chore: remove stale scripts/build_validator_sft_v3_balanced.py

scripts/build_validator_sft_v3_balanced.py

@@ -1,168 +0,0 @@
-"""Build v3 validator SFT data with balanced all-OK + critique rows.
-
-v2 had 8.1% all-OK rows → validator hallucinates critiques at inference.
-v3 supplements v2 with ~5000 all-OK rows mined from real planner_correct
-trajectories on BIRD-TRAIN, so the validator learns to stay silent when
-the planner SQL is already correct.
-
-Output: data/multi-agents/fixed/sft-validator-diverse-v3
-"""
-import json
-import random
-from datasets import load_from_disk, Dataset, DatasetDict
-
-OK_TEMPLATES = [
-    """<select>
-SELECT.
-No issues with SELECT.
-</select>
-
-<condition>
-CONDITION.
-No issues with WHERE/HAVING.
-</condition>
-
-<join>
-JOIN.
-Tables and join keys look correct.
-</join>
-
-<order>
-ORDER BY.
-None
-</order>""",
-    """<select>
-SELECT.
-The SELECT clause is correct.
-</select>
-
-<condition>
-CONDITION.
-Filter conditions look correct.
-</condition>
-
-<join>
-JOIN.
-No issues with JOIN.
-</join>
-
-<order>
-ORDER BY.
-None
-</order>""",
-    """<select>
-SELECT.
-None
-</select>
-
-<condition>
-CONDITION.
-None
-</condition>
-
-<join>
-JOIN.
-None
-</join>
-
-<order>
-ORDER BY.
-None
-</order>""",
-    """<select>
-SELECT.
-The projection list matches the question.
-</select>
-
-<condition>
-CONDITION.
-WHERE/HAVING clauses are correct.
-</condition>
-
-<join>
-JOIN.
-Tables and join keys are correct.
-</join>
-
-<order>
-ORDER BY.
-The ordering is correct.
-</order>""",
-]
-
-
-def main():
-    rng = random.Random(42)
-
-    # Load existing v2 (force plain-dict copy; drop "messages" because v2 stores it as a non-list dict that breaks arrow)
-    v2 = load_from_disk("/home/datht/mats-sql-tist/data/multi-agents/fixed/sft-validator-diverse-v2")
-    v2_train = [{"prompt": r["prompt"], "completion": r["completion"]} for r in v2["train"]]
-    v2_test = [{"prompt": r["prompt"], "completion": r["completion"]} for r in v2["test"]]
-
-    # Mine all-OK rows from K=4 train rollouts (planner_correct trajectories)
-    src = "/home/datht/mats-sql-tist/data/rollouts/scaleup_bird_train_2stage_K4.jsonl"
-    ok_rows = []
-    seen_prompts = set()
-    with open(src) as f:
-        for line in f:
-            s = json.loads(line)
-            for t in s.get("trajectories", []):
-                if not t.get("is_planner_correct"):
-                    continue
-                vp = (t.get("validator_prompt") or "").strip()
-                if not vp:
-                    # rebuild from planner_prompt
-                    pp = (t.get("planner_prompt") or "").strip()
-                    psql = (t.get("planner_sql") or "").strip()
-                    if not pp or not psql:
-                        continue
-                    vp = pp + "\n\nSQL query:\n" + psql
-                # dedup on full vp
-                if vp in seen_prompts:
-                    continue
-                seen_prompts.add(vp)
-                ok_rows.append(vp)
-
-    rng.shuffle(ok_rows)
-
-    # Aim: balance such that all-OK ≈ critique. v2 has ~5208 critique rows.
-    target_ok = 5200
-    ok_rows = ok_rows[:target_ok]
-
-    # Add additional sft-style critique training: use v2 + new all-OK
-    new_rows = []
-    for vp in ok_rows:
-        completion = rng.choice(OK_TEMPLATES)
-        new_rows.append({"prompt": vp, "completion": completion})
-
-    # Test split: keep v2 test + small mined sample
-    test_ok = ok_rows[target_ok:target_ok + 100] if len(ok_rows) > target_ok else []
-    new_test_rows = []
-    for vp in test_ok:
-        completion = rng.choice(OK_TEMPLATES)
-        new_test_rows.append({"prompt": vp, "completion": completion})
-
-    # Combine
-    train_combined = v2_train + new_rows
-    test_combined = v2_test + new_test_rows
-    rng.shuffle(train_combined)
-
-    dd = DatasetDict({
-        "train": Dataset.from_list(train_combined),
-        "test": Dataset.from_list(test_combined),
-    })
-
-    out_dir = "/home/datht/mats-sql-tist/data/multi-agents/fixed/sft-validator-diverse-v3"
-    dd.save_to_disk(out_dir)
-
-    # Stats
-    n_train = len(train_combined)
-    n_train_ok = sum(1 for r in train_combined if "No issues" in r["completion"] or r["completion"].count("None") >= 3)
-    print(f"v3 built:")
-    print(f"  train: {n_train} ({n_train_ok} all-OK, {n_train - n_train_ok} critique)")
-    print(f"  test:  {len(test_combined)}")
-    print(f"  Saved to {out_dir}")
-
-
-if __name__ == "__main__":
-    main()