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e09df37 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 | """Generate README-ready PNG plots from `results/<policy>/{episodes.jsonl,summary.json}`.
Plots produced (saved to `results/plots/`):
return_hist.png histogram of per-episode return per policy
success_by_incident.png per-incident success rate per policy
per_step_reward.png average step reward per tool used (composed
across policies if multiple given)
return_curve.png rolling mean return over episode index
(sanity check that episodes are independent)
"""
from __future__ import annotations
import argparse
import json
from pathlib import Path
from typing import Any
import matplotlib
matplotlib.use("Agg") # headless
import matplotlib.pyplot as plt # noqa: E402
def _load_jsonl(path: Path) -> list[dict[str, Any]]:
out: list[dict[str, Any]] = []
with path.open(encoding="utf-8") as f:
for line in f:
line = line.strip()
if not line:
continue
out.append(json.loads(line))
return out
def plot_return_hist(results: dict[str, list[dict[str, Any]]], out_path: Path) -> None:
plt.figure(figsize=(8, 4.5))
for name, episodes in results.items():
returns = [e["return"] for e in episodes]
plt.hist(returns, bins=30, alpha=0.55, label=name)
plt.xlabel("Episode return")
plt.ylabel("Count")
plt.title("Per-episode return distribution by policy")
plt.legend()
plt.grid(alpha=0.3)
plt.tight_layout()
plt.savefig(out_path, dpi=150)
plt.close()
def plot_success_by_incident(
summaries: dict[str, dict[str, Any]],
out_path: Path,
) -> None:
incidents = sorted(
{iid for s in summaries.values() for iid in s.get("incidents", {})}
)
width = 0.8 / max(len(summaries), 1)
plt.figure(figsize=(9, 4.5))
for i, (name, summary) in enumerate(summaries.items()):
inc_counts = summary.get("incidents", {})
succ = summary.get("success_by_incident", {})
rates = [
(succ.get(iid, 0) / inc_counts[iid]) if inc_counts.get(iid) else 0.0
for iid in incidents
]
xs = [j + i * width for j in range(len(incidents))]
plt.bar(xs, rates, width=width, label=name)
plt.xticks(
[j + width * (len(summaries) - 1) / 2 for j in range(len(incidents))],
incidents,
)
plt.ylim(0, 1.05)
plt.ylabel("Success rate")
plt.title("Success rate per incident type")
plt.legend()
plt.grid(alpha=0.3, axis="y")
plt.tight_layout()
plt.savefig(out_path, dpi=150)
plt.close()
def plot_return_curve(
results: dict[str, list[dict[str, Any]]],
out_path: Path,
window: int = 10,
) -> None:
plt.figure(figsize=(9, 4.5))
for name, episodes in results.items():
returns = [e["return"] for e in episodes]
rolling = []
for i in range(len(returns)):
lo = max(0, i - window + 1)
chunk = returns[lo : i + 1]
rolling.append(sum(chunk) / len(chunk))
plt.plot(rolling, label=name)
plt.xlabel("Episode index")
plt.ylabel(f"Rolling mean return (window={window})")
plt.title("Episode return over evaluation order")
plt.legend()
plt.grid(alpha=0.3)
plt.tight_layout()
plt.savefig(out_path, dpi=150)
plt.close()
def plot_component_breakdown(
summaries: dict[str, dict[str, Any]],
out_path: Path,
) -> None:
components = [
"step_cost",
"downtime_cost",
"evidence_bonus",
"redundant_penalty",
"invalid_penalty",
"destructive_penalty",
"fix_bonus",
"timeout_penalty",
]
plt.figure(figsize=(10, 5))
width = 0.8 / max(len(summaries), 1)
for i, (name, summary) in enumerate(summaries.items()):
means = summary.get("component_means", {})
ys = [means.get(c, 0.0) for c in components]
xs = [j + i * width for j in range(len(components))]
plt.bar(xs, ys, width=width, label=name)
plt.xticks(
[j + width * (len(summaries) - 1) / 2 for j in range(len(components))],
components,
rotation=20,
ha="right",
)
plt.axhline(0, color="black", linewidth=0.6)
plt.ylabel("Mean per-step contribution")
plt.title("Reward component breakdown by policy")
plt.legend()
plt.grid(alpha=0.3, axis="y")
plt.tight_layout()
plt.savefig(out_path, dpi=150)
plt.close()
def main() -> None:
parser = argparse.ArgumentParser(description="Plot eval results")
parser.add_argument("--results-root", default="results")
parser.add_argument(
"--policies",
nargs="+",
default=None,
help="If unset, plot every subdirectory of --results-root.",
)
args = parser.parse_args()
root = Path(args.results_root)
if not root.exists():
raise SystemExit(f"missing results dir: {root}")
policy_names = args.policies
if not policy_names:
policy_names = sorted(
p.name for p in root.iterdir() if p.is_dir() and p.name != "plots"
)
results: dict[str, list[dict[str, Any]]] = {}
summaries: dict[str, dict[str, Any]] = {}
for name in policy_names:
ep_path = root / name / "episodes.jsonl"
sm_path = root / name / "summary.json"
if not ep_path.exists() or not sm_path.exists():
print(f"skip {name}: missing files")
continue
results[name] = _load_jsonl(ep_path)
summaries[name] = json.loads(sm_path.read_text(encoding="utf-8"))
if not results:
raise SystemExit("nothing to plot")
out = root / "plots"
out.mkdir(parents=True, exist_ok=True)
plot_return_hist(results, out / "return_hist.png")
plot_return_curve(results, out / "return_curve.png")
plot_success_by_incident(summaries, out / "success_by_incident.png")
plot_component_breakdown(summaries, out / "component_breakdown.png")
print(f"plots written to {out}")
if __name__ == "__main__":
main()
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