def transform_semQL_to_sql(schemas, sem_ql_prediction, output_dir):
# TODO: find out if this adds any benefit for the trained models. If we run it with the ground truth (so no prediction, just SQL -> SemQL -> SQL) it is even slightly better without it.
# alter_not_in(sem_ql_prediction, schemas=schemas)
# alter_inter(sem_ql_prediction)
alter_column0(sem_ql_prediction)
index = range(len(sem_ql_prediction))
count = 0
exception_count = 0
with open(os.path.join(output_dir, 'output.txt'), 'w',
encoding='utf8') as d, open(os.path.join(output_dir,
'ground_truth.txt'),
'w',
encoding='utf8') as g:
for i in index:
try:
result = transform(sem_ql_prediction[i],
schemas[sem_ql_prediction[i]['db_id']])
d.write(result[0] + '\n')
g.write("%s\t%s\t%s\n" % (sem_ql_prediction[i]['query'],
sem_ql_prediction[i]["db_id"],
sem_ql_prediction[i]["question"]))
count += 1
except Exception as e:
# This origin seems to be the fallback-query. Not sure how we come up with it, most probably it's just a dummy query to fill in a result for each example.
result = transform(
sem_ql_prediction[i],
schemas[sem_ql_prediction[i]['db_id']],
origin='Root1(3) Root(5) Sel(0) N(0) A(3) C(0) T(0)')
exception_count += 1
d.write(result[0] + '\n')
g.write("%s\t%s\t%s\n" % (sem_ql_prediction[i]['query'],
sem_ql_prediction[i]["db_id"],
sem_ql_prediction[i]["question"]))
count += 1
# print(e)
print('Exception')
print(traceback.format_exc())
print(sem_ql_prediction[i]['question'])
print(sem_ql_prediction[i]['query'])
print(sem_ql_prediction[i]['db_id'])
print('===\n\n')
return count, exception_count
def _semql_to_sql(prediction, schemas):
alter_column0([prediction])
result = transform(prediction, schemas[prediction['db_id']])
return result[0]