| """ |
| Selector v3 SFT data builder: PAIRWISE framing with HARD NEGATIVES. |
| |
| For each BIRD-train question with at least one YES and one NO trajectory: |
| - Build (A, B) pairs where the gold answer is A or B (balanced 50/50). |
| - Hard negatives: prefer NO SQL with highest lexical overlap to YES SQL (Jaccard |
| on lowercased token n-grams). Falls back to random NO if overlap is uniform. |
| - Both SQLs include row-preview exec result in the prompt (matching v2 style). |
| |
| Output: HF dataset at data/sft_selector_v3_pairwise/{train,test} |
| Format: {"messages": [...chat...], "prompt": str, "completion": "A" or "B", ...} |
| """ |
| import json, os, re, sys, random |
| from collections import defaultdict |
| from concurrent.futures import ThreadPoolExecutor, as_completed |
|
|
| ROOT = "/weka/s225250685/mats-tist" |
| os.chdir(ROOT); sys.path.insert(0, ROOT) |
|
|
| os.environ.setdefault("DB_EXEC_API_DISABLE", "1") |
| os.environ.setdefault("PYTHONNOUSERSITE", "1") |
|
|
| from validator_data.validator import _execute_sql |
| from datasets import Dataset, DatasetDict |
|
|
| PAIRWISE_PROMPT = ( |
| "You are a SQL correctness judge.\n" |
| "Schema:\n{schema}\n\n" |
| "Question: {question}\n" |
| "External knowledge: {evidence}\n\n" |
| "Candidate A:\n{sql_a}\n\n" |
| "Execution result of A:\n{exec_a}\n\n" |
| "Candidate B:\n{sql_b}\n\n" |
| "Execution result of B:\n{exec_b}\n\n" |
| "Which candidate is MORE LIKELY to correctly answer the question? Answer A or B." |
| ) |
|
|
| 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", |
| ] |
|
|
| OUT_DIR = "data/sft_selector_v3_pairwise" |
| HARDNEG_PER_POS = 3 |
| MAX_PAIRS_PER_QUESTION = 8 |
|
|
| 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 tokens(sql): |
| |
| return set(re.findall(r"[a-zA-Z_][a-zA-Z0-9_]*|[<>=!]+", (sql or "").lower())) |
|
|
| def jaccard(a, b): |
| if not a or not b: return 0.0 |
| ai, ui = a & b, a | b |
| return len(ai) / max(len(ui), 1) |
|
|
| def exec_str(db_path, sql): |
| try: |
| r, err = _execute_sql("./" + db_path, sql, timeout=10) |
| except Exception as e: |
| return f"Error: {str(e)[:160]}" |
| if err: |
| return f"Error: {str(r)[:160]}" |
| rows = str(r)[:260] |
| if rows.strip() and rows.strip() != "[]": |
| return f"OK. Rows preview: {rows}" |
| return "OK. (no rows returned)" |
|
|
| def load_question_groups(rng): |
| """Walk all rollouts, return list of (sample, [(sql, label_is_correct), ...]) per question.""" |
| by_q = {} |
| for src in SRC_PATHS: |
| if not os.path.exists(src): |
| print(f"skip missing: {src}", flush=True) |
| continue |
| print(f"loading {src}...", flush=True) |
| with open(src) as f: |
| for line in f: |
| line = line.strip() |
| if not line: continue |
| s = json.loads(line) |
| key = (s.get("question",""), s.get("db_id","")) |
| if key not in by_q: |
| by_q[key] = {"sample": s, "cands": [], "seen": set()} |
| 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()) |
| if norm in by_q[key]["seen"]: continue |
| by_q[key]["seen"].add(norm) |
| is_correct = bool(t.get("is_fixed_correct") if t.get("fixed_sql") else t.get("is_planner_correct")) |
| by_q[key]["cands"].append((sql, is_correct)) |
