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"""
Evaluate baseline query-translation prediction files against gold UPPAAL models.
Each prediction file contains exactly 100 plain-text formulas (one per line),
ordered S01-Q1 … S01-Q5, S02-Q1 … S20-Q5.
For each formula the script:
1. Runs verifyta against the matching gold model (datasets/gold_models/S{id:02d}/model.xml).
2. Records the T/F verdict (or '?' on compile failure).
3. Compares against the expected answer from datasets/query_translation_eval.csv.
Outputs:
artifacts/baselines/<name>/trans_query_eval.csv — per-query detail
artifacts/results/rq2_baseline_trans_query_summary.txt — per-spec + overall table
Usage
-----
python scripts/eval_baselines_trans_query.py
python scripts/eval_baselines_trans_query.py --baselines gpt claude
"""
from __future__ import annotations
import argparse
import csv
import sys
from pathlib import Path
ROOT = Path(__file__).resolve().parents[1]
sys.path.insert(0, str(ROOT / "src"))
from frame.pipeline.model_checking_pipeline import ModelCheckingPipeline
from frame.pipeline.nl_uppaal_query import parse_verifyta_text_verdict
EVAL_CSV = ROOT / "datasets" / "query_translation_eval.csv"
GOLD_DIR = ROOT / "datasets" / "gold_models"
BASELINES = ROOT / "artifacts" / "baselines"
RESULTS = ROOT / "artifacts" / "results"
FIELDNAMES = ["spec_id", "nl_query", "uppaal_query", "status", "verdict", "expected", "correct"]
ALL_BASELINES = ["gpt", "claude", "grok"]
def load_eval_csv() -> list[dict]:
with EVAL_CSV.open(encoding="utf-8", newline="") as f:
return list(csv.DictReader(f))
def load_predictions(path: Path) -> list[str]:
"""Read up to 100 non-empty lines from a prediction file."""
lines = []
for ln in path.read_text(encoding="utf-8", errors="replace").splitlines():
ln = ln.strip()
if ln:
lines.append(ln)
if len(lines) == 100:
break
return lines
def verdict_char(v: str) -> str:
return {"satisfied": "T", "not_satisfied": "F"}.get(v, "?")
def evaluate(name: str, preds: list[str], eval_rows: list[dict]) -> list[dict]:
checkers: dict[int, ModelCheckingPipeline] = {}
results: list[dict] = []
for idx, row in enumerate(eval_rows):
sid = int(row["spec_id"])
nl = row["nl_query"].strip()
exp = row["answer"].strip().upper()
formula = preds[idx].strip() if idx < len(preds) else ""
if formula:
if sid not in checkers:
xml = GOLD_DIR / f"S{sid:02d}" / "model.xml"
checkers[sid] = ModelCheckingPipeline(str(xml))
res, _, errs = checkers[sid].verify(formula)
v = parse_verifyta_text_verdict(res, errors=errs)
verdict = verdict_char(v)
else:
verdict = "?"
