def eval_one_qelos(db_file, pred_file, source_file):
engine = DBEngine(db_file)
exact_match = []
with open(source_file) as fs, open(pred_file) as fp:
grades = []
for ls, lp in tqdm(zip(fs, fp), total=count_lines(source_file)):
eg = json.loads(ls)
ep = json.loads(lp)
qg = Query.from_dict(eg['sql'])
gold = engine.execute_query(eg['table_id'], qg, lower=True)
qp = None
try:
qp = Query.from_dict(ep)
pred = engine.execute_query(eg['table_id'], qp, lower=True)
except Exception as e:
pred = repr(e)
correct = pred == gold
match = qp == qg
grades.append(correct)
exact_match.append(match)
result = {
'ex_accuracy': sum(grades) / len(grades),
'lf_accuracy': sum(exact_match) / len(exact_match),
}
return result
def hello():
pred_json = json.loads(request.args.get('pred'))
gold_json = json.loads(request.args.get('gold'))
dataset = request.args.get('dataset')
engine = DBEngine(os.path.join(DATABASE_PATH, "{}.db".format(dataset)))
exact_match = []
grades = []
for lp, ls in tqdm(zip(pred_json, gold_json), total=len(gold_json)):
eg = ls
ep = lp
qg = Query.from_dict(eg['sql'])
gold = engine.execute_query(eg['table_id'], qg, lower=True)
pred = ep['error']
qp = None
if not ep['error']:
try:
qp = Query.from_dict(ep['query'])
pred = engine.execute_query(eg['table_id'], qp, lower=True)
except Exception as e:
pred = repr(e)
correct = pred == gold
match = qp == qg
grades.append(correct)
exact_match.append(match)
ex_accuracy = sum(grades) / len(grades)
lf_accuracy = sum(exact_match) / len(exact_match)
return json.dumps({"ex_accuracy": ex_accuracy, "lf_accuracy": lf_accuracy})
def eval(self, gold, sql_gold, engine):
self.exception_raised = False # reset this flag for a new evaluation
if self.agg == gold['query']['agg']:
self.correct['agg'] = 1
if self.sel == gold['query']['sel']:
self.correct['sel'] = 1
op_list_pred = [op for col, op, span in self.cond]
op_list_gold = [op for col, op, span in gold['query']['conds']]
col_list_pred = [col for col, op, span in self.cond]
col_list_gold = [col for col, op, span in gold['query']['conds']]
q = gold['question']['words']
span_list_pred = [' '.join(q[span[0]:span[1] + 1])
for col, op, span in self.cond]
span_list_gold = [' '.join(span['words'])
for col, op, span in gold['query']['conds']]
where_pred = list(zip(col_list_pred, op_list_pred, span_list_pred))
where_gold = list(zip(col_list_gold, op_list_gold, span_list_gold))
where_pred.sort()
where_gold.sort()
if where_pred == where_gold and (len(col_list_pred) == len(col_list_gold)) and (len(op_list_pred) == len(op_list_gold)) and (len(span_list_pred) == len(span_list_gold)):
self.correct['where'] = 1
if (len(col_list_pred) == len(col_list_gold)) and ([it[0] for it in where_pred] == [it[0] for it in where_gold]):
self.correct['col'] = 1
if (len(op_list_pred) == len(op_list_gold)) and ([it[1] for it in where_pred] == [it[1] for it in where_gold]):
self.correct['lay'] = 1
if (len(span_list_pred) == len(span_list_gold)) and ([it[2] for it in where_pred] == [it[2] for it in where_gold]):
self.correct['span'] = 1
if all((self.correct[it] == 1 for it in ('agg', 'sel', 'where'))):
self.correct['all'] = 1
# execution
table_id = gold['table_id']
ans_gold = engine.execute_query(
table_id, Query.from_dict(sql_gold), lower=True)
try:
sql_pred = {'agg':self.agg, 'sel':self.sel, 'conds': self.recover_cond_to_gloss(gold)}
ans_pred = engine.execute_query(
table_id, Query.from_dict(sql_pred), lower=True)
except Exception as e:
#pdb.set_trace()
self.exception_raised = True
ans_pred = repr(e)
if set(ans_gold) == set(ans_pred):
self.correct['exe'] = 1
def get_query_from_json(self, json_line):
"""Returns a Query object for the json input and returns the table object as well"""
q = Query.from_dict(json_line["sql"])
t_id = json_line["table_id"]
table = self.table_map[t_id]
t = Table("", table["header"], table["types"], table["rows"])
return t, q
def main(argv):
del argv # Unused.
