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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Demo: All Quantum-Scaling RL Visualizations
Demonstrates all four visualization modules with sample data
"""
import sys
sys.path.append('..')
import numpy as np
from Backend_Performance_Comparison import (
plot_backend_performance_comparison,
plot_backend_performance_by_language
)
from Reward_vs_BatchSize_Scaling import (
plot_reward_vs_batch_size,
plot_scaling_law_validation,
plot_compute_efficiency_heatmap
)
from Cross_Lingual_Backend_Preference import (
plot_backend_preference_pie,
plot_language_backend_matrix,
plot_backend_preference_bars
)
from Performance_Trend_Over_Edit_Cycles import (
plot_performance_trend,
plot_backend_usage_over_time,
plot_learning_curve_with_retraining
)
def generate_sample_data():
"""Generate realistic sample data for all visualizations"""
np.random.seed(42)
# Backend performance data
backend_performance = {
'ibm': [0.807, 0.785, 0.820, 0.795, 0.830],
'russian': [0.825, 0.810, 0.840, 0.815, 0.835, 0.820, 0.845, 0.830, 0.825, 0.838]
}
# Learned heuristics
learned_heuristics = {
'ru': {'preferred_backend': 'ibm', 'avg_reward': 0.807, 'edit_count': 5},
'zh': {'preferred_backend': 'russian', 'avg_reward': 0.814, 'edit_count': 4},
'es': {'preferred_backend': 'russian', 'avg_reward': 0.853, 'edit_count': 2},
'fr': {'preferred_backend': 'russian', 'avg_reward': 0.842, 'edit_count': 2},
'en': {'preferred_backend': 'russian', 'avg_reward': 0.803, 'edit_count': 2}
}
# Batch size scaling data
batch_sizes = [4, 6, 8, 10, 12, 14, 16, 18, 20]
model_sizes = [0.5, 1.0, 1.5, 2.0, 2.5, 3.0, 3.5, 4.0, 4.5]
rewards = [0.70 + 0.05 * np.sqrt(b) + 0.02 * np.random.randn()
for b in batch_sizes]
optimal_batch_sizes = [int(8 * np.sqrt(m)) for m in model_sizes]
# Compute efficiency heatmap
efficiencies = np.random.uniform(5, 12, (len(model_sizes), len(batch_sizes)))
# Edit history
edit_history = []
for i in range(30):
base_reward = 0.65 + 0.01 * i + 0.05 * np.random.randn()
performance_delta = base_reward - 0.5
edit_history.append({
'edit_id': f'edit_{i}',
'backend': 'russian' if i > 5 else np.random.choice(['ibm', 'russian']),
'performance_delta': performance_delta,
'reward': base_reward
})
retrain_intervals = [10, 20, 30]
return {
'backend_performance': backend_performance,
'learned_heuristics': learned_heuristics,
'batch_sizes': batch_sizes,
'model_sizes': model_sizes,
'rewards': rewards,
'optimal_batch_sizes': optimal_batch_sizes,
'efficiencies': efficiencies,
'edit_history': edit_history,
'retrain_intervals': retrain_intervals
}
def main():
print("=" * 80)
print("Quantum-Scaling RL Visualization Demo")
print("=" * 80)
print()
# Generate sample data
print("Generating sample data...")
data = generate_sample_data()
print("✓ Sample data generated")
print()
# Module 1: Backend Performance Comparison
print("=" * 80)
print("Module 1: Backend Performance Comparison")
print("=" * 80)
plot_backend_performance_comparison(
data['backend_performance'],
'output/backend_comparison.png'
)
plot_backend_performance_by_language(
data['learned_heuristics'],
data['backend_performance'],
'output/backend_by_language.png'
)
print()
# Module 2: Reward vs Batch Size Scaling
print("=" * 80)
print("Module 2: Reward vs Batch Size Scaling")
print("=" * 80)
plot_reward_vs_batch_size(
data['batch_sizes'],
data['rewards'],
data['model_sizes'],
'output/reward_vs_batch_size.png'
)
plot_scaling_law_validation(
data['model_sizes'],
data['optimal_batch_sizes'],
'output/scaling_law_validation.png'
)
plot_compute_efficiency_heatmap(
data['batch_sizes'],
data['model_sizes'],
data['efficiencies'],
'output/compute_efficiency_heatmap.png'
)
print()
# Module 3: Cross-Lingual Backend Preference
print("=" * 80)
print("Module 3: Cross-Lingual Backend Preference")
print("=" * 80)
plot_backend_preference_pie(
data['learned_heuristics'],
'output/backend_preference_pie.png'
)
plot_language_backend_matrix(
data['learned_heuristics'],
'output/language_backend_matrix.png'
)
plot_backend_preference_bars(
data['learned_heuristics'],
'output/backend_preference_bars.png'
)
print()
# Module 4: Performance Trend Over Edit Cycles
print("=" * 80)
print("Module 4: Performance Trend Over Edit Cycles")
print("=" * 80)
plot_performance_trend(
data['edit_history'],
'output/performance_trend.png'
)
plot_backend_usage_over_time(
data['edit_history'],
'output/backend_usage_trend.png'
)
plot_learning_curve_with_retraining(
data['edit_history'],
data['retrain_intervals'],
'output/learning_curve.png'
)
print()
print("=" * 80)
print("All Visualizations Complete!")
print("=" * 80)
print()
print("Generated 10 visualization files in output/ directory:")
print(" 1. backend_comparison.png")
print(" 2. backend_by_language.png")
print(" 3. reward_vs_batch_size.png")
print(" 4. scaling_law_validation.png")
print(" 5. compute_efficiency_heatmap.png")
print(" 6. backend_preference_pie.png")
print(" 7. language_backend_matrix.png")
print(" 8. backend_preference_bars.png")
print(" 9. performance_trend.png")
print(" 10. backend_usage_trend.png")
print(" 11. learning_curve.png")
if __name__ == '__main__':
import os
os.makedirs('output', exist_ok=True)
main()
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