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import argparse
from typing import List, Dict, Any, Tuple
import pickle as pkl
import tqdm
from .exec_eval import exec_on_db, result_eq
import os
from collections import defaultdict
import time
from multiprocessing import cpu_count, Pool, Manager
from itertools import repeat
NUM_PROCESSES = cpu_count() // 3
if NUM_PROCESSES == 0:
NUM_PROCESSES = 1
MULTIPLICATIVE_OVERHEAD = 3
ADDITIVE_OVERHEAD = 30
GOLD_TIMEOUT = 100
cache_path = "cache.pkl"
m = Manager()
cache = m.dict()
def load_predictions(f_path: str) -> List[str]:
preds = []
with open(f_path, "r") as in_file:
for l in in_file:
preds.append(l.strip())
return preds
def acc(l, idxes=None):
if idxes is None:
idxes = [_ for _ in range(len(l))]
c = 0
for idx in idxes:
if l[idx]:
c += 1
return float(c) / len(idxes)
# the input is a tuple of gold_dict, model prediction and whether to use cache
# and teh output is whether the model prediction passes the entire test suite
def judge(args: Tuple[Dict[str, Any], str, bool]) -> bool:
gold_dict, pred, use_cache = args
testsuite_paths = gold_dict["testsuite"]
gold_query = gold_dict["query"]
order_matters = "order by" in gold_query.lower()
db_path = gold_dict["db_path"]
# if already computed sometime before
# and cache allowed, directly return the result
k = (db_path, gold_query, pred)
if use_cache and k in cache:
return cache[k]
pass_all_testcase = True
for testcase_path in testsuite_paths:
start = time.time()
flg, gold_result = exec_on_db(testcase_path, gold_query, timeout=GOLD_TIMEOUT)
duration = time.time() - start
timeout = ADDITIVE_OVERHEAD + MULTIPLICATIVE_OVERHEAD * duration
if flg != "result":
print("Warning: executing gold query results in an exception")
continue
flg, pred_result = exec_on_db(testcase_path, pred, timeout=int(timeout))
if flg != "result":
pass_all_testcase = False
break
if not result_eq(gold_result, pred_result, order_matters):
pass_all_testcase = False
break
# save the results in the cache
if use_cache:
cache[k] = pass_all_testcase
return pass_all_testcase
# cache is a dictionary
# the key is a ternary tuple (empty_database_path, SQL1, SQL2)
# the value is whether SQL1 and SQL2 are equivalent, judged by the test suites
def load_cache() -> Dict[Tuple[str, str, str], bool]:
if os.path.exists(cache_path):
d = m.dict(pkl.load(open(cache_path, "rb")))
for k, v in d.items():
cache[k] = v
return cache
# dump the cache
def save_cache():
pkl.dump(dict(cache), open(cache_path, "wb"))
def main(
preds: List[str],
gold_file: str = "classical_test.pkl",
verbose: bool = True,
num_processes: int = NUM_PROCESSES,
subset: str = "full",
use_cache: bool = True,
) -> List[bool]:
gold_dicts = pkl.load(open(gold_file, "rb"))
if subset != "full":
gold_dicts = [
d
for d in gold_dicts
if d["db_path"] == "database/{db_id}/{db_id}.sqlite".format(db_id=subset)
]
assert len(gold_dicts) == len(
preds
), "number of gold and prediction should be equal"
group_name2idxes = defaultdict(list)
for idx, gold_dict in enumerate(gold_dicts):
group_name2idxes[gold_dict["db_id"]].append(idx)
with Pool(num_processes) as pool:
result = list(
tqdm.tqdm(
pool.imap(judge, zip(gold_dicts, preds, repeat(use_cache, len(preds)))),
total=len(gold_dicts),
)
)
if verbose:
print("overall accuracy: ", acc(result))
for group, idxes in group_name2idxes.items():
print("accuracy for ", group, acc(result, idxes))
return result
if __name__ == "__main__":
start = time.time()
parser = argparse.ArgumentParser()
parser.add_argument(
"--gold",
dest="gold",
type=str,
default="classical_test.pkl",
help="the path to the predicted queries",
)
parser.add_argument(
"--pred", dest="pred", type=str, help="the path to the predicted queries"
)
parser.add_argument(
"--out_file", type=str, required=True, help="the output file path"
)
parser.add_argument(
"--num_processes", default=NUM_PROCESSES, help="number of processes to use"
)
parser.add_argument(
"--subset",
default="full",
choices=(
"atis",
"advising",
"academic",
"imdb",
"restaurants",
"geography",
"scholar",
"yelp",
"full",
),
help="which subset to evaluate on.",
)
parser.add_argument(
"--disable_cache",
default=False,
action="store_true",
help="whether to directly apply previously computed result and cache the current results. "
"use this flag to disable caching.",
)
args = parser.parse_args()
preds = load_predictions(args.pred)
assert not os.path.exists(args.out_file), (
"output file path %s already exists" % args.out_file
)
use_cache = not args.disable_cache
if use_cache:
load_cache()
result = main(
preds=preds,
gold_file=args.gold,
verbose=True,
num_processes=args.num_processes,
subset=args.subset,
use_cache=use_cache,
)
pkl.dump(result, open(args.out_file, "wb"))
print("total time used: ", time.time() - start)
if use_cache:
save_cache()
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