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d19137b | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 | """Visualization for RecallTrace adversarial self-play training.
Two main functions:
- show_training_curves(): 2x2 panel with F1, adversary reward, quarantined, steps
- show_episode_comparison(): side-by-side early vs late episode comparison
"""
from __future__ import annotations
import os
from typing import Any, Dict, List
import numpy as np
def _rolling_average(data: List[float], window: int = 20) -> List[float]:
"""Compute rolling average with the given window size."""
result = []
for i in range(len(data)):
start = max(0, i - window + 1)
result.append(sum(data[start:i+1]) / (i - start + 1))
return result
def show_training_curves(
stats: List[Dict[str, Any]],
save_path: str = "plots/selfplay_training.png",
) -> None:
"""Create a 2x2 publication-quality training curves figure.
Top left: Investigator F1 over episodes (raw + rolling avg)
Top right: Adversary reward over episodes
Bottom left: Nodes quarantined over episodes
Bottom right: Steps to finalize over episodes
Uses a dark theme for hackathon-ready visuals.
"""
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt
from matplotlib import font_manager
episodes = [s["episode"] for s in stats]
f1_scores = [s["investigator_f1"] for s in stats]
adv_rewards = [s["adversary_reward"] for s in stats]
quarantined = [s["num_quarantined"] for s in stats]
steps = [s["steps_taken"] for s in stats]
f1_rolling = _rolling_average(f1_scores)
adv_rolling = _rolling_average(adv_rewards)
q_rolling = _rolling_average(quarantined)
s_rolling = _rolling_average(steps)
# --- Dark theme setup ---
plt.style.use("dark_background")
fig, axes = plt.subplots(2, 2, figsize=(16, 10))
fig.patch.set_facecolor("#0d1117")
colors = {
"f1_raw": "#3b82f6", # blue
"f1_avg": "#60a5fa", # light blue
"adv_raw": "#ef4444", # red
"adv_avg": "#f87171", # light red
"q_raw": "#22c55e", # green
"q_avg": "#4ade80", # light green
"s_raw": "#f59e0b", # amber
"s_avg": "#fbbf24", # light amber
}
bg_color = "#161b22"
grid_color = "#30363d"
text_color = "#e6edf3"
for ax in axes.flat:
ax.set_facecolor(bg_color)
ax.tick_params(colors=text_color, labelsize=10)
ax.spines["bottom"].set_color(grid_color)
ax.spines["left"].set_color(grid_color)
ax.spines["top"].set_visible(False)
ax.spines["right"].set_visible(False)
ax.grid(True, alpha=0.15, color=grid_color)
# --- Top Left: Investigator F1 ---
ax = axes[0, 0]
ax.scatter(episodes, f1_scores, c=colors["f1_raw"], alpha=0.15, s=8, zorder=2)
ax.plot(episodes, f1_rolling, color=colors["f1_avg"], linewidth=2.5, zorder=3, label="20-ep rolling avg")
ax.axhline(y=0.5, color="#ef4444", linestyle="--", alpha=0.4, linewidth=1)
ax.axhline(y=0.8, color="#22c55e", linestyle="--", alpha=0.4, linewidth=1)
ax.set_title("Investigator F1 Score", fontsize=14, color=text_color, fontweight="bold", pad=12)
ax.set_xlabel("Episode", color=text_color, fontsize=11)
ax.set_ylabel("F1 Score", color=text_color, fontsize=11)
ax.set_ylim(-0.05, 1.05)
ax.legend(loc="lower right", fontsize=9, facecolor=bg_color, edgecolor=grid_color)
# Add annotations
ax.text(0.02, 0.95, "Adversary wins ↓", transform=ax.transAxes,
fontsize=8, color="#ef4444", alpha=0.7, va="top")
ax.text(0.02, 0.05, "Investigator wins ↑", transform=ax.transAxes,
fontsize=8, color="#22c55e", alpha=0.7, va="bottom")
# --- Top Right: Adversary Reward ---
ax = axes[0, 1]
ax.scatter(episodes, adv_rewards, c=colors["adv_raw"], alpha=0.15, s=8, zorder=2)
ax.plot(episodes, adv_rolling, color=colors["adv_avg"], linewidth=2.5, zorder=3, label="20-ep rolling avg")
ax.axhline(y=0, color=text_color, linestyle="-", alpha=0.2, linewidth=1)
ax.set_title("Adversary Reward", fontsize=14, color=text_color, fontweight="bold", pad=12)
ax.set_xlabel("Episode", color=text_color, fontsize=11)
ax.set_ylabel("Reward", color=text_color, fontsize=11)
ax.set_ylim(-1.3, 1.3)
ax.legend(loc="upper right", fontsize=9, facecolor=bg_color, edgecolor=grid_color)
# --- Bottom Left: Nodes Quarantined ---
ax = axes[1, 0]
ax.scatter(episodes, quarantined, c=colors["q_raw"], alpha=0.15, s=8, zorder=2)
ax.plot(episodes, q_rolling, color=colors["q_avg"], linewidth=2.5, zorder=3, label="20-ep rolling avg")
ax.set_title("Nodes Quarantined per Episode", fontsize=14, color=text_color, fontweight="bold", pad=12)
ax.set_xlabel("Episode", color=text_color, fontsize=11)
ax.set_ylabel("Count", color=text_color, fontsize=11)
ax.legend(loc="upper right", fontsize=9, facecolor=bg_color, edgecolor=grid_color)
# --- Bottom Right: Steps Taken ---
ax = axes[1, 1]
ax.scatter(episodes, steps, c=colors["s_raw"], alpha=0.15, s=8, zorder=2)
ax.plot(episodes, s_rolling, color=colors["s_avg"], linewidth=2.5, zorder=3, label="20-ep rolling avg")
ax.set_title("Steps to Finalize", fontsize=14, color=text_color, fontweight="bold", pad=12)
ax.set_xlabel("Episode", color=text_color, fontsize=11)
ax.set_ylabel("Steps", color=text_color, fontsize=11)
ax.legend(loc="upper right", fontsize=9, facecolor=bg_color, edgecolor=grid_color)
# --- Main title ---
fig.suptitle(
"RecallTrace — Adversarial Self-Play Training",
fontsize=18, color=text_color, fontweight="bold", y=0.98,
)
fig.text(
0.5, 0.935,
"Investigator vs Adversary co-evolution over 200 episodes",
ha="center", fontsize=11, color="#8b949e",
)
plt.tight_layout(rect=[0, 0, 1, 0.92])
# Save
os.makedirs(os.path.dirname(save_path), exist_ok=True)
fig.savefig(save_path, dpi=200, bbox_inches="tight", facecolor=fig.get_facecolor())
plt.close(fig)
print(f" Saved training curves to {save_path}")
def show_episode_comparison(
early_stats: Dict[str, Any],
late_stats: Dict[str, Any],
save_path: str = "plots/episode_comparison.png",
) -> None:
"""Create a side-by-side comparison of early vs late episode behavior.