db_file = join(FLAGS.data_root, FLAGS.db_file)
parsed_std_sql_file = join(FLAGS.data_root, FLAGS.parsed_std_sql_file)
parsed_pred_sql_file = join(FLAGS.data_root, FLAGS.parsed_pred_sql_file)
engine = DBEngine(db_file)
exact_match = []
with open(parsed_std_sql_file) as fs, open(parsed_pred_sql_file) as fp:
grades = []
for ls, lp in tqdm(zip(fs, fp),
total=count_lines(parsed_std_sql_file)):
eg = json.loads(ls)
ep = json.loads(lp)
try:
qg = Query.from_dict(eg['sql'])
gold = engine.execute_query(eg['table_id'], qg, lower=True)
except Exception as e:
gold = repr(e)
#pred = ep['error']
qp = None
#if not ep['error']:
if True:
try:
qp = Query.from_dict(ep['sql'])
pred = engine.execute_query(eg['table_id'], qp, lower=True)
except Exception as e:
pred = repr(e)
correct = pred == gold
match = qp == qg
if pred == gold and qp != qg:
print(qp)
print(qg)
grades.append(correct)
exact_match.append(match)
print(
json.dumps(
{
'ex_accuracy': sum(grades) / len(grades),
'lf_accuracy': sum(exact_match) / len(exact_match),
},
indent=2))
def main(anno_file_name, col_headers, raw_args=None, verbose=True):
parser = argparse.ArgumentParser(description='evaluate.py')
opts.translate_opts(parser)
opt = parser.parse_args(raw_args)
torch.cuda.set_device(opt.gpu)
opt.db_file = os.path.join(opt.data_path, '{}.db'.format(opt.split))
opt.pre_word_vecs = os.path.join(opt.data_path, 'embedding')
dummy_parser = argparse.ArgumentParser(description='train.py')
opts.model_opts(dummy_parser)
opts.train_opts(dummy_parser)
dummy_opt = dummy_parser.parse_known_args([])[0]
opt.anno = anno_file_name
engine = DBEngine(opt.db_file)
js_list = table.IO.read_anno_json(opt.anno)
prev_best = (None, None)
sql_query = []
for fn_model in glob.glob(opt.model_path):
opt.model = fn_model
translator = Translator(opt, dummy_opt.__dict__)
data = table.IO.TableDataset(js_list, translator.fields, None, False)
test_data = table.IO.OrderedIterator(dataset=data,
device=opt.gpu,
batch_size=opt.batch_size,
train=False,
sort=True,
sort_within_batch=False)
# inference
r_list = []
for batch in test_data:
r_list += translator.translate(batch)
r_list.sort(key=lambda x: x.idx)
pred = r_list[-1]
sql_pred = {
'agg': pred.agg,
'sel': pred.sel,
'conds': pred.recover_cond_to_gloss(js_list[-1])
}
if verbose:
print('\n sql_pred: ', sql_pred, '\n')
print('\n col_headers: ', col_headers, '\n')
sql_query = Query(sql_pred['sel'], sql_pred['agg'], sql_pred['conds'])
try:
ans_pred = engine.execute_query(js_list[-1]['table_id'],
Query.from_dict(sql_pred),
lower=True,
verbose=verbose)
except Exception as e:
ans_pred = None
return sql_query.get_complete_query(col_headers), ans_pred
sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
from lib.query import Query
from lib.dbengine import DBEngine
import pytorch
if __name__ == '__main__':
for split in ['train', 'dev', 'test']:
print('checking {}'.format(split))
engine = DBEngine('data/{}.db'.format(split))
n_lines = 0
with open('data/{}.jsonl'.format(split)) as f:
for l in f:
n_lines += 1
with open('data/{}.jsonl'.format(split)) as f:
for l in tqdm(f, total=n_lines):
d = json.loads(l)
query = Query.from_dict(d['sql'])
# make sure it's executable
result = engine.execute_query(d['table_id'], query)
if result:
for a, b, c in d['sql']['conds']:
if str(c).lower() not in d['question'].lower():
raise Exception(
'Could not find condition {} in question {} for query {}'
