| import csv |
| import json |
| from pathlib import Path |
|
|
| ROOT = Path(__file__).resolve().parents[1] |
| SCENARIOS = ROOT / "scenarios" |
| RESULTS = ROOT / "results" |
|
|
| EVIDENCE_IN = RESULTS / "ollama_order_v3_evidence.csv" |
| SUMMARY_IN = RESULTS / "order_v3_ollama_scenario_summary.csv" |
| EVAL_OUT = RESULTS / "order_v3_eval.csv" |
| SUMMARY_OUT = RESULTS / "order_v3_eval_summary.csv" |
|
|
|
|
| def parse_bool(value): |
| s = str(value).strip().lower() |
| if s == "true": |
| return True |
| if s == "false": |
| return False |
| return None |
|
|
|
|
| def load_gold(): |
| gold = {} |
| expected_verdict = {} |
|
|
| for path in sorted(SCENARIOS.glob("sc*.json")): |
| scenario = json.loads(path.read_text(encoding="utf-8")) |
| sid = scenario["id"] |
| expected_verdict[sid] = scenario.get("expected_verdict", "") |
|
|
| for i, step in enumerate(scenario["steps"], start=1): |
| gold[(sid, i)] = { |
| "supports_forward": bool(step["supports_forward"]), |
| "strength": float(step["strength"]), |
| } |
|
|
| return gold, expected_verdict |
|
|
|
|
| def main(): |
| if not EVIDENCE_IN.exists(): |
| raise FileNotFoundError(f"Missing input file: {EVIDENCE_IN}") |
|
|
| gold, expected_verdict = load_gold() |
|
|
| rows = [] |
| all_steps = 0 |
| parse_ok_count = 0 |
| parsed_total = 0 |
| parsed_correct = 0 |
| conf_errors = [] |
|
|
| with EVIDENCE_IN.open("r", encoding="utf-8") as f: |
| reader = csv.DictReader(f) |
| for row in reader: |
| sid = row["scenario_id"] |
| try: |
| step = int(row["step"]) |
| except Exception: |
| continue |
|
|
| key = (sid, step) |
| if key not in gold: |
| continue |
|
|
| all_steps += 1 |
| parse_ok = str(row.get("parse_ok", "")).lower() == "true" |
| if parse_ok: |
| parse_ok_count += 1 |
|
|
| pred = parse_bool(row.get("supports_forward", "")) |
| gold_val = gold[key]["supports_forward"] |
| direction_correct = pred == gold_val if pred is not None else False |
|
|
| if pred is not None: |
| parsed_total += 1 |
| if direction_correct: |
| parsed_correct += 1 |
|
|
| confidence = float(row["confidence"]) if row.get("confidence") else 0.5 |
| gold_strength = gold[key]["strength"] |
| conf_error = abs(confidence - gold_strength) |
|
|
| if pred is not None: |
| conf_errors.append(conf_error) |
|
|
| rows.append({ |
| "scenario_id": sid, |
| "step": step, |
| "parse_ok": parse_ok, |
| "order": row.get("order", ""), |
| "gold_supports_forward": gold_val, |
| "pred_supports_forward": "" if pred is None else pred, |
| "direction_correct": direction_correct, |
| "gold_strength": gold_strength, |
| "pred_confidence": confidence, |
| "confidence_abs_error": round(conf_error, 6), |
| "evidence": row.get("evidence", ""), |
| }) |
|
|
| scenario_verdicts = {} |
| if SUMMARY_IN.exists(): |
| with SUMMARY_IN.open("r", encoding="utf-8") as f: |
| reader = csv.DictReader(f) |
| for row in reader: |
| scenario_verdicts[row["scenario_id"]] = row.get("verdict", "") |
|
|
| verdict_total = 0 |
| verdict_correct = 0 |
| for sid, expected in expected_verdict.items(): |
| pred = scenario_verdicts.get(sid) |
| if pred: |
| verdict_total += 1 |
| if pred == expected: |
| verdict_correct += 1 |
|
|
| parse_success_rate = parse_ok_count / all_steps if all_steps else 0.0 |
| direction_accuracy = parsed_correct / parsed_total if parsed_total else 0.0 |
| confidence_mae = sum(conf_errors) / len(conf_errors) if conf_errors else 0.0 |
| verdict_accuracy = verdict_correct / verdict_total if verdict_total else 0.0 |
|
|
| with EVAL_OUT.open("w", encoding="utf-8", newline="") as f: |
| fieldnames = [ |
| "scenario_id", |
| "step", |
| "parse_ok", |
| "order", |
| "gold_supports_forward", |
| "pred_supports_forward", |
| "direction_correct", |
| "gold_strength", |
| "pred_confidence", |
| "confidence_abs_error", |
| "evidence", |
| ] |
| writer = csv.DictWriter(f, fieldnames=fieldnames) |
| writer.writeheader() |
| writer.writerows(rows) |
|
|
| with SUMMARY_OUT.open("w", encoding="utf-8", newline="") as f: |
| writer = csv.writer(f) |
| writer.writerow(["metric", "value"]) |
| writer.writerow(["parse_success_rate", round(parse_success_rate, 6)]) |
| writer.writerow(["direction_accuracy", round(direction_accuracy, 6)]) |
| writer.writerow(["confidence_mae", round(confidence_mae, 6)]) |
| writer.writerow(["verdict_accuracy", round(verdict_accuracy, 6)]) |
| writer.writerow(["num_all_steps", all_steps]) |
| writer.writerow(["num_parsed_steps", parsed_total]) |
| writer.writerow(["num_evaluated_verdicts", verdict_total]) |
|
|
| print(f"Saved: {EVAL_OUT}") |
| print(f"Saved: {SUMMARY_OUT}") |
| print(f"parse_success_rate={parse_success_rate:.4f}") |
| print(f"direction_accuracy={direction_accuracy:.4f}") |
| print(f"confidence_mae={confidence_mae:.4f}") |
| print(f"verdict_accuracy={verdict_accuracy:.4f}") |
|
|
|
|
| if __name__ == "__main__": |
| main() |
|
|
