File size: 4,395 Bytes
797eb32 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 | 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()
|