.format(c, d['question'], query))
else:
raise Exception(
'Query {} did not execute to a valid result'.format(
query))
parser.add_argument('--topk', type=int, default=3, help='k of top_k')
args = parser.parse_args()
engine = DBEngine(args.db_file)
temp = []
with open(args.source_file) as fs, open(args.pred_file) as fp:
grades = []
exact_match = []
for ls, lp in tqdm(zip(fs, fp), total=count_lines(args.source_file)):
eg = json.loads(ls)
qg = Query.from_dict(eg['sql'], ordered=args.ordered)
gold = engine.execute_query(eg['table_id'], qg, lower=True)
pred_topk = []
qp_topk = []
ep = json.loads(lp)
pred = ep.get('error', None)
qp = None
for i in range(args.topk):
if not ep.get('error', None):
try:
if ep['query'][str(i)]['conds'] == [[]]:
ep['query'][str(i)]['conds'] = []
def eval(self, split, gold, sql_gold, engine=None):
if self.agg == gold['query']['agg']:
self.correct['agg'] = 1
if self.sel == gold['query']['sel']:
self.correct['sel'] = 1
#gold['BIO_label'].data, gold['BIO_column_label'].data
op_list_pred = [op for col, op, span in self.cond]
op_list_gold = [op for col, op, span in gold['query']['conds']]
col_list_pred = [col for col, op, span in self.cond]
col_list_gold = [col for col, op, span in gold['query']['conds']]
q = gold['question']['words']
span_list_pred = [
' '.join(q[span[0]:span[1] + 1]) for col, op, span in self.cond
]
span_list_gold = [
' '.join(span['words']) for col, op, span in gold['query']['conds']
]
where_pred = list(zip(col_list_pred, op_list_pred, span_list_pred))
where_gold = list(zip(col_list_gold, op_list_gold, span_list_gold))
where_pred.sort()
where_gold.sort()
if where_pred == where_gold and (
len(col_list_pred) == len(col_list_gold)) and (
len(op_list_pred)
== len(op_list_gold)) and (len(span_list_pred)
== len(span_list_gold)):
self.correct['where'] = 1
if (len(col_list_pred) == len(col_list_gold)) and ([
it[0] for it in where_pred
] == [it[0] for it in where_gold]):
self.correct['col'] = 1
if (len(op_list_pred) == len(op_list_gold)) and ([
it[1] for it in where_pred
] == [it[1] for it in where_gold]):
self.correct['lay'] = 1
if (len(span_list_pred) == len(span_list_gold)) and ([
it[2] for it in where_pred
] == [it[2] for it in where_gold]):
self.correct['span'] = 1
if all((self.correct[it] == 1 for it in ('agg', 'sel', 'where'))):
self.correct['all'] = 1
# execution
table_id = gold['table_id']
ans_gold = '0'
ans_pred = '1'
if engine is not None:
ans_gold = engine.execute_query(table_id,
Query.from_dict(sql_gold),
lower=True)
try:
sql_pred = {
'agg': self.agg,
'sel': self.sel,
'conds': self.recover_cond_to_gloss(gold)
}
ans_pred = engine.execute_query(table_id,
Query.from_dict(sql_pred),
lower=True)
except Exception as e:
ans_pred = repr(e)
else:
ans_gold = '0'
ans_pred = '1'
if set(ans_gold) == set(ans_pred):
self.correct['exe'] = 1
error_case = {}
if split == 'finaltest':
#if self.correct['where'] != 1:
if True:
error_case['sel'] = self.correct['sel']
error_case['where'] = self.correct['where']
error_case['all'] = self.correct['all']
error_case['table_id'] = gold['table_id']
error_case['question_id'] = gold['id']
error_case['question'] = gold['question']['words']
error_case['table_head'] = [
head['words'] for head in gold['table']['header']
]
#error_case['table_content'] = gold['tbl_content']