correct = "Y" if verdict == exp else "N"
results.append({
"spec_id": sid,
"nl_query": nl,
"uppaal_query": formula,
"status": "ok" if verdict in ("T", "F") else "compile_fail",
"verdict": verdict,
"expected": exp,
"correct": correct,
})
return results
def write_csv(out_path: Path, rows: list[dict]) -> None:
out_path.parent.mkdir(parents=True, exist_ok=True)
with out_path.open("w", encoding="utf-8", newline="") as f:
w = csv.DictWriter(f, fieldnames=FIELDNAMES)
w.writeheader()
w.writerows(rows)
def summarise(name: str, rows: list[dict]) -> dict:
total = len(rows)
correct = sum(1 for r in rows if r["correct"] == "Y")
compile_ok = sum(1 for r in rows if r["verdict"] in ("T", "F"))
return {
"name": name,
"total": total,
"correct": correct,
"compile_ok": compile_ok,
"qar": 100 * correct // total if total else 0,
"qcr": 100 * compile_ok // total if total else 0,
}
def main() -> int:
ap = argparse.ArgumentParser(description=__doc__,
formatter_class=argparse.RawDescriptionHelpFormatter)
ap.add_argument("--baselines", nargs="+", default=ALL_BASELINES,
help="Which baselines to evaluate (default: gpt claude grok)")
ap.add_argument("--pred-file", default="trans_query.txt",
help="Prediction filename inside each baseline folder (default: trans_query.txt)")
args = ap.parse_args()
eval_rows = load_eval_csv()
if len(eval_rows) != 100:
print(f"WARNING: eval CSV has {len(eval_rows)} rows (expected 100)", file=sys.stderr)
RESULTS.mkdir(parents=True, exist_ok=True)
summaries: list[dict] = []
for name in args.baselines:
pred_path = BASELINES / name / args.pred_file
if not pred_path.is_file():
print(f"[{name}] prediction file not found: {pred_path} — skipping")
continue
preds = load_predictions(pred_path)
print(f"[{name}] loaded {len(preds)} predictions from {pred_path.name}")
rows = evaluate(name, preds, eval_rows)
out_csv = BASELINES / name / "trans_query_eval.csv"
write_csv(out_csv, rows)
s = summarise(name, rows)
summaries.append(s)
# Per-spec breakdown
print(f"\n {'Spec':<6} {'Score':>5} Q1 Q2 Q3 Q4 Q5")
print(f" {'-'*38}")
for sid in range(1, 21):
spec_rows = [r for r in rows if r["spec_id"] == sid]
marks = [("+" if r["correct"] == "Y" else "x") for r in spec_rows]
sc = marks.count("+")
print(f" S{sid:02d} {sc}/5 " + " ".join(marks))
print(f" {'-'*38}")
print(f" Total {s['correct']}/{s['total']} QAR={s['qar']}% QCR={s['qcr']}%")
print(f" Saved -> {out_csv}\n")
# Overall comparison table
print("\n" + "=" * 55)
print(f"{'Baseline':<10} {'Correct':>8} {'QAR':>6} {'QCR':>6}")
print("-" * 55)
for s in summaries:
print(f"{s['name']:<10} {s['correct']:>4}/{s['total']:<4} {s['qar']:>4}% {s['qcr']:>4}%")
# Also include ours if available
ours_csv = ROOT / "artifacts" / "ours" / "trans_query.csv"
if ours_csv.is_file():
ours_rows = list(csv.DictReader(ours_csv.open(encoding="utf-8", newline="")))
if "correct" in (ours_rows[0] if ours_rows else {}):
oc = sum(1 for r in ours_rows if r["correct"] == "Y")
ot = len(ours_rows)
oqcr = sum(1 for r in ours_rows if r.get("verdict") in ("T", "F"))
print(f"{'ours':<10} {oc:>4}/{ot:<4} {100*oc//ot if ot else 0:>4}% {100*oqcr//ot if ot else 0:>4}%")
print("=" * 55)
# Write summary text file
summary_path = RESULTS / "rq2_baseline_trans_query_summary.txt"
with summary_path.open("w", encoding="utf-8") as f:
f.write(f"{'Baseline':<10} {'Correct':>8} {'QAR':>6} {'QCR':>6}\n")
f.write("-" * 35 + "\n")
for s in summaries:
f.write(f"{s['name']:<10} {s['correct']:>4}/{s['total']:<4} {s['qar']:>4}% {s['qcr']:>4}%\n")
if ours_csv.is_file() and ours_rows and "correct" in ours_rows[0]:
f.write(f"{'ours':<10} {oc:>4}/{ot:<4} {100*oc//ot if ot else 0:>4}% {100*oqcr//ot if ot else 0:>4}%\n")
print(f"\nSummary saved -> {summary_path}")
return 0
if __name__ == "__main__":
raise SystemExit(main())
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