| print(f"questions: {len(by_q)}", flush=True) |
| out = [] |
| for k, v in by_q.items(): |
| yes = [c for c in v["cands"] if c[1]] |
| no = [c for c in v["cands"] if not c[1]] |
| if not yes or not no: |
| continue |
| out.append((v["sample"], yes, no)) |
| print(f"questions with both YES and NO: {len(out)}", flush=True) |
| return out |
|
|
| def build_pair_records(rng, qgroups): |
| """Build pairwise records: for each question, pair each YES with hardest NOs.""" |
| work = [] |
| for sample, yes, no in qgroups: |
| |
| no_scored = [] |
| for ns, _ in no: |
| best = max(jaccard(tokens(ns), tokens(ys)) for ys, _ in yes) |
| no_scored.append((best, ns)) |
| no_scored.sort(reverse=True) |
|
|
| pairs = [] |
| for ys, _ in yes: |
| |
| chosen = no_scored[:HARDNEG_PER_POS] |
| for _, ns in chosen: |
| pairs.append((ys, ns)) |
| if len(pairs) >= MAX_PAIRS_PER_QUESTION: |
| break |
|
|
| rng.shuffle(pairs) |
| pairs = pairs[:MAX_PAIRS_PER_QUESTION] |
| for ys, ns in pairs: |
| work.append((sample, ys, ns)) |
| return work |
|
|
| def render_one(rng, item): |
| sample, sql_yes, sql_no = item |
| db_path = sample["db_path"] |
| schema = safe_truncate(sample.get("schema", ""), 2000) |
| question = sample.get("question", "") |
| evidence = sample.get("evidence", "") or "None" |
|
|
| exec_yes = safe_truncate(exec_str(db_path, sql_yes), 240) |
| exec_no = safe_truncate(exec_str(db_path, sql_no), 240) |
|
|
| |
| if rng.random() < 0.5: |
| a_sql, b_sql, a_exec, b_exec, label = sql_yes, sql_no, exec_yes, exec_no, "A" |
| else: |
| a_sql, b_sql, a_exec, b_exec, label = sql_no, sql_yes, exec_no, exec_yes, "B" |
|
|
| prompt = PAIRWISE_PROMPT.format( |
| schema=schema, question=question, evidence=evidence, |
| sql_a=safe_truncate(a_sql, 700), exec_a=a_exec, |
| sql_b=safe_truncate(b_sql, 700), exec_b=b_exec, |
| ) |
| return { |
| "prompt": prompt, |
| "completion": label, |
| "messages": [ |
| {"role": "user", "content": prompt}, |
| {"role": "assistant", "content": label}, |
| ], |
| "question": question, |
| "db_id": sample.get("db_id", ""), |
| } |
|
|
| def main(): |
| rng = random.Random(42) |
| qgroups = load_question_groups(rng) |
| pairs = build_pair_records(rng, qgroups) |
| print(f"pair records to render: {len(pairs)}", flush=True) |
|
|
| out = [] |
| with ThreadPoolExecutor(max_workers=32) as exe: |
| futs = [exe.submit(render_one, rng, p) for p in pairs] |
| n_done = 0 |
| for fut in as_completed(futs): |
| try: |
| out.append(fut.result()) |
| except Exception as e: |
| print(f"render err: {e}", flush=True) |
| n_done += 1 |
| if n_done % 1000 == 0: |
| print(f" {n_done}/{len(pairs)}", flush=True) |
|
|
| rng.shuffle(out) |
| n_test = max(500, len(out) // 25) |
| test = out[:n_test]; train = out[n_test:] |
| n_a = sum(1 for r in train if r["completion"] == "A") |
| print(f"=== v3 pairwise selector data ===") |
| print(f" train: {len(train)} ({100*n_a/max(len(train),1):.1f}% A-label)") |
| print(f" test: {len(test)}") |
| DatasetDict({ |
| "train": Dataset.from_list(train), |
| "test": Dataset.from_list(test), |
| }).save_to_disk(OUT_DIR) |
| print(f" saved {OUT_DIR}", flush=True) |
|
|
| if __name__ == "__main__": |
| main() |
|
|