File size: 5,601 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 | 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-level parse success can be inferred from evaluated steps / 52 if present.
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()
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