| 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 / "order_v3_comparison" |
| OUT_DIR.mkdir(parents=True, exist_ok=True) |
|
|
|
|
| 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 Exception: |
| pass |
| return metrics |
|
|
|
|
| def read_trajectory(path): |
| data = defaultdict(list) |
| if not path.exists(): |
| 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 r: r["step"]) |
| return data |
|
|
|
|
| def plot_accuracy(): |
| baseline = read_metric_file(RESULTS / "converter_eval_summary.csv") |
| scenario = read_metric_file(RESULTS / "ollama_eval_summary.csv") |
| stepwise = read_metric_file(RESULTS / "stepwise_ollama_eval_summary.csv") |
| order_v3 = read_metric_file(RESULTS / "order_v3_eval_summary.csv") |
|
|
| labels = ["Baseline", "EXAONE scenario", "EXAONE step-wise", "EXAONE order v3"] |
| values = [ |
| baseline.get("direction_accuracy", 0.0), |
| scenario.get("direction_accuracy", 0.0), |
| stepwise.get("direction_accuracy", 0.0), |
| order_v3.get("direction_accuracy", 0.0), |
| ] |
|
|
| plt.figure(figsize=(10, 5)) |
| plt.bar(labels, values) |
| plt.ylim(0, 1) |
| plt.ylabel("Direction accuracy") |
| plt.title("Direction accuracy across extraction methods") |
| plt.xticks(rotation=15, ha="right") |
| plt.tight_layout() |
| out = FIGURES / "order_v3_accuracy_comparison.png" |
| plt.savefig(out, dpi=160) |
| plt.close() |
| print(f"Saved: {out}") |
|
|
|
|
| def plot_parse_success(): |
| scenario = read_metric_file(RESULTS / "ollama_eval_summary.csv") |
| stepwise = read_metric_file(RESULTS / "stepwise_ollama_eval_summary.csv") |
| order_v3 = read_metric_file(RESULTS / "order_v3_eval_summary.csv") |
|
|
| labels = ["EXAONE scenario", "EXAONE step-wise", "EXAONE order v3"] |
| values = [ |
| scenario.get("num_evaluated_steps", 0.0) / 52.0, |
| stepwise.get("parse_success_rate", 0.0), |
| order_v3.get("parse_success_rate", 0.0), |
| ] |
|
|
| plt.figure(figsize=(9, 5)) |
| plt.bar(labels, values) |
| plt.ylim(0, 1) |
| plt.ylabel("Parse success rate") |
| plt.title("Structured output parse success") |
| plt.xticks(rotation=15, ha="right") |
| plt.tight_layout() |
| out = FIGURES / "order_v3_parse_success_comparison.png" |
| plt.savefig(out, dpi=160) |
| plt.close() |
| print(f"Saved: {out}") |
|
|
|
|
| def plot_scenario_comparison(): |
| gold = read_trajectory(RESULTS / "trajectories_gold.csv") |
| baseline = read_trajectory(RESULTS / "trajectories_auto.csv") |
| stepwise = read_trajectory(RESULTS / "trajectories_stepwise_ollama.csv") |
| order_v3 = read_trajectory(RESULTS / "trajectories_order_v3_ollama.csv") |
|
|
| scenario_ids = sorted(set(gold) | set(baseline) | set(stepwise) | set(order_v3)) |
|
|
| for sid in scenario_ids: |
| plt.figure(figsize=(9, 5)) |
|
|
| for label, data, marker in [ |
| ("Gold", gold, "o"), |
| ("Baseline", baseline, "s"), |
| ("EXAONE step-wise", stepwise, "^"), |
| ("EXAONE order v3", order_v3, "D"), |
| ]: |
| if sid in data: |
| xs = [r["step"] for r in data[sid]] |
| ys = [r["p_forward"] for r in data[sid]] |
| plt.plot(xs, ys, marker=marker, label=label) |
|
|
| 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}: Order v3 comparison") |
| plt.legend() |
| plt.tight_layout() |
|
|
| out = OUT_DIR / f"{sid.lower()}_order_v3_comparison.png" |
| plt.savefig(out, dpi=160) |
| plt.close() |
| print(f"Saved: {out}") |
|
|
|
|
| def main(): |
| plot_accuracy() |
| plot_parse_success() |
| plot_scenario_comparison() |
|
|
|
|
| if __name__ == "__main__": |
| main() |
|
|
