| import csv |
| import json |
| import math |
| from collections import defaultdict |
| from pathlib import Path |
|
|
| ROOT = Path(__file__).resolve().parents[1] |
| SCENARIOS = ROOT / "scenarios" |
| RESULTS = ROOT / "results" |
|
|
| IN_PATH = RESULTS / "ollama_stepwise_evidence.csv" |
| TRAJ_OUT = RESULTS / "trajectories_stepwise_ollama.csv" |
| SUMMARY_OUT = RESULTS / "stepwise_ollama_scenario_summary.csv" |
|
|
| LEARNING_RATE = 0.4 |
|
|
|
|
| def sigmoid(x: float) -> float: |
| return 1.0 / (1.0 + math.exp(-x)) |
|
|
|
|
| def verdict_from_p(p: float) -> str: |
| if p > 0.65: |
| return "forward" |
| if p < 0.35: |
| return "backward" |
| return "ambiguous" |
|
|
|
|
| def parse_bool(value): |
| s = str(value).strip().lower() |
| if s == "true": |
| return True |
| if s == "false": |
| return False |
| return None |
|
|
|
|
| def load_source_weights(): |
| weights = {} |
| for path in sorted(SCENARIOS.glob("sc*.json")): |
| scenario = json.loads(path.read_text(encoding="utf-8")) |
| sid = scenario["id"] |
| for i, step in enumerate(scenario["steps"], start=1): |
| weights[(sid, i)] = float(step.get("source_weight", 1.0)) |
| return weights |
|
|
|
|
| def main(): |
| if not IN_PATH.exists(): |
| raise FileNotFoundError(f"Missing input file: {IN_PATH}") |
|
|
| source_weights = load_source_weights() |
|
|
| by_scenario = defaultdict(list) |
| with IN_PATH.open("r", encoding="utf-8") as f: |
| reader = csv.DictReader(f) |
| for row in reader: |
| try: |
| row["step_int"] = int(row["step"]) |
| except (TypeError, ValueError): |
| continue |
| by_scenario[row["scenario_id"]].append(row) |
|
|
| trajectory_rows = [] |
| summary_rows = [] |
|
|
| for sid in sorted(by_scenario.keys()): |
| rows = sorted(by_scenario[sid], key=lambda r: r["step_int"]) |
|
|
| p = 0.5 |
| prev_delta = None |
| flips = 0 |
| parsed_steps = 0 |
| skipped_steps = 0 |
|
|
| trajectory_rows.append({ |
| "scenario_id": sid, |
| "step": 0, |
| "p_forward": round(p, 6), |
| "delta": 0.0, |
| "direction_flips": 0, |
| "supports_forward": "", |
| "confidence": "", |
| "source_weight": "", |
| "skipped": False, |
| }) |
|
|
| for row in rows: |
| step = row["step_int"] |
| parse_ok = str(row.get("parse_ok", "")).lower() == "true" |
| supports_forward = parse_bool(row.get("supports_forward", "")) |
| confidence = float(row["confidence"]) if row.get("confidence") else 0.5 |
| source_weight = source_weights.get((sid, step), 1.0) |
|
|
| if not parse_ok or supports_forward is None: |
| skipped_steps += 1 |
| trajectory_rows.append({ |
| "scenario_id": sid, |
| "step": step, |
| "p_forward": round(p, 6), |
| "delta": 0.0, |
| "direction_flips": flips, |
| "supports_forward": "", |
| "confidence": confidence, |
| "source_weight": source_weight, |
| "skipped": True, |
| }) |
| continue |
|
|
| parsed_steps += 1 |
| direction = 1 if supports_forward else -1 |
| old_p = p |
| old_logit = math.log(p / (1 - p)) |
| new_logit = old_logit + direction * confidence * source_weight * LEARNING_RATE |
| p = max(0.001, min(0.999, sigmoid(new_logit))) |
|
|
| delta = p - old_p |
| if prev_delta is not None and delta * prev_delta < 0: |
| flips += 1 |
| if delta != 0: |
| prev_delta = delta |
|
|
| trajectory_rows.append({ |
| "scenario_id": sid, |
| "step": step, |
| "p_forward": round(p, 6), |
| "delta": round(delta, 6), |
| "direction_flips": flips, |
| "supports_forward": supports_forward, |
| "confidence": confidence, |
| "source_weight": source_weight, |
| "skipped": False, |
| }) |
|
|
| total_steps = parsed_steps + skipped_steps |
|
|
| summary_rows.append({ |
| "scenario_id": sid, |
| "final_p": round(p, 6), |
| "verdict": verdict_from_p(p), |
| "direction_flips": flips, |
| "parsed_steps": parsed_steps, |
| "skipped_steps": skipped_steps, |
| "total_steps": total_steps, |
| "parse_success_rate": round(parsed_steps / total_steps, 6) if total_steps else 0.0, |
| }) |
|
|
| with TRAJ_OUT.open("w", encoding="utf-8", newline="") as f: |
| fieldnames = [ |
| "scenario_id", |
| "step", |
| "p_forward", |
| "delta", |
| "direction_flips", |
| "supports_forward", |
| "confidence", |
| "source_weight", |
| "skipped", |
| ] |
| writer = csv.DictWriter(f, fieldnames=fieldnames) |
| writer.writeheader() |
| writer.writerows(trajectory_rows) |
|
|
| with SUMMARY_OUT.open("w", encoding="utf-8", newline="") as f: |
| fieldnames = [ |
| "scenario_id", |
| "final_p", |
| "verdict", |
| "direction_flips", |
| "parsed_steps", |
| "skipped_steps", |
| "total_steps", |
| "parse_success_rate", |
| ] |
| writer = csv.DictWriter(f, fieldnames=fieldnames) |
| writer.writeheader() |
| writer.writerows(summary_rows) |
|
|
| print(f"Saved: {TRAJ_OUT}") |
| print(f"Saved: {SUMMARY_OUT}") |
|
|
|
|
| if __name__ == "__main__": |
| main() |
|
|