# for i in range(len(sql_gold['conds'])):
# sql_gold['conds'][i][0] = (
# sql_gold['conds'][i][0], gold['table']['header'][sql_gold['conds'][i][0]]['words'],
# gold['tbl_content'][sql_gold['conds'][i][0]])
error_case['gold'] = sql_gold
error_case['predict'] = {
'agg': self.agg.item(),
'sel': self.sel.item(),
'conds': self.print_recover_cond_to_gloss(gold)
}
# error_case['exe result']=self.correct['exe']
error_case['BIO'] = [(x.item(), y) for x, y in zip(
list(self.BIO), list(gold['question']['words']))]
error_case['BIO_col'] = self.BIO_col.tolist()
return error_case
opts = Options()
fill_from_args(opts)
for split in ['train', 'dev', 'test']:
orig = os.path.join(opts.data_dir, f'{split}.jsonl')
db_file = os.path.join(opts.data_dir, f'{split}.db')
ans_file = os.path.join(opts.data_dir, f"{split}_ans.jsonl.gz")
tbl_file = os.path.join(opts.data_dir, f"{split}.tables.jsonl")
engine = DBEngine(db_file)
exact_match = []
with open(orig) as fs, write_open(ans_file) as fo:
grades = []
for ls in tqdm(fs, total=count_lines(orig)):
eg = json.loads(ls)
sql = eg['sql']
qg = Query.from_dict(sql, ordered=False)
gold = engine.execute_query(eg['table_id'], qg, lower=True)
assert isinstance(gold, list)
#if len(gold) != 1:
# print(f'for {sql} : {gold}')
eg['answer'] = gold
eg['rowids'] = engine.execute_query_rowid(eg['table_id'],
qg,
lower=True)
# CONSIDER: if it is not an agg query, somehow identify the particular cell
fo.write(json.dumps(eg) + '\n')
convert(jsonl_lines(ans_file),
jsonl_lines(tbl_file),
os.path.join(opts.data_dir, f"{split}_agg.jsonl.gz"),
skip_aggregation=False)
if __name__ == '__main__':
parser = ArgumentParser()
parser.add_argument('source_file', help='source file for the prediction')
parser.add_argument('db_file', help='source database for the prediction')
parser.add_argument('pred_file', help='predictions by the model')
args = parser.parse_args()
engine = DBEngine(args.db_file)
exact_match = []
with open(args.source_file) as fs, open(args.pred_file) as fp:
grades = []
for ls, lp in tqdm(zip(fs, fp), total=count_lines(args.pred_file)):
eg = json.loads(ls)
ep = json.loads(lp)
qg = Query.from_dict(eg['sql'])
#print qg
gold = engine.execute_query(eg['table_id'], qg, lower=True)
pred = ep['error']
qp = None
if not ep['error']:
try:
qp = Query.from_dict(ep['query'])
pred = engine.execute_query(eg['table_id'], qp, lower=True)
except Exception as e:
pred = repr(e)
correct = pred == gold
match = qp == qg
#if not match:
# print qp.to_dict()
# print qg.to_dict()
def main():
parser = ArgumentParser()
parser.add_argument('--din', default=data_path, help='data directory')
parser.add_argument('--dout', default='annotated', help='output directory')
args = parser.parse_args()
if not os.path.isdir(args.dout):
os.makedirs(args.dout)
for split in ['dev']:
#for split in ['train', 'test', 'dev']:
with open(save_path+'%s.qu'%split, 'w') as qu_file, open(save_path+'%s.lon'%split, 'w') as lon_file, \
open(save_path+'%s.out'%split, 'w') as out, open(save_path+'%s_sym_pairs.txt'%split, 'w') as sym_file, \
open(save_path+'%s_ground_truth.txt'%split, 'w') as S_file:
fsplit = os.path.join(args.din, split) + '.jsonl'
ftable = os.path.join(args.din, split) + '.tables.jsonl'
with open(fsplit) as fs, open(ftable) as ft:
print('loading tables')
tables = {}
for line in tqdm(ft, total=count_lines(ftable)):
d = json.loads(line)
tables[d['id']] = d
print('loading tables done.')
