Datasets:
File size: 3,428 Bytes
4bd5225 | 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 | from __future__ import annotations
from pathlib import Path
import matplotlib.pyplot as plt
import pandas as pd
def setup_style() -> None:
plt.style.use("default")
plt.rcParams.update(
{
"figure.dpi": 180,
"savefig.dpi": 300,
"axes.grid": True,
"grid.alpha": 0.25,
"axes.spines.top": False,
"axes.spines.right": False,
"font.size": 10,
}
)
def make_figures(run_dir: str | Path, figures_dir: str | Path) -> list[Path]:
setup_style()
run = Path(run_dir)
output = Path(figures_dir)
output.mkdir(parents=True, exist_ok=True)
metrics = pd.read_csv(run / "metrics.csv")
city_hour = pd.read_csv(run / "city_hour_policy.csv")
written: list[Path] = []
fig, ax = plt.subplots(figsize=(7, 4))
for policy, frame in metrics.groupby("policy"):
ax.plot(frame["episode"], frame["cumulative_reward"], marker="o", label=policy)
ax.set_xlabel("Episode")
ax.set_ylabel("Episode Return")
ax.legend()
path = output / "learning_curves.png"
fig.tight_layout()
fig.savefig(path)
plt.close(fig)
written.append(path)
fig, ax = plt.subplots(figsize=(6, 4))
for policy, frame in metrics.groupby("policy"):
ax.scatter(frame["physics_violation_rate"], frame["cumulative_reward"], s=70, label=policy)
ax.set_xlabel("Physics Violation Rate")
ax.set_ylabel("Episode Return")
ax.legend()
path = output / "safety_utility_frontier.png"
fig.tight_layout()
fig.savefig(path)
plt.close(fig)
written.append(path)
heat_source = city_hour.groupby(["policy", "city", "hour"], as_index=False)["liquidity_ratio"].mean()
preferred_policy = "ppo" if "ppo" in set(heat_source["policy"]) else sorted(heat_source["policy"].unique())[0]
static_heat = _policy_heat(heat_source, "static")
agent_heat = _policy_heat(heat_source, preferred_policy)
if static_heat is None:
static_heat = agent_heat.copy()
static_heat, agent_heat = static_heat.align(agent_heat, join="outer", axis=None, fill_value=0.0)
fig, axes = plt.subplots(1, 2, figsize=(10, 4), sharey=True)
vmin = min(float(static_heat.min().min()), float(agent_heat.min().min()))
vmax = max(float(static_heat.max().max()), float(agent_heat.max().max()))
for ax, heat, title in [
(axes[0], static_heat, "Static 1:3"),
(axes[1], agent_heat, preferred_policy),
]:
image = ax.imshow(heat.to_numpy(), aspect="auto", cmap="YlGnBu", vmin=vmin, vmax=vmax)
ax.set_xticks(range(len(heat.columns)))
ax.set_xticklabels([str(col) for col in heat.columns], rotation=0)
ax.set_yticks(range(len(heat.index)))
ax.set_yticklabels(heat.index)
ax.set_xlabel("Hour of Day")
ax.set_title(title)
axes[0].set_ylabel("City")
cbar = fig.colorbar(image, ax=axes.ravel().tolist())
cbar.set_label("Liquidity Split")
path = output / "city_hour_liquidity_heatmap.png"
fig.subplots_adjust(wspace=0.12)
fig.savefig(path)
plt.close(fig)
written.append(path)
return written
def _policy_heat(heat_source: pd.DataFrame, policy: str) -> pd.DataFrame | None:
frame = heat_source[heat_source["policy"].eq(policy)]
if frame.empty:
return None
return frame.pivot_table(index="city", columns="hour", values="liquidity_ratio", fill_value=0.0)
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