def convert_trees(results_dict, feature, align, paths, failtok, best, backoff):
if not align:
results = sorted(results_dict.items(), key=lambda x:x[0])
lines = []
for iid, item in results:
if feature == "short-derivation":
lines.extend(item)
continue
for reading in item[:best]:
features = reading_features(reading, feature)
if features is not None:
lines.append(features)
else:
if backoff is not None:
backoffs = extract_backoff_items(backoff)
lines = []
for iid in delphin.get_profile_ids(*paths):
try:
features = reading_features(results_dict[iid][0], feature)
lines.append(features)
except IndexError:
if backoff is not None:
lines.append(tagged[iid])
else:
lines.append(failtok)
if feature in MULTILINE_FEATURES:
sep = '\n\n'
else:
sep = '\n'
return sep.join(lines)
def main():
arg = argparser().parse_args()
query = "select i-id where p-id = {}".format(arg.pid)
tsdb_results = delphin.tsdb_query(query, arg.profile)
gold_iids = set(int(x) for x in tsdb_results.split())
all_iids = delphin.get_profile_ids(arg.profile)
predicted_iids = get_predicted_iids(arg)
print evaluate(predicted_iids, gold_iids, len(all_iids))