Shows: nodes visited, nodes quarantined, F1 score, belief confidence,
intervention type, correctly identified or not.
"""
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt
from matplotlib.patches import FancyBboxPatch
fig, (ax_early, ax_late) = plt.subplots(1, 2, figsize=(18, 9))
fig.patch.set_facecolor("#0d1117")
bg_color = "#161b22"
text_color = "#e6edf3"
dim_color = "#8b949e"
def _draw_episode_card(ax, stats, title, is_good):
ax.set_facecolor(bg_color)
ax.set_xlim(0, 10)
ax.set_ylim(0, 10)
ax.axis("off")
# Title bar
border_color = "#22c55e" if is_good else "#ef4444"
title_bg = "#1a3a2a" if is_good else "#3a1a1a"
rect = FancyBboxPatch(
(0.3, 8.5), 9.4, 1.2,
boxstyle="round,pad=0.15",
facecolor=title_bg, edgecolor=border_color, linewidth=2,
)
ax.add_patch(rect)
ax.text(5, 9.1, title, fontsize=16, fontweight="bold",
color=text_color, ha="center", va="center")
# F1 Score (large)
f1 = stats["investigator_f1"]
f1_color = "#22c55e" if f1 > 0.7 else "#f59e0b" if f1 > 0.4 else "#ef4444"
ax.text(5, 7.5, f"F1 Score: {f1:.3f}", fontsize=28, fontweight="bold",
color=f1_color, ha="center", va="center")
# Stats grid
info_lines = [
("Nodes Visited", str(len(stats.get("nodes_visited", [])))),
("Nodes Quarantined", str(stats["num_quarantined"])),
("Steps Taken", str(stats["steps_taken"])),
("Belief Confidence", f"{stats['belief_confidence']:.2f}"),
("Intervention Type", stats["intervention_type"]),
("Correctly Identified", "YES" if stats["intervention_correctly_identified"] else "NO"),
("Quarantine Threshold", f"{stats['quarantine_threshold']:.3f}"),
("Exploration Rate", f"{stats['exploration_rate']:.3f}"),
]
y_pos = 6.2
for label, value in info_lines:
# Label
ax.text(1.0, y_pos, label + ":", fontsize=11, color=dim_color,
ha="left", va="center", fontfamily="monospace")
# Value
v_color = text_color
if label == "Correctly Identified":
v_color = "#22c55e" if value == "YES" else "#ef4444"
ax.text(9.0, y_pos, value, fontsize=12, fontweight="bold",
color=v_color, ha="right", va="center", fontfamily="monospace")
y_pos -= 0.7
# Quarantined nodes list
q_nodes = stats.get("nodes_quarantined_list", [])
if q_nodes:
ax.text(1.0, y_pos - 0.3, "Quarantined:", fontsize=10, color=dim_color,
ha="left", va="center")
node_text = ", ".join(q_nodes[:6])
if len(q_nodes) > 6:
node_text += f" +{len(q_nodes)-6} more"
ax.text(1.0, y_pos - 0.9, node_text, fontsize=9, color="#f59e0b",
ha="left", va="center", fontfamily="monospace")
_draw_episode_card(ax_early, early_stats,
f"Episode {early_stats['episode']} (Early)", is_good=False)
_draw_episode_card(ax_late, late_stats,
f"Episode {late_stats['episode']} (Late)", is_good=True)
# Arrow between cards
fig.text(0.5, 0.5, "→", fontsize=48, color="#8b949e",
ha="center", va="center", fontweight="bold")
fig.suptitle(
"RecallTrace — Before / After Self-Play Training",
fontsize=18, color=text_color, fontweight="bold", y=0.97,
)
fig.text(
0.5, 0.92,
"Investigator behavior change: spray & pray → precision targeting",
ha="center", fontsize=12, color=dim_color,
)
plt.tight_layout(rect=[0, 0, 1, 0.90])
os.makedirs(os.path.dirname(save_path), exist_ok=True)
fig.savefig(save_path, dpi=200, bbox_inches="tight", facecolor=fig.get_facecolor())
plt.close(fig)
print(f" Saved episode comparison to {save_path}")
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