def main(args):
in_dir = join(dirname(__file__), '../../data/labeled/')
out_dir = join(dirname(__file__),
'../../output/predictions/{}'.format(args.community))
if not run_exists(out_dir, args.run_id):
logger.info('Run %s does not exists. Start new...', args.run_id)
pred = make_predictions(args)
save_predictions(pred, out_dir, args.run_id)
logger.info('Finish training and testing.')
results_test = evaluation.evaluate(
csv_true=join(in_dir, '{}_test.csv'.format(args.community)),
csv_pred=join(out_dir, '{}_test.csv'.format(args.run_id)),
label_encoder=preprocessing.LABEL_ENCODER)
evaluation.print_results(results_test, '{}_test'.format(args.run_id))
def main(args):
in_dir = join(dirname(__file__), '../../data/labeled/')
out_dir = join(dirname(__file__), '../../output/predictions/{}'.format(args.community))
if not run_exists(out_dir, args.run_id):
pred = make_predictions(
join(in_dir, '{}_train.csv'.format(args.community)),
join(in_dir, '{}_test.csv'.format(args.community)),
args
)
save_predictions(pred, out_dir, args.run_id)
le = LabelEncoder().fit(['CLEAR', 'UNCLEAR'])
results_test = evaluation.evaluate(
csv_true=join(in_dir, '{}_test.csv'.format(args.community)),
csv_pred=join(out_dir, '{}_test.csv'.format(args.run_id)),
label_encoder=le
)
evaluation.print_results(results_test, '{}_test'.format(args.run_id))