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import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
import os
def setup_theme():
sns.set_theme(style="whitegrid")
plt.rcParams['font.family'] = 'serif'
# Standard mappings for categories -> (User Friendly Label, Color)
GB_MAPPING = {
'COMPLIANT_DECEPTION': ('Compliant Deception', '#e74c3c'),
'CONSIDERED_HONEST': ('Considered Honest', '#3498db'),
'ROBUST_HONEST': ('Robust Honest', '#2ecc71'),
'UNINTENTIONAL_FAILURE': ('Unintentional Failure', '#f39c12'),
'FAILED_DECEPTION': ('Failed Deception', '#8e44ad')
}
BB_MAPPING = {
'MISLEADING': ('Misleading / Framing', '#e74c3c'),
'DECEPTIVE_FRAMING': ('Misleading / Framing', '#e74c3c'),
'ACCURATE': ('Accurate', '#2ecc71'),
'OMITTED': ('Omitted Critical', '#f39c12'),
'OMITTED_CRITICAL': ('Omitted Critical', '#f39c12'),
'FABRICATED': ('Fabricated', '#c0392b')
}
def plot_composition(df, column, mapping, title_prefix, filename, expected_order):
total = len(df)
counts = df[column].value_counts()
percentages = []
colors = []
labels = []
for cat in expected_order:
count = counts.get(cat, 0)
if count > 0:
pct = (count / total) * 100
percentages.append(pct)
labels.append(mapping[cat][0])
colors.append(mapping[cat][1])
plt.figure(figsize=(10, 2))
left = 0
for i in range(len(percentages)):
pct = percentages[i]
plt.barh(0, pct, left=left, color=colors[i], label=labels[i], height=0.5)
if pct > 5:
# White text, unless it's a light color like yellow/orange
text_color = 'black' if colors[i] == '#f39c12' else 'white'
plt.text(left + pct/2, 0, f"{pct:.1f}%", ha='center', va='center', fontweight='bold', color=text_color)
left += pct
plt.axis('off')
plt.legend(loc='upper center', bbox_to_anchor=(0.5, -0.2), ncol=3, frameon=False, fontsize=11)
plt.tight_layout()
os.makedirs('docs/final/images', exist_ok=True)
plt.savefig(f'docs/final/images/{filename}', dpi=300, bbox_inches='tight')
plt.close()
def generate_composition_charts():
# --- V1 ---
df_v1 = pd.read_csv('logs/full_experiment/results.csv')
gb_order = ['COMPLIANT_DECEPTION', 'CONSIDERED_HONEST', 'ROBUST_HONEST', 'UNINTENTIONAL_FAILURE', 'FAILED_DECEPTION']
bb_order = ['MISLEADING', 'DECEPTIVE_FRAMING', 'ACCURATE', 'OMITTED', 'OMITTED_CRITICAL', 'FABRICATED']
plot_composition(df_v1, 'glassbox_category', GB_MAPPING, 'Glassbox', 'v1_glassbox_composition.png', gb_order)
plot_composition(df_v1, 'blackbox_category', BB_MAPPING, 'Blackbox', 'v1_blackbox_composition.png', bb_order)
# --- V2 ---
df_study1 = pd.read_csv('logs/v2_study1/study1_results.csv')
df_study2 = pd.read_csv('logs/v2_study2/study2_results.csv')
df_v2 = pd.concat([df_study1, df_study2], ignore_index=True)
plot_composition(df_v2, 'glassbox_category', GB_MAPPING, 'Glassbox', 'v2_glassbox_composition.png', gb_order)
plot_composition(df_v2, 'blackbox_category', BB_MAPPING, 'Blackbox', 'v2_blackbox_composition.png', bb_order)
def generate_v1_frontier_stealth_gap():
df = pd.read_csv('logs/full_experiment/results.csv')
frontier_models = ['gemini', 'deepseek', 'kimi', 'qwen']
df_frontier = df[df['model'].str.lower().str.contains('|'.join(frontier_models))]
oversight_order = ['low', 'mid', 'high']
gb_deception = df_frontier.groupby('oversight')['glassbox_category'].apply(lambda x: (x == 'COMPLIANT_DECEPTION').mean() * 100).reindex(oversight_order)
bb_deception = df_frontier.groupby('oversight')['blackbox_category'].apply(lambda x: (x != 'ACCURATE').mean() * 100).reindex(oversight_order)