print('loading examples')
n, acc, acc_pair, acc_all, error = 0, 0, 0, 0, 0
target = -1
ADD_FIELDS = False
step2 = True
for line in tqdm(fs, total=count_lines(fsplit)):
ADD_TO_FILE = True
d = json.loads(line)
Q = d['question']
rows = tables[d['table_id']]['rows']
rows = np.asarray(rows)
fs = tables[d['table_id']]['header']
all_fields = []
for f in fs:
all_fields.append(_preclean(f))
# all fields are sorted by length in descending order
# for string match purpose
all_fields = sorted(all_fields, key=len, reverse=True)
smap = defaultdict(list) #f2v
reverse_map = defaultdict(list) #v2f
for row in rows:
for i in range(len(fs)):
cur_f = _preclean(str(fs[i]))
cur_row = _preclean(str(row[i]))
smap[cur_f].append(cur_row)
if cur_f not in reverse_map[cur_row]:
reverse_map[cur_row].append(cur_f)
#----------------------------------------------------------
# all values are sorted by length in descending order
# for string match purpose
keys = sorted(reverse_map.keys(), key=len, reverse=True)
Q = _preclean(Q)
Q_ori = Q
#####################################
########## Annotate question ########
#####################################
candidates, cond_fields = _match_pairs(
Q, Q_ori, keys, reverse_map)
Q_head, head2partial = _match_head(Q, Q_ori, smap,
all_fields, cond_fields)
Q, Qpairs = annotate_Q(Q, Q_ori, Q_head, candidates,
all_fields, head2partial, n, target)
qu_file.write(Q + '\n')
validation_pairs = copy.copy(Qpairs)
validation_pairs.append((Q_head, '<f0>', 'head'))
for i, f in enumerate(all_fields):
validation_pairs.append((f, '<c' + str(i) + '>', 'c'))
#####################################
########## Annotate SQL #############
#####################################
q_sent = Query.from_dict(d['sql'])
S, col_names, val_names = q_sent.to_sentence(
tables[d['table_id']]['header'], rows,
tables[d['table_id']]['types'])
S = _preclean(S)
S_ori = S
S_noparen = q_sent.to_sentence_noparenthesis(
tables[d['table_id']]['header'], rows,
tables[d['table_id']]['types'])
S_noparen = _preclean(S_noparen)
col_names = [_preclean(col_name) for col_name in col_names]
val_names = [_preclean(val_name) for val_name in val_names]
HEAD = col_names[-1]
S_head = _preclean(HEAD)
#annotate for SQL
name_pairs = []
for col_name, val_name in zip(col_names, val_names):
if col_name == val_name:
name_pairs.append([_preclean(col_name), 'true'])
else:
name_pairs.append(
[_preclean(col_name),
_preclean(val_name)])
# sort to compare with candidates
name_pairs.sort(key=lambda x: x[1])
new_name_pairs = [[
'<f' + str(i + 1) + '>', '<v' + str(i + 1) + '>'
] for i, (field, value) in enumerate(name_pairs)]
# only annotate S while identified (f,v) pairs are right
if _equal(name_pairs, candidates):
pairs = []
for (f, v), (new_f,
new_v) in zip(name_pairs, new_name_pairs):
pairs.append((f, new_f, 'f'))
pairs.append((v, new_v, 'v'))
# sort (word,symbol) pairs by length in descending order
pairs.sort(key=lambda x: 100 - len(x[0]))
for p, new_p, t in pairs:
cp = _backslash(p)
if new_p in Q:
if t == 'v':
S = S.replace(p + ' )', new_p + ' )')
if t == 'f':
S = re.sub(
'\( ' + cp + ' (equal|less|greater)',
'( ' + new_p + r' \1', S)
# only annotate S while identified HEAD is right
if S_head == Q_head and '<f0>' in Q:
S = S.replace(S_head, '<f0>')
# annote unseen fields
if ADD_FIELDS:
for i, f in enumerate(all_fields):
