| """ |
| Build SFT data for a binary correctness classifier (selection agent). |
| |
| Input: rollout JSONL from run_pipeline_rollouts.py |
| Output: HF DatasetDict where each row is one (question, schema, candidate_sql, exec_result) trajectory |
| with a YES/NO label based on whether the final SQL is correct. |
| |
| The selector at eval time scores each of N candidates with this classifier and picks the highest |
| yes-probability candidate. |
| |
| Usage: |
| python scripts/build_selector_sft_data.py \\ |
| --rollouts data/rollouts/scaleup_bird_train_2stage_K4.jsonl \\ |
| --output_dir data/sft_selector_classifier |
| """ |
| import argparse |
| import json |
| import os |
| import random |
| import re |
| import sys |
|
|
| ROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) |
| os.chdir(ROOT) |
|
|
|
|
| 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." |
| ) |
|
|
|
|
| def safe_truncate(s, n=400): |
| if s is None: |
| return "(empty)" |
| s = str(s) |
| return s if len(s) <= n else s[:n] + "..." |
|
|
|
|
| def main(): |
| parser = argparse.ArgumentParser() |
| parser.add_argument("--rollouts", required=True) |
| parser.add_argument("--output_dir", required=True) |
| parser.add_argument("--train_frac", type=float, default=0.95) |
| args = parser.parse_args() |
|
|
| print(f"Loading {args.rollouts}...") |
| samples = [] |
| with open(args.rollouts) as f: |
| for line in f: |
| line = line.strip() |
| if not line: |
| continue |
| samples.append(json.loads(line)) |
| print(f" {len(samples)} samples") |
|
|
| rows = [] |
| n_correct = n_wrong = 0 |
| for s in samples: |
| schema = s.get("schema", "") |
| question = s.get("question", "") |
| evidence = s.get("evidence", "") or "None" |
| for t in s.get("trajectories", []): |
| fixed_sql = t.get("fixed_sql") or t.get("planner_sql") |
| if not fixed_sql or not fixed_sql.strip(): |
| continue |
| |
| |
| exec_response = "" |
| if t.get("planner_exec_ok"): |
| exec_response = "OK" |
| else: |
| exec_response = "Error / no rows" |
|
|
| label = "YES" if t.get("is_fixed_correct") else "NO" |
| if label == "YES": |
| n_correct += 1 |
| else: |
| n_wrong += 1 |
|
|
| prompt = PROMPT_TEMPLATE.format( |
| schema=safe_truncate(schema, 3000), |
| question=question, |
| evidence=evidence, |
| sql=safe_truncate(fixed_sql, 800), |
| exec_result=safe_truncate(exec_response, 300), |
| ) |
| rows.append({ |
| "prompt": prompt, |
| "completion": label, |
| "messages": {"prompt": prompt, "completion": label}, |
| "question": question, |
| "db_id": s.get("db_id", ""), |
| "label_int": 1 if label == "YES" else 0, |
| }) |
|
|
| print(f"Built {len(rows)} (correct={n_correct} wrong={n_wrong})") |
|
|
| random.seed(42) |
| indices = list(range(len(rows))) |
| random.shuffle(indices) |
| n_train = int(len(rows) * args.train_frac) |
| train_rows = [rows[i] for i in indices[:n_train]] |
| test_rows = [rows[i] for i in indices[n_train:]] or [rows[-1]] |
|
|
| from datasets import Dataset, DatasetDict |
| import shutil |
| if os.path.exists(args.output_dir): |
| shutil.rmtree(args.output_dir) |
| os.makedirs(args.output_dir, exist_ok=True) |
| ds = DatasetDict({ |
| "train": Dataset.from_list(train_rows), |
| "test": Dataset.from_list(test_rows), |
| }) |
| ds.save_to_disk(args.output_dir) |
| print(f"Saved DatasetDict (train={len(train_rows)}, test={len(test_rows)}) → {args.output_dir}") |
|
|
|
|
| if __name__ == "__main__": |
| main() |
|
|
