
def run_prepare_data_for_submission(settings, targets, pipelines):
for pipeline in pipelines:
for target in targets:
print 'Running %s pipeline %s' % (target, pipeline.get_name())
check_training_data_loaded(settings, target, pipeline)
check_test_data_loaded(settings, target, pipeline)
def run_prepare_data_for_cross_validation(settings, targets, pipelines, quiet=False):
if not quiet: print '\n'.join([p.get_name() for p in pipelines])
for i, pipeline in enumerate(pipelines):
for j, target in enumerate(targets):
if not quiet: print 'Running prepare data', 'P=%d/%d T=%d/%d' % (i+1, len(pipelines), j+1, len(targets))
check_training_data_loaded(settings, target, pipeline)
