File size: 3,978 Bytes
778d47d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 | """Fast selector v2 data builder: use stored is_*_correct labels."""
import json, os, re, sys, random
from concurrent.futures import ThreadPoolExecutor, as_completed
os.environ["NO_PROXY"] = "localhost,127.0.0.1"
ROOT = "/home/datht/mats-sql-tist"
os.chdir(ROOT); sys.path.insert(0, ROOT)
from validator_data.validator import _execute_sql
from datasets import Dataset, DatasetDict
PROMPT_TEMPLATE = (
"You are a SQL correctness judge.\n"
"Schema:\n{schema}\n\n"
"Question: {question}\n"
"External knowledge: {evidence}\n\n"
"Candidate SQL:\n{sql}\n\n"
"Execution result:\n{exec_result}\n\n"
"Is this SQL correct for the question? Answer YES or NO."
)
SRC_PATHS = [
"data/rollouts/bird_train_3stage_K4.jsonl",
"data/rollouts/scaleup_bird_train_2stage_K4.jsonl",
"data/rollouts/scaleup_bird_train_3stage_K4.jsonl",
"data/rollouts/iter2_bird_train_3stage_K8.jsonl",
]
def safe_truncate(s, n=400):
s = str(s) if s is not None else ""
return s if len(s) <= n else s[:n] + "..."
def process_one(item):
s, sql, label = item
db_path = s["db_path"]
try:
p_resp, p_err = _execute_sql("./" + db_path, sql)
except Exception:
p_err = True; p_resp = ""
if p_err:
exec_str = f"Error: {str(p_resp)[:180]}"
else:
rows = str(p_resp)[:280]
exec_str = f"OK. Rows preview: {rows}" if rows.strip() and rows.strip() != "[]" else "OK. (no rows returned)"
prompt = PROMPT_TEMPLATE.format(
schema=safe_truncate(s.get("schema", ""), 3000),
question=s.get("question", ""),
evidence=s.get("evidence", "") or "None",
sql=safe_truncate(sql, 800),
exec_result=safe_truncate(exec_str, 300),
)
return {"prompt": prompt, "completion": label,
"messages": {"prompt": prompt, "completion": label},
"question": s.get("question", ""), "db_id": s.get("db_id", ""),
"label_int": 1 if label == "YES" else 0}
def main():
rng = random.Random(42)
work = []
seen = set()
for src in SRC_PATHS:
if not os.path.exists(src): continue
print(f"Loading {src}...", flush=True)
with open(src) as f:
for line in f:
s = json.loads(line)
q = s.get("question", "")
for t in s.get("trajectories", []):
sql = (t.get("fixed_sql") or t.get("planner_sql") or "").strip()
if not sql: continue
norm = re.sub(r"\s+", " ", sql).lower()
key = (q, norm)
if key in seen: continue
seen.add(key)
# Use stored labels: prefer fixed_sql label
if t.get("fixed_sql"):
label = "YES" if t.get("is_fixed_correct") else "NO"
else:
label = "YES" if t.get("is_planner_correct") else "NO"
work.append((s, sql, label))
print(f"Work items: {len(work)}", flush=True)
pairs = []
with ThreadPoolExecutor(max_workers=32) as exe:
futs = [exe.submit(process_one, it) for it in work]
n_done = 0
for fut in as_completed(futs):
pairs.append(fut.result())
n_done += 1
if n_done % 500 == 0:
print(f" {n_done}/{len(work)}", flush=True)
rng.shuffle(pairs)
n_test = max(200, len(pairs) // 25)
test = pairs[:n_test]; train = pairs[n_test:]
yes_train = sum(1 for p in train if p["completion"] == "YES")
print(f"=== v2 selector data ===")
print(f" train: {len(train)} ({100*yes_train/max(len(train),1):.1f}% YES)")
print(f" test: {len(test)}")
out = "/home/datht/mats-sql-tist/data/sft_selector_classifier_v2_rows"
DatasetDict({"train": Dataset.from_list(train), "test": Dataset.from_list(test)}).save_to_disk(out)
print(f" Saved {out}", flush=True)
if __name__ == "__main__":
main()
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