def main():
parser = argparse.ArgumentParser(description='get pickeled instances')
subparsers = parser.add_subparsers(dest='subparser_name' ,help='sub-command help')
parser_serialize = subparsers.add_parser('serialize', help='pickle instances')
parser_serialize.add_argument('--data', required=True, help='path to output pickled files')
parser_serialize.add_argument('--threads', type=int, default = 1 , help='number of threads to run in parallel')
parser_add = subparsers.add_parser('add', help='add to pickled instances')
parser_add.add_argument('--data', required=True, help='path to output pickled files')
parser_add.add_argument('--threads', type=int, default = 1 , help='number of threads to run in parallel')
args = parser.parse_args()
logging.info("pid: " + str(os.getpid()))
if args.subparser_name == "serialize":
earmark_ids = list(get_earmarks_from_db())
logging.info("Got %d earmarks" % len(earmark_ids))
entity_ids = list(get_entities_from_db())
logging.info("Got %d entities" % len(entity_ids))
instances = get_matching_instances(entity_ids, earmark_ids, get_earmark_entity_tuples(), args.threads)
logging.info("Got %d instances" % len(instances))
logging.info("Creating pipe")
fgs = [
JaccardFeatureGenerator(),
]
pipe = Pipe(fgs, instances, num_processes=1)
logging.info("Pushing into pipe")
pipe.push_all_parallel()
# group by earmark and document:
pairs = [("JACCARD_FG","JACCARD_FG_max_inferred_name_jaccard" ), ("JACCARD_FG", "JACCARD_FG_max_cell_jaccard")]
fgs = [
RankingFeatureGenerator(pairs = pairs),
DifferenceFeatureGenerator(pairs = pairs)
]
grouper = InstancesGrouper(['earmark_id', 'document_id'])
pipe = BlocksPipe(grouper, fgs, pipe.instances, num_processes=1 )
pipe.push_all_parallel()
#Serialize
logging.info("Start Serializing")
serialize_instances(pipe.instances, args.data)
logging.info("Done!")
elif args.subparser_name == "add":
instances = load_instances(args.data)
logging.info("Creating pipe")
fgs = [
InfixFeatureGenerator()
]
pipe = Pipe(fgs, instances, num_processes=args.threads)
logging.info("Pushing into pipe")
pipe.push_all_parallel()
#Serialize
logging.info("Start Serializing")
serialize_instances(pipe.instances, args.data)
logging.info("Done!")
def folder_to_scipy(folder_path, feature_space = None):
instances = load_instances(folder_path)
x, y, feature_space = pipe.instances_to_matrix(instances, feature_space= feature_space, dense = True)
return x, y, feature_space
