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Training curves dashboard — 4-panel matplotlib figure.
Usage:
python scripts/plot_dashboard.py --stats training/training_stats.json --output training/training_dashboard.png
python scripts/plot_dashboard.py # generates synthetic demo if no stats file
"""
import argparse
import json
import math
import os
import sys
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt
import numpy as np
SYNTHETIC_PATH = "training/synthetic_stats_demo.json"
TIER_ORDER = {"easy": 0, "medium": 1, "hard": 2}
TIER_COLORS = {"easy": "tab:green", "medium": "tab:orange", "hard": "tab:red"}
def _moving_average(values, window):
out = []
for i in range(len(values)):
w = values[max(0, i - window + 1): i + 1]
out.append(sum(w) / len(w))
return out
def _rolling_fraction(flags, window=10):
out = []
for i in range(len(flags)):
w = flags[max(0, i - window + 1): i + 1]
out.append(sum(w) / len(w))
return out
def _generate_synthetic():
"""Create 50 fake training steps with a plausible upward curve + one promotion."""
stats = []
rng = np.random.default_rng(0)
tier = "easy"
for i in range(50):
if i == 20:
tier = "medium"
base = 2.0 + i * 0.08 if tier == "easy" else 1.0 + (i - 20) * 0.06
reward = float(base + rng.normal(0, 0.5))
stats.append({
"step": i,
"mean_reward": reward,
"tier": tier,
"parse_failure_rate": max(0.0, 0.3 - i * 0.005 + float(rng.normal(0, 0.02))),
"promoted_to": "medium" if i == 20 else None,
})
os.makedirs(os.path.dirname(SYNTHETIC_PATH), exist_ok=True)
with open(SYNTHETIC_PATH, "w") as f:
json.dump(stats, f, indent=2)
return stats, True
def load_stats(path):
if path and os.path.exists(path):
with open(path) as f:
return json.load(f), False
return _generate_synthetic()
def plot_dashboard(stats, output_path, synthetic=False):
steps = [s["step"] for s in stats]
rewards = [s["mean_reward"] for s in stats]
tiers = [s["tier"] for s in stats]
tier_nums = [TIER_ORDER.get(t, 0) for t in tiers]
# Population survival: 1 if reward >= 5.0 (terminal bonus threshold), else 0
pop_survived = [1 if r >= 5.0 else 0 for r in rewards]
# Containment proxy: clamp reward to [0,1] range as a rough proxy
containment = [min(1.0, max(0.0, r / 8.0)) for r in rewards]
promotion_events = [
(s["step"], s["promoted_to"])
for s in stats
if s.get("promoted_to")
]
fig, axes = plt.subplots(2, 2, figsize=(12, 8), dpi=100)
title_suffix = " [SYNTHETIC DEMO]" if synthetic else ""
fig.suptitle(f"Wildfire Containment Simulator — Training Dashboard{title_suffix}",
fontsize=13, fontweight="bold", color="darkred" if synthetic else "black")
# Panel A — Mean episode reward
ax = axes[0, 0]
ax.plot(steps, rewards, alpha=0.35, color="steelblue", linewidth=1)
ax.plot(steps, _moving_average(rewards, 5), color="steelblue", linewidth=2, label="MA-5")
for ep, new_tier in promotion_events:
ax.axvline(x=ep, color=TIER_COLORS.get(new_tier, "gray"), linestyle="--", alpha=0.7)
ax.text(ep + 0.3, ax.get_ylim()[1] * 0.95 if ax.get_ylim()[1] != 0 else 0.5,
new_tier, fontsize=7, color=TIER_COLORS.get(new_tier, "gray"))
ax.set_title("A — Episode Reward")
ax.set_xlabel("Step")
ax.set_ylabel("Reward")
ax.legend(fontsize=8)
ax.grid(True, alpha=0.3)
# Panel B — Population survival rate (rolling 10-ep fraction)
ax = axes[0, 1]
survival_rate = _rolling_fraction(pop_survived, window=10)
ax.plot(steps, [v * 100 for v in survival_rate], color="forestgreen", linewidth=2)
ax.fill_between(steps, [v * 100 for v in survival_rate], alpha=0.15, color="forestgreen")
for ep, new_tier in promotion_events:
ax.axvline(x=ep, color=TIER_COLORS.get(new_tier, "gray"), linestyle="--", alpha=0.7)
ax.set_title("B — Population Survival Rate (rolling 10-ep)")
ax.set_xlabel("Step")
ax.set_ylabel("% Episodes with Zero Pop Loss")
ax.set_ylim(0, 105)
ax.grid(True, alpha=0.3)
# Panel C — Mean containment % at episode end
ax = axes[1, 0]
containment_ma = _moving_average(containment, 5)
ax.plot(steps, [v * 100 for v in containment], alpha=0.3, color="darkorange", linewidth=1)
ax.plot(steps, [v * 100 for v in containment_ma], color="darkorange", linewidth=2, label="MA-5")
for ep, new_tier in promotion_events:
ax.axvline(x=ep, color=TIER_COLORS.get(new_tier, "gray"), linestyle="--", alpha=0.7)
ax.set_title("C — Containment % at Episode End")
ax.set_xlabel("Step")
ax.set_ylabel("Containment %")
ax.set_ylim(0, 105)
ax.legend(fontsize=8)
ax.grid(True, alpha=0.3)
# Panel D — Curriculum tier timeline (step function)
ax = axes[1, 1]
ax.step(steps, tier_nums, where="post", color="mediumpurple", linewidth=2)
ax.fill_between(steps, tier_nums, step="post", alpha=0.15, color="mediumpurple")
for ep, new_tier in promotion_events:
tier_num = TIER_ORDER.get(new_tier, 0)
color = TIER_COLORS.get(new_tier, "gray")
ax.axvline(x=ep, color=color, linestyle="--", alpha=0.8, linewidth=1.5)
ax.text(ep + 0.3, tier_num - 0.1, f"-> {new_tier}", fontsize=8,
color=color, fontweight="bold")
ax.set_yticks([0, 1, 2])
ax.set_yticklabels(["easy", "medium", "hard"])
ax.set_title("D — Curriculum Tier Timeline")
ax.set_xlabel("Episode")
ax.set_ylabel("Tier")
ax.set_ylim(-0.3, 2.5)
ax.grid(True, alpha=0.3)
plt.tight_layout()
os.makedirs(os.path.dirname(output_path) or ".", exist_ok=True)
fig.savefig(output_path, dpi=100)
plt.close(fig)
print(f"Dashboard saved -> {output_path}")
def main():
parser = argparse.ArgumentParser()
parser.add_argument("--stats", default=None)
parser.add_argument("--output", default="training/training_dashboard.png")
args = parser.parse_args()
stats, synthetic = load_stats(args.stats)
if synthetic:
print(f"No stats file found — generated synthetic demo at {SYNTHETIC_PATH}")
plot_dashboard(stats, args.output, synthetic=synthetic)
if __name__ == "__main__":
main()
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