cf = _backslash(f)
S = re.sub('(\s|^)' + cf + '(\s|$|s)',
' <c' + str(i) + '> ', S)
S = _clean(S)
lon_file.write(S + '\n')
###############################
######### VALIDATION ##########
###############################
recover_S = S
for word, sym, t in validation_pairs:
recover_S = recover_S.replace(sym, word)
sym_file.write(sym + '=>' + word + '<>')
sym_file.write('\n')
S_file.write(S_noparen + '\n')
#------------------------------------------------------------------------
if _equal(name_pairs, candidates):
acc_pair += 1
if Q_head == S_head:
acc += 1
if _equal(name_pairs, candidates) and Q_head == S_head:
acc_all += 1
full_anno = True
for s in S.split():
if s[0] != '<' and s not in [
'(', ')', 'where', 'less', 'greater', 'equal',
'max', 'min', 'count', 'sum', 'avg', 'and',
'true'
]:
error += 1
full_anno = False
break
if False and not (_equal(name_pairs, candidates)
and head == col_names[-1]):
print('--------' + str(n) + '-----------')
print(Q_ori)
print(Q)
print(S_ori)
print(S)
print('head:')
print(head)
print(head2partial)
print('true HEAD')
print(Q_head)
print('fields:')
print(candidates)
print(p2partial)
print('true fields')
print(name_pairs)
n += 1
print('total number of examples:' + str(n))
print('fully snnotated:' + str(1 - error * 1.0 / n))
print('accurate all percent:' + str(acc_all * 1.0 / n))
print('accurate HEAD match percent:' + str(acc * 1.0 / n))
print('accurate fields pair match percent:' +
str(acc_pair * 1.0 / n))
def _gen_files(save_path=save_path):
"""
Prepare files used for Machine Comprehension Binary Classifier
"""
for split in ['train', 'test', 'dev']:
print('------%s-------' % split)
n = 0
fsplit = os.path.join(data_path, split) + '.jsonl'
ftable = os.path.join(data_path, split) + '.tables.jsonl'
"""
test.txt original column content w/o truncate
test_model.txt column name truncate or pad to length 3
"""
with open(fsplit) as fs, open(ftable) as ft, \
open(os.path.join(save_path, split+'.txt'), mode='w') as fw, \
open(os.path.join(save_path, '%s_model.txt'%split), mode='w') as fw_model, \
open(os.path.join(save_path, split+'.lon'), mode='w') as fsql, \
open(os.path.join(save_path, '%s.ori.qu'%split), 'w') as qu_file, \
open(os.path.join(save_path, '%s.ori.lon'%split), 'w') as lon_file:
print('loading tables...')
tables = {}
for line in tqdm(ft, total=count_lines(ftable)):
d = json.loads(line)
tables[d['id']] = d
print('loading tables done.')
print('loading examples')
f2v_all, v2f_all = [], []
for line in tqdm(fs, total=count_lines(fsplit)):
d = json.loads(line)
Q = d['question']
Q = _preclean(Q).replace('\t', '')
qu_file.write(Q + '\n')
q_sent = Query.from_dict(d['sql'])
rows = tables[d['table_id']]['rows']
S, col_names, val_names = q_sent.to_sentence(
tables[d['table_id']]['header'], rows,
tables[d['table_id']]['types'])
S = _preclean(S)
lon_file.write(S + '\n')
rows = np.asarray(rows)
fs = tables[d['table_id']]['header']
all_fields = [_preclean(f) for f in fs]
# all fields are sorted by length in descending order
# for string match purpose
headers = sorted(all_fields, key=len, reverse=True)
f2v = defaultdict(list) #f2v
v2f = defaultdict(list) #v2f
for row in rows:
for i in range(len(fs)):
cur_f = _preclean(str(fs[i]))
cur_row = _preclean(str(row[i]))
#cur_f = cur_f.replace('\u2003',' ')
f2v[cur_f].append(cur_row)
if cur_f not in v2f[cur_row]:
v2f[cur_row].append(cur_f)
f2v_all.append(f2v)
v2f_all.append(v2f)
#####################################
########## Annotate SQL #############
#####################################
q_sent = Query.from_dict(d['sql'])
S, col_names, val_names = q_sent.to_sentence(
tables[d['table_id']]['header'], rows,
tables[d['table_id']]['types'])
S = _preclean(S)
S_noparen = q_sent.to_sentence_noparenthesis(
tables[d['table_id']]['header'], rows,
tables[d['table_id']]['types'])
S_noparen = _preclean(S_noparen)
col_names = [_preclean(col_name) for col_name in col_names]
val_names = [_preclean(val_name) for val_name in val_names]
HEAD = col_names[-1]
S_head = _preclean(HEAD)
#annotate for SQL
name_pairs = []
for col_name, val_name in zip(col_names, val_names):
if col_name == val_name:
name_pairs.append([_preclean(col_name), 'true'])
else:
name_pairs.append(
[_preclean(col_name),
_preclean(val_name)])
# sort to compare with candidates
name_pairs.sort(key=lambda x: x[1])
fsql.write('#%d\n' % n)
fw.write('#%d\n' % n)
for f in col_names:
fsql.write(S.replace(f, '[' + f + ']') + '\n')
f = _truncate(f, END='<bos>', PAD='<pad>', max_len=-1)
s = (Q + '\t' + f + '\t 1')
assert len(s.split('\t')) == 3
fw.write(s + '\n')
for f in [f for f in headers if f not in col_names]:
f = _truncate(f, END='<bos>', PAD='<pad>', max_len=-1)
s = (Q + '\t' + f + '\t 0')
assert len(s.split('\t')) == 3
fw.write(s + '\n')
fsql.write(S + '\n')
#if '\u2003' in Q:
# print('u2003: '+Q)
#if '\xa0' in Q:
# print(n)
# print('xa0: '+Q)
# print(S)
for f in col_names:
f = f.replace(u'\xa0', u' ').replace('\t', '')
Q = Q.replace(u'\xa0', u' ').replace('\t', '')
f = _truncate(f, END='bos', PAD='pad', max_len=3)
s = (Q + '\t' + f + '\t 1')
assert len(s.split('\t')) == 3
fw_model.write(s + '\n')
for f in [f for f in headers if f not in col_names]:
f = f.replace(u'\xa0', u' ').replace('\t', '')
Q = Q.replace(u'\xa0', u' ').replace('\t', '')
f = _truncate(f, END='bos', PAD='pad', max_len=3)
s = (Q + '\t' + f + '\t 0')
assert len(s.split('\t')) == 3
fw_model.write(s + '\n')
n += 1
fsql.write('#%d\n' % n)
fw.write('#%d\n' % n)
scipy.savez(os.path.join(save_path, '%s_dict.npz' % split),
f2v_all=f2v_all,
v2f_all=v2f_all)
print('num of records:%d' % n)
parser.add_argument("--db_file")
parser.add_argument("--pred_file")
parser.add_argument("--ordered", action='store_true')
args = parser.parse_args()
engine = DBEngine(args.db_file)
ex_acc_list = []
lf_acc_list = []
with open(args.source_file) as sf, open(args.pred_file) as pf:
for source_line, pred_line in tqdm(zip(sf, pf), total=count_lines(args.source_file)):
# line별 정답과 예측 샘플 가져오기
gold_example = json.loads(source_line)
pred_example = json.loads(pred_line)
# 정답 샘플 lf, ex 구하기
lf_gold_query = Query.from_dict(gold_example['sql'], ordered=args.ordered)
ex_gold = engine.execute_query(gold_example['table_id'], lf_gold_query, lower=True)
# error가 아닌 경우 예측 샘플 lf, ex 구하기
lf_pred_query = None
ex_pred = pred_example.get('error', None)
if not ex_pred:
try:
lf_pred_query = Query.from_dict(pred_example['query'], ordered=args.ordered)
ex_pred = engine.execute_query(gold_example['table_id'], lf_pred_query, lower=True)
except Exception as e:
ex_pred = repr(e)
# lf, ex의 gold, pred 매칭결과 구하기
ex_acc_list.append(ex_pred == ex_gold)
lf_acc_list.append(lf_pred_query == lf_gold_query) # query의 __eq__를 호출