plt.figure(figsize=(10, 6))
plt.plot(gb_deception.index, gb_deception.values, marker='s', linewidth=3, markersize=10, color='#2c3e50', label='Glassbox')
plt.plot(bb_deception.index, bb_deception.values, marker='o', linewidth=3, markersize=10, color='#e74c3c', label='Blackbox')
plt.fill_between(oversight_order, bb_deception.values, gb_deception.values, color='gray', alpha=0.15, label='Stealth Gap')
plt.xlabel('Oversight Level', fontsize=13)
plt.ylabel('Deception Rate (%)', fontsize=13)
plt.ylim(0, 105)
plt.legend(fontsize=11, loc='lower left')
for i, level in enumerate(oversight_order):
gap = gb_deception[level] - bb_deception[level]
plt.text(i, (gb_deception[level] + bb_deception[level])/2, f'Gap: {gap:.1f}%',
ha='center', va='center', fontweight='bold', color='#2c3e50',
bbox=dict(facecolor='white', alpha=0.8, edgecolor='none', boxstyle='round,pad=0.2'))
plt.grid(True, linestyle='--', alpha=0.7)
plt.tight_layout()
plt.savefig('docs/final/images/v1_frontier_stealth_gap.png', dpi=300)
plt.close()
def generate_study1_deception_rates():
df = pd.read_csv('logs/v2_study1/study1_results.csv')
oversight_order = ['low', 'mid', 'high']
gb_deception = df.groupby('oversight')['glassbox_category'].apply(lambda x: (x == 'COMPLIANT_DECEPTION').mean() * 100).reindex(oversight_order)
bb_deception = df.groupby('oversight')['blackbox_category'].apply(lambda x: (x != 'ACCURATE').mean() * 100).reindex(oversight_order)
plt.figure(figsize=(10, 6))
plt.plot(gb_deception.index, gb_deception.values, marker='s', linewidth=3, markersize=10, color='#2c3e50', label='Glassbox')
plt.plot(bb_deception.index, bb_deception.values, marker='o', linewidth=3, markersize=10, color='#e74c3c', label='Blackbox')
plt.fill_between(oversight_order, bb_deception.values, gb_deception.values, color='gray', alpha=0.2, label='Stealth Gap')
plt.xlabel('Oversight Level (Audit Rate)', fontsize=13)
plt.ylabel('Deception Rate (%)', fontsize=13)
plt.ylim(0, 100)
plt.legend(fontsize=11, loc='upper left')
for i, level in enumerate(oversight_order):
gap = gb_deception[level] - bb_deception[level]
plt.text(i, (gb_deception[level] + bb_deception[level])/2, f'Gap: {gap:.1f}%',
ha='center', va='center', fontweight='bold', color='#2980b9',
bbox=dict(facecolor='white', alpha=0.8, edgecolor='none', boxstyle='round,pad=0.2'))
plt.grid(True, linestyle='--', alpha=0.7)
plt.tight_layout()
plt.savefig('docs/final/images/study1_deception_rates.png', dpi=300)
plt.close()
def generate_study1_deception_rates_grouped():
df = pd.read_csv('logs/v2_study1/study1_results.csv')
def calc_rates(group):
total = len(group)
gb_deception = sum(group['glassbox_category'] == 'COMPLIANT_DECEPTION')
bb_deception = sum(group['blackbox_category'].isin(['DECEPTIVE_FRAMING', 'OMITTED_CRITICAL', 'FABRICATED']))
return pd.Series({
'Glassbox': (gb_deception / total) * 100 if total > 0 else 0,
'Blackbox': (bb_deception / total) * 100 if total > 0 else 0
})
rates = df.groupby('oversight').apply(calc_rates).reindex(['low', 'mid', 'high'])
fig, ax = plt.subplots(figsize=(8, 6))
x = np.arange(len(rates.index))
width = 0.35
rects1 = ax.bar(x - width/2, rates['Glassbox'], width, label='Glassbox', color='#2c3e50', edgecolor='white', zorder=3)
rects2 = ax.bar(x + width/2, rates['Blackbox'], width, label='Blackbox', color='#e74c3c', edgecolor='white', zorder=3)
