| import csv |
| from collections import defaultdict |
| from pathlib import Path |
|
|
| import matplotlib.pyplot as plt |
|
|
| ROOT = Path(__file__).resolve().parents[1] |
| RESULTS = ROOT / "results" |
| FIGURES = ROOT / "figures" |
| OUT_DIR = FIGURES / "stepwise_comparison" |
| OUT_DIR.mkdir(parents=True, exist_ok=True) |
|
|
|
|
| def read_trajectory(path): |
| data = defaultdict(list) |
| if not path.exists(): |
| print(f"Missing: {path}") |
| return data |
|
|
| with path.open("r", encoding="utf-8") as f: |
| reader = csv.DictReader(f) |
| for row in reader: |
| data[row["scenario_id"]].append({ |
| "step": int(row["step"]), |
| "p_forward": float(row["p_forward"]), |
| }) |
|
|
| for sid in data: |
| data[sid].sort(key=lambda x: x["step"]) |
|
|
| return data |
|
|
|
|
| def read_metric_file(path): |
| metrics = {} |
| if not path.exists(): |
| return metrics |
| with path.open("r", encoding="utf-8") as f: |
| reader = csv.DictReader(f) |
| for row in reader: |
| try: |
| metrics[row["metric"]] = float(row["value"]) |
| except (ValueError, KeyError): |
| pass |
| return metrics |
|
|
|
|
| def plot_accuracy_comparison(): |
| baseline = read_metric_file(RESULTS / "converter_eval_summary.csv") |
| scenario_level = read_metric_file(RESULTS / "ollama_eval_summary.csv") |
| stepwise = read_metric_file(RESULTS / "stepwise_ollama_eval_summary.csv") |
|
|
| labels = ["Baseline", "EXAONE scenario", "EXAONE step-wise"] |
| values = [ |
| baseline.get("direction_accuracy", 0.0), |
| scenario_level.get("direction_accuracy", 0.0), |
| stepwise.get("direction_accuracy", 0.0), |
| ] |
|
|
| plt.figure(figsize=(9, 5)) |
| plt.bar(labels, values) |
| plt.ylim(0, 1) |
| plt.ylabel("Direction accuracy") |
| plt.title("Direction accuracy comparison") |
| plt.tight_layout() |
| out = FIGURES / "stepwise_vs_baseline_accuracy.png" |
| plt.savefig(out, dpi=160) |
| plt.close() |
| print(f"Saved: {out}") |
|
|
|
|
| def plot_parse_success(): |
| scenario_level = read_metric_file(RESULTS / "ollama_eval_summary.csv") |
| stepwise = read_metric_file(RESULTS / "stepwise_ollama_eval_summary.csv") |
|
|
| |
| scenario_steps = scenario_level.get("num_evaluated_steps", 0.0) |
| scenario_parse_rate = scenario_steps / 52.0 if scenario_steps else 0.0 |
|
|
| labels = ["EXAONE scenario", "EXAONE step-wise"] |
| values = [ |
| scenario_parse_rate, |
| stepwise.get("parse_success_rate", 0.0), |
| ] |
|
|
| plt.figure(figsize=(8, 5)) |
| plt.bar(labels, values) |
| plt.ylim(0, 1) |
| plt.ylabel("Parse success rate") |
| plt.title("Structured output parse success") |
| plt.tight_layout() |
| out = FIGURES / "stepwise_parse_success.png" |
| plt.savefig(out, dpi=160) |
| plt.close() |
| print(f"Saved: {out}") |
|
|
|
|
| def plot_all_stepwise_trajectories(stepwise): |
| plt.figure(figsize=(13, 7)) |
|
|
| for sid in sorted(stepwise.keys()): |
| xs = [r["step"] for r in stepwise[sid]] |
| ys = [r["p_forward"] for r in stepwise[sid]] |
| plt.plot(xs, ys, marker="o", linewidth=1.5, label=sid) |
|
|
| plt.axhline(0.65, linestyle="--", linewidth=1) |
| plt.axhline(0.35, linestyle="--", linewidth=1) |
| plt.ylim(0, 1) |
| plt.xlabel("Step") |
| plt.ylabel("p_forward") |
| plt.title("EXAONE step-wise belief trajectories") |
| plt.legend(ncol=2, fontsize=8) |
| plt.tight_layout() |
| out = FIGURES / "stepwise_ollama_trajectories.png" |
| plt.savefig(out, dpi=160) |
| plt.close() |
| print(f"Saved: {out}") |
|
|
|
|
| def plot_scenario_comparisons(gold, baseline, scenario_ollama, stepwise): |
| scenario_ids = sorted( |
| set(gold.keys()) |
| | set(baseline.keys()) |
| | set(scenario_ollama.keys()) |
| | set(stepwise.keys()) |
| ) |
|
|
| for sid in scenario_ids: |
| plt.figure(figsize=(9, 5)) |
|
|
| if sid in gold: |
| xs = [r["step"] for r in gold[sid]] |
| ys = [r["p_forward"] for r in gold[sid]] |
| plt.plot(xs, ys, marker="o", label="Gold") |
|
|
| if sid in baseline: |
| xs = [r["step"] for r in baseline[sid]] |
| ys = [r["p_forward"] for r in baseline[sid]] |
| plt.plot(xs, ys, marker="s", label="Baseline") |
|
|
| if sid in scenario_ollama: |
| xs = [r["step"] for r in scenario_ollama[sid]] |
| ys = [r["p_forward"] for r in scenario_ollama[sid]] |
| plt.plot(xs, ys, marker="^", label="EXAONE scenario") |
|
|
| if sid in stepwise: |
| xs = [r["step"] for r in stepwise[sid]] |
| ys = [r["p_forward"] for r in stepwise[sid]] |
| plt.plot(xs, ys, marker="D", label="EXAONE step-wise") |
|
|
| plt.axhline(0.65, linestyle="--", linewidth=1) |
| plt.axhline(0.35, linestyle="--", linewidth=1) |
| plt.ylim(0, 1) |
| plt.xlabel("Step") |
| plt.ylabel("p_forward") |
| plt.title(f"{sid}: Gold vs baseline vs EXAONE variants") |
| plt.legend() |
| plt.tight_layout() |
|
|
| out = OUT_DIR / f"{sid.lower()}_comparison.png" |
| plt.savefig(out, dpi=160) |
| plt.close() |
| print(f"Saved: {out}") |
|
|
|
|
| def main(): |
| gold = read_trajectory(RESULTS / "trajectories_gold.csv") |
| baseline = read_trajectory(RESULTS / "trajectories_auto.csv") |
| scenario_ollama = read_trajectory(RESULTS / "trajectories_ollama.csv") |
| stepwise = read_trajectory(RESULTS / "trajectories_stepwise_ollama.csv") |
|
|
| plot_accuracy_comparison() |
| plot_parse_success() |
| plot_all_stepwise_trajectories(stepwise) |
| plot_scenario_comparisons(gold, baseline, scenario_ollama, stepwise) |
|
|
|
|
| if __name__ == "__main__": |
| main() |
|
|
