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Build v7 pointwise data from BIRD-DEV K=8 rollouts split by db_id.
Adds fb_* features to prompt.
Trains on 8 dbs (1268 Q × 3 rollout files), holds out 3 smallest dbs (256 Q) for clean test.
Note: full BIRD-dev eval will have some db overlap (contamination), but holdout-DB is clean.
"""
import argparse, json, os, re, sys, random
from concurrent.futures import ThreadPoolExecutor, as_completed
os.environ.setdefault("PYTHONNOUSERSITE", "1")
os.environ.setdefault("DB_EXEC_API_DISABLE", "1")
ROOT = "/weka/s225250685/mats-tist"
os.chdir(ROOT); sys.path.insert(0, ROOT)
from validator_data.validator import _execute_sql
from datasets import Dataset, DatasetDict
from scripts.rich_schema import render_rich_schema
POINTWISE_PROMPT = (
"You are a SQL correctness judge for the BIRD benchmark.\n"
"Database Schema (with column meanings, value descriptions, and example values):\n"
"{schema}\n\n"
"Question: {question}\n"
"External knowledge: {evidence}\n\n"
"Candidate SQL:\n{sql}\n\n"
"Execution result of the candidate:\n{exec_result}\n\n"
"Validator critique of the planner draft (for context):\n"
" - select: {fb_select}\n"
" - condition: {fb_condition}\n"
" - join: {fb_join}\n"
" - order: {fb_order}\n\n"
"Does this SQL correctly answer the question, given the schema, the column "
"descriptions, the external knowledge, the execution result, and the validator's critique? "
"Answer YES or NO."
)
MAX_SCHEMA_CHARS = 3000
HOLDOUT_DBS = {"debit_card_specializing", "california_schools", "financial"}
def safe_truncate(s, n):
s = str(s) if s is not None else ""
return s if len(s) <= n else s[:n] + "..."
def exec_str(db_path, sql, timeout=8):
if not sql or not sql.strip(): return "Error: empty SQL"
try:
r, err = _execute_sql("./" + db_path if not db_path.startswith("./") else db_path, sql, timeout=timeout)
except Exception as e:
return f"Error: {str(e)[:160]}"
if err: return f"Error: {str(r)[:160]}"
rows = str(r)[:260]
return f"OK. Rows preview: {rows}" if rows.strip() and rows.strip() != "[]" else "OK. (no rows returned)"
def render(sample, t, schema_text):
sql_fixed = (t.get("fixed_sql") or "").strip()
sql = sql_fixed or (t.get("planner_sql") or "").strip()
if not sql: return None
is_correct = bool(t.get("is_fixed_correct") if sql_fixed else t.get("is_planner_correct"))
ex = exec_str(sample["db_path"], sql)
label = "YES" if is_correct else "NO"
prompt = POINTWISE_PROMPT.format(
schema=schema_text,
question=sample.get("question", ""),
evidence=sample.get("evidence", "") or "None",
sql=safe_truncate(sql, 800),
exec_result=safe_truncate(ex, 300),
fb_select=safe_truncate(t.get("fb_select") or "None", 200),
fb_condition=safe_truncate(t.get("fb_condition") or "None", 200),
fb_join=safe_truncate(t.get("fb_join") or "None", 200),
fb_order=safe_truncate(t.get("fb_order") or "None", 200),
)
return {
"prompt": prompt,
"completion": label,
"messages": [
{"role": "user", "content": prompt},
{"role": "assistant", "content": label},
],
"question": sample.get("question", ""),
"db_id": sample.get("db_id", ""),
"is_yes": int(label == "YES"),
}
def main():
ap = argparse.ArgumentParser()
ap.add_argument("--inputs", nargs="+", default=[
"eval_results/paper_SFT_VF_passAt8_bird_dev.jsonl",
"eval_results/paper_COLLAB_par_passAt8_bird_dev.jsonl",
"eval_results/paper_INDEP_par_passAt8_bird_dev.jsonl",
])
ap.add_argument("--out", default="data/sft_selector_v7_dev_pointwise_fb")
args = ap.parse_args()
rng = random.Random(42)
train_recs = []
holdout_recs = []
schema_cache = {}
n_yes = n_no = 0
n_rows = 0
# Phase 1: collect all (sample, trajectory) jobs first.
jobs = []
for inp in args.inputs:
if not os.path.exists(inp):
print(f"SKIP {inp}", flush=True); continue
print(f"Reading {inp}", flush=True)
with open(inp) as f:
for line in f:
line = line.strip()
if not line: continue
s = json.loads(line)
n_rows += 1
seen = set()
for t in s.get("trajectories", []):
sql_fixed = (t.get("fixed_sql") or "").strip()
sql = sql_fixed or (t.get("planner_sql") or "").strip()
if not sql: continue
norm = re.sub(r"\s+", " ", sql.lower())
if norm in seen: continue
seen.add(norm)
jobs.append((s, t))
print(f"Total jobs to exec+render: {len(jobs)} (from {n_rows} questions)", flush=True)
# Cache schemas (CPU-bound, fast)
for s, _ in jobs:
key = s["db_id"]
if key not in schema_cache:
schema_cache[key] = safe_truncate(render_rich_schema(s, split="dev"), MAX_SCHEMA_CHARS)
# Phase 2: parallel render (exec is the bottleneck, threadable)
def _job(item):
s, t = item
return s, render(s, t, schema_cache[s["db_id"]])
n_done = 0
with ThreadPoolExecutor(max_workers=32) as exe:
futs = [exe.submit(_job, it) for it in jobs]
for fut in as_completed(futs):
try:
s, rec = fut.result()
except Exception:
continue
n_done += 1
if rec is None: continue
is_holdout = s["db_id"] in HOLDOUT_DBS
target = holdout_recs if is_holdout else train_recs
target.append(rec)
if rec["is_yes"]: n_yes += 1
else: n_no += 1
if n_done % 1000 == 0:
print(f" rendered {n_done}/{len(jobs)} train={len(train_recs)} holdout={len(holdout_recs)} (Y={n_yes}, N={n_no})", flush=True)
print(f"\nAfter all files: train={len(train_recs)} holdout={len(holdout_recs)}", flush=True)
rng.shuffle(train_recs)
rng.shuffle(holdout_recs)
# Balance NO to <= 1.2*YES in train
yes_t = [r for r in train_recs if r["is_yes"]]
no_t = [r for r in train_recs if not r["is_yes"]]
rng.shuffle(no_t)
keep_no = no_t[: min(len(no_t), int(1.2 * len(yes_t)))]
train_recs = yes_t + keep_no
rng.shuffle(train_recs)
print(f"balanced train: {len(train_recs)} (Y={len(yes_t)}, N={len(keep_no)})", flush=True)
DatasetDict({
"train": Dataset.from_list(train_recs),
"test": Dataset.from_list(holdout_recs[: max(200, len(holdout_recs) // 10)]),
"holdout_test": Dataset.from_list(holdout_recs),
}).save_to_disk(args.out)
print(f"SAVED: {args.out}")
if __name__ == "__main__":
main()
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