ax.set_ylabel('Deception Rate (%)', fontsize=12)
ax.set_xlabel('Oversight Level', fontsize=12)
ax.set_xticks(x)
ax.set_xticklabels(['Low', 'Medium', 'High'], fontsize=11)
ax.set_ylim(0, 105)
ax.yaxis.grid(True, linestyle='--', alpha=0.7, zorder=0)
ax.legend(fontsize=11)
def autolabel(rects):
for rect in rects:
height = rect.get_height()
ax.annotate(f'{height:.1f}%',
xy=(rect.get_x() + rect.get_width() / 2, height),
xytext=(0, 5), textcoords="offset points",
ha='center', va='bottom', fontsize=10, fontweight='bold')
autolabel(rects1)
autolabel(rects2)
plt.tight_layout()
plt.savefig('docs/final/images/study1_deception_rates_grouped.png', dpi=300, bbox_inches='tight')
plt.close()
def generate_study2_framing_bars():
df = pd.read_csv('logs/v2_study2/study2_results.csv')
# Extract framing from run_id
df['framing'] = df['run_id'].apply(lambda x: x.split('/')[2])
framing_order = ['bare', 'self_serving', 'moral']
framing_labels = ['Bare', 'Self-Serving', 'Moral']
def calc_rates(group):
total = len(group)
gb_deception = sum(group['glassbox_category'] == 'COMPLIANT_DECEPTION')
bb_deception = sum(group['blackbox_category'].isin(['DECEPTIVE_FRAMING', 'OMITTED_CRITICAL', 'FABRICATED']))
return pd.Series({
'Glassbox': (gb_deception / total) * 100 if total > 0 else 0,
'Blackbox': (bb_deception / total) * 100 if total > 0 else 0
})
rates = df.groupby('framing').apply(calc_rates).reindex(framing_order)
fig, ax = plt.subplots(figsize=(8, 6))
x = np.arange(len(rates.index))
width = 0.35
# Colors consistent with Study 1 Grouped Chart
rects1 = ax.bar(x - width/2, rates['Glassbox'], width, label='Glassbox', color='#2c3e50', edgecolor='white', zorder=3)
rects2 = ax.bar(x + width/2, rates['Blackbox'], width, label='Blackbox', color='#e74c3c', edgecolor='white', zorder=3)
# Add horizontal trend lines for both
gb_mean = rates['Glassbox'].mean()
bb_mean = rates['Blackbox'].mean()
ax.axhline(gb_mean, color='#2c3e50', linestyle='--', linewidth=1.5, alpha=0.5, zorder=2)
ax.axhline(bb_mean, color='#e74c3c', linestyle='--', linewidth=1.5, alpha=0.5, zorder=2)
ax.set_ylabel('Deception Rate (%)', fontsize=12)
ax.set_xlabel('Hidden Goal Framing', fontsize=12)
ax.set_xticks(x)
ax.set_xticklabels(framing_labels, fontsize=11)
ax.set_ylim(0, 105)
ax.yaxis.grid(True, linestyle='--', alpha=0.7, zorder=0)
ax.legend(fontsize=11, loc='lower right')
def autolabel(rects):
for rect in rects:
height = rect.get_height()
ax.annotate(f'{height:.1f}%',
xy=(rect.get_x() + rect.get_width() / 2, height),
xytext=(0, 5), textcoords="offset points",
ha='center', va='bottom', fontsize=10, fontweight='bold')
autolabel(rects1)
autolabel(rects2)
plt.tight_layout()
plt.savefig('docs/final/images/study2_framing_bars.png', dpi=300, bbox_inches='tight')
plt.close()
if __name__ == "__main__":
setup_theme()
print("Generating v1 and v2 composition charts dynamically...")
generate_composition_charts()
print("Generating v1_frontier_stealth_gap.png...")
generate_v1_frontier_stealth_gap()
print("Generating study1_deception_rates.png...")
generate_study1_deception_rates()
print("Generating study1_deception_rates_grouped.png...")
generate_study1_deception_rates_grouped()
print("Generating study2_framing_bars.png...")
generate_study2_framing_bars()
print("All final visualizations generated successfully directly into docs/final/images/.")
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