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"""
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Backend Performance Comparison Visualization
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Compares IBM vs Russian backends across languages using mean reward and standard deviation
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"""
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import matplotlib.pyplot as plt
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import numpy as np
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from typing import Dict, List
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def plot_backend_performance_comparison(backend_performance: Dict[str, List[float]],
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output_file: str = 'backend_comparison.png'):
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"""
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Create bar chart comparing backend performance with error bars
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Args:
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backend_performance: Dict mapping backend names to reward lists
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output_file: Output filename for the plot
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"""
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backends = list(backend_performance.keys())
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means = [np.mean(backend_performance[b]) if backend_performance[b] else 0
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for b in backends]
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stds = [np.std(backend_performance[b]) if backend_performance[b] else 0
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for b in backends]
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fig, ax = plt.subplots(figsize=(10, 6))
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x = np.arange(len(backends))
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width = 0.6
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bars = ax.bar(x, means, width, yerr=stds, capsize=10,
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color=['#3498db', '#e74c3c'], alpha=0.8, edgecolor='black')
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ax.set_xlabel('Backend', fontsize=12, fontweight='bold')
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ax.set_ylabel('Mean Reward', fontsize=12, fontweight='bold')
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ax.set_title('Backend Performance Comparison\n(IBM vs Russian)',
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fontsize=14, fontweight='bold')
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ax.set_xticks(x)
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ax.set_xticklabels(backends)
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ax.grid(axis='y', alpha=0.3, linestyle='--')
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ax.set_ylim(0, 1.0)
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for i, (bar, mean, std) in enumerate(zip(bars, means, stds)):
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height = bar.get_height()
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ax.text(bar.get_x() + bar.get_width()/2., height + std + 0.02,
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f'{mean:.3f}±{std:.3f}',
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ha='center', va='bottom', fontweight='bold')
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plt.tight_layout()
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plt.savefig(output_file, dpi=300, bbox_inches='tight')
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print(f"✓ Backend comparison saved to {output_file}")
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plt.close()
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def plot_backend_performance_by_language(learned_heuristics: Dict[str, Dict],
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backend_performance: Dict[str, List[float]],
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output_file: str = 'backend_by_language.png'):
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"""
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Create grouped bar chart showing backend performance per language
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Args:
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learned_heuristics: Dict mapping languages to heuristic info
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backend_performance: Dict mapping backend names to reward lists
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output_file: Output filename for the plot
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"""
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languages = list(learned_heuristics.keys())
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backends = list(backend_performance.keys())
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data = {backend: [] for backend in backends}
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for lang in languages:
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preferred = learned_heuristics[lang]['preferred_backend']
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avg_reward = learned_heuristics[lang]['avg_reward']
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data[preferred].append(avg_reward)
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fig, ax = plt.subplots(figsize=(12, 6))
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x = np.arange(len(languages))
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width = 0.35
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for i, backend in enumerate(backends):
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rewards = [learned_heuristics[lang]['avg_reward']
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if learned_heuristics[lang]['preferred_backend'] == backend
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else 0 for lang in languages]
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offset = width * (i - len(backends)/2 + 0.5)
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ax.bar(x + offset, rewards, width, label=backend, alpha=0.8)
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ax.set_xlabel('Language', fontsize=12, fontweight='bold')
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ax.set_ylabel('Average Reward', fontsize=12, fontweight='bold')
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ax.set_title('Backend Performance by Language', fontsize=14, fontweight='bold')
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ax.set_xticks(x)
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ax.set_xticklabels(languages)
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ax.legend()
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ax.grid(axis='y', alpha=0.3, linestyle='--')
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ax.set_ylim(0, 1.0)
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plt.tight_layout()
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plt.savefig(output_file, dpi=300, bbox_inches='tight')
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print(f"✓ Backend by language saved to {output_file}")
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plt.close()
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if __name__ == '__main__':
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backend_performance = {
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'ibm': [0.807, 0.785, 0.820, 0.795, 0.830],
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'russian': [0.825, 0.810, 0.840, 0.815, 0.835]
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}
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learned_heuristics = {
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'ru': {'preferred_backend': 'ibm', 'avg_reward': 0.807},
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'zh': {'preferred_backend': 'russian', 'avg_reward': 0.814},
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'es': {'preferred_backend': 'russian', 'avg_reward': 0.853},
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'fr': {'preferred_backend': 'russian', 'avg_reward': 0.842},
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'en': {'preferred_backend': 'russian', 'avg_reward': 0.803}
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}
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plot_backend_performance_comparison(backend_performance)
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plot_backend_performance_by_language(learned_heuristics, backend_performance)
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