chore: remove stale scripts/build_fixer_replanner_iter2.py

scripts/build_fixer_replanner_iter2.py

@@ -1,102 +0,0 @@
-"""Build fixer ORPO iter-2 're-planner' dataset.
-
-Insight: the current fixer is too conservative — it changes planner_sql only 1.4%
-of the time and rescues 0/533 hard questions on BIRD-dev. The fixer architecture
-needs to be re-framed: instead of 'apply small critique-driven edit', train it as
-a re-planner that produces a COMPLETE correct alternative when given a failed
-attempt.
-
-Data source: K=4 BIRD-train rollouts. For each question, find a (wrong-trajectory,
-correct-trajectory) pair within the K=4 samples. Use:
-- chosen = correct trajectory's planner_sql (the alternative that works)
-- rejected = wrong trajectory's planner_sql or the fixer's mistaken output
-- prompt = fixer's standard prompt with the wrong trajectory as the input
-
-Output: data/llm_alignment/scaleup_iter2_v3/hf_fixer_replanner
-"""
-import json
-import os
-import random
-import re
-from datasets import Dataset, DatasetDict
-
-OUT_DIR = "/home/datht/mats-sql-tist/data/llm_alignment/scaleup_iter2_v3/hf_fixer_replanner"
-
-SRC_PATHS = [
-    "/home/datht/mats-sql-tist/data/rollouts/bird_train_3stage_K4.jsonl",
-    "/home/datht/mats-sql-tist/data/rollouts/scaleup_bird_train_3stage_K4.jsonl",
-    "/home/datht/mats-sql-tist/data/rollouts/iter2_bird_train_3stage_K8.jsonl",
-    "/home/datht/mats-sql-tist/data/rollouts/scaleup_bird_train_2stage_K4.jsonl",
-]
-
-
-def normalize_sql(sql):
-    return re.sub(r"\s+", " ", sql or "").lower().strip()
-
-
-def main():
-    rng = random.Random(42)
-    pairs = []
-    seen_keys = set()  # (question_hash, wrong_sql_hash) → dedup
-
-    for p in SRC_PATHS:
-        if not os.path.exists(p):
-            continue
-        with open(p) as f:
-            for line in f:
-                s = json.loads(line)
-                traj = s.get("trajectories", [])
-                if len(traj) < 2:
-                    continue
-                correct_trajs = [t for t in traj if t.get("is_planner_correct")]
-                wrong_trajs = [t for t in traj if not t.get("is_planner_correct")]
-                if not correct_trajs or not wrong_trajs:
-                    continue
-                # Build (wrong → correct) pairs within the K samples
-                for wt in wrong_trajs:
-                    wsql = (wt.get("planner_sql") or "").strip()
-                    if not wsql:
-                        continue
-                    # Pick the shortest correct planner_sql as the "preferred" alternative
-                    correct_trajs_sorted = sorted(correct_trajs, key=lambda t: len(t.get("planner_sql") or ""))
-                    csql = (correct_trajs_sorted[0].get("planner_sql") or "").strip()
-                    if not csql or normalize_sql(csql) == normalize_sql(wsql):
-                        continue
-                    fixer_prompt = (wt.get("fixer_prompt") or "").strip()
-                    if not fixer_prompt:
-                        continue
-                    key = (hash(s.get("question", "")), hash(normalize_sql(wsql)))
-                    if key in seen_keys:
-                        continue
-                    seen_keys.add(key)
-                    chosen_text = f"```sql\n{csql}\n```"
-                    rejected_text = f"```sql\n{wsql}\n```"
-                    pairs.append({
-                        "prompt": fixer_prompt,
-                        "chosen": chosen_text,
-                        "rejected": rejected_text,
-                        "db_path": s.get("db_path", ""),
-                        "question": s.get("question", ""),
-                        "db_id": s.get("db_id", ""),
-                    })
-
-    rng.shuffle(pairs)
-
-    n_test = max(40, len(pairs) // 30)
-    test = pairs[:n_test]
-    train = pairs[n_test:]
-
-    dd = DatasetDict({
-        "train_dpo": Dataset.from_list(train),
-        "test_dpo": Dataset.from_list(test),
-    })
-    dd.save_to_disk(OUT_DIR)
-
-    print(f"=== Fixer ORPO iter-2 RE-PLANNER dataset ===")
-    print(f"  total pairs: {len(pairs)}")
-    print(f"  train: {len(train)}, test: {len(test)}")
-    print(f"  Saved to {OUT_DIR}")
-
-
-if __name__ == "__main__":
-    main()