subset='validation', verbose=True, check_status=True):

anet_classification = ANETclassification(ground_truth_filename,

prediction_filename,

subset=subset, verbose=verbose,

check_status=True, top_k=1)

anet_classification.evaluate()

anet_classification = ANETclassification(ground_truth_filename,

prediction_filename,

subset=subset, verbose=verbose,

check_status=True, top_k=5)

description = ('This script allows you to evaluate the ActivityNet '

'untrimmed video classification task which is intended to '

'evaluate the ability of algorithms to predict activities '

'in untrimmed video sequences.')

p = argparse.ArgumentParser(description=description)

p.add_argument('ground_truth_filename',

help='Full path to json file containing the ground truth.')

p.add_argument('prediction_filename',

help='Full path to json file containing the predictions.')

p.add_argument('--subset', default='validation',

help=('String indicating subset to evaluate: '

'(training, validation)'))

p.add_argument('--verbose', type=bool, default=True)

p.add_argument('--check_status', type=bool, default=True)