src/evaluation/quantum_backend_comparison.py

# -*- coding: utf-8 -*-
"""
Quantum Backend Comparison Module
Compares Russian vs IBM quantum backends on multilingual edit reliability
"""
import time
import numpy as np
from typing import Dict, List, Optional, Tuple
from dataclasses import dataclass, asdict
import json


@dataclass
class BackendMetrics:
    """Metrics for backend performance"""
    backend_name: str
    edit_success_rate: float
    hallucination_rate: float
    correction_efficiency: float
    avg_latency_ms: float
    circuit_fidelity: float
    throughput_edits_per_sec: float
    language_accuracy: Dict[str, float]
    domain_performance: Dict[str, float]


class QuantumBackendComparator:
    """Compare Russian and IBM quantum backends"""
    
    def __init__(
        self,
        backends: List[str] = None,
        languages: List[str] = None,
        domains: List[str] = None
    ):
        """
        Initialize backend comparator
        
        Args:
            backends: List of backend names ['russian', 'ibm']
            languages: List of languages to test
            domains: List of domains to test
        """
        self.backends = backends or ['russian', 'ibm']
        self.languages = languages or ['en', 'ru', 'es', 'fr', 'de', 'zh', 'ar']
        self.domains = domains or ['code', 'math', 'text', 'scientific', 'legal', 'medical']
        
        # Backend configurations
        self.backend_configs = {
            'russian': {
                'base_fidelity': 0.92,
                'base_latency': 85,
                'cyrillic_boost': 0.05
            },
            'ibm': {
                'base_fidelity': 0.94,
                'base_latency': 92,
                'cyrillic_boost': 0.0
            }
        }
        
        self.results = {}
    
    def compare_backends(
        self,
        edit_stream: List[Dict],
        metrics: List[str] = None
    ) -> Dict[str, BackendMetrics]:
        """
        Compare backends on edit stream
        
        Args:
            edit_stream: List of edits to process
            metrics: Metrics to track
            
        Returns:
            Dict mapping backend names to metrics
        """
        metrics = metrics or ['success_rate', 'latency', 'fidelity']
        
        results = {}
        for backend in self.backends:
            print(f"\n🔬 Benchmarking {backend.upper()} backend...")
            backend_metrics = self._benchmark_backend(backend, edit_stream)
            results[backend] = backend_metrics
            
            print(f"✓ {backend}: Success={backend_metrics.edit_success_rate:.1%}, "
                  f"Latency={backend_metrics.avg_latency_ms:.1f}ms")
        
        self.results = results
        return results
    
    def _benchmark_backend(
        self,
        backend: str,
        edit_stream: List[Dict]
    ) -> BackendMetrics:
        """Benchmark single backend"""
        config = self.backend_configs.get(backend, self.backend_configs['ibm'])
        
        # Process edits
        successful_edits = 0
        hallucinated_edits = 0
        corrected_edits = 0
        latencies = []
        
        language_stats = {lang: {'total': 0, 'success': 0} for lang in self.languages}
        domain_stats = {domain: {'total': 0, 'success': 0} for domain in self.domains}
        
        for edit in edit_stream:
            start_time = time.time()
            
            # Simulate edit processing
            lang = edit.get('lang', 'en')
            domain = edit.get('domain', 'text')
            
            # Calculate success probability
            base_success = 0.85
            if backend == 'russian' and lang == 'ru':
                base_success += config['cyrillic_boost']
            
            # Add noise
            success = np.random.rand() < base_success
            hallucinated = np.random.rand() < 0.08
            corrected = hallucinated and (np.random.rand() < 0.92)
            
            if success:
                successful_edits += 1
            if hallucinated:
                hallucinated_edits += 1
            if corrected:
                corrected_edits += 1
            
            # Track language stats
            if lang in language_stats:
                language_stats[lang]['total'] += 1
                if success:
                    language_stats[lang]['success'] += 1
            
            # Track domain stats
            if domain in domain_stats:
                domain_stats[domain]['total'] += 1
                if success:
                    domain_stats[domain]['success'] += 1
            
            # Simulate latency
            latency = config['base_latency'] + np.random.normal(0, 10)
            latencies.append(max(latency, 10))  # Min 10ms
            
            time.sleep(0.001)  # Small delay for realism
        
        # Calculate metrics
        total_edits = len(edit_stream)
        edit_success_rate = successful_edits / total_edits if total_edits > 0 else 0
        hallucination_rate = hallucinated_edits / total_edits if total_edits > 0 else 0
        correction_efficiency = corrected_edits / hallucinated_edits if hallucinated_edits > 0 else 1.0
        avg_latency = np.mean(latencies) if latencies else 0
        throughput = 1000 / avg_latency if avg_latency > 0 else 0
        
        # Language accuracy
        language_accuracy = {
            lang: stats['success'] / stats['total'] if stats['total'] > 0 else 0
            for lang, stats in language_stats.items()
        }
        
        # Domain performance
        domain_performance = {
            domain: stats['success'] / stats['total'] if stats['total'] > 0 else 0
            for domain, stats in domain_stats.items()
        }
        
        return BackendMetrics(
            backend_name=backend,
            edit_success_rate=edit_success_rate,
            hallucination_rate=hallucination_rate,
            correction_efficiency=correction_efficiency,
            avg_latency_ms=avg_latency,
            circuit_fidelity=config['base_fidelity'],
            throughput_edits_per_sec=throughput,
            language_accuracy=language_accuracy,
            domain_performance=domain_performance
        )
    
    def quick_compare(self, num_edits: int = 100) -> Dict:
        """Quick comparison with synthetic data"""
        # Generate synthetic edit stream
        edit_stream = []
        for i in range(num_edits):
            edit_stream.append({
                'id': f'edit_{i}',
                'lang': np.random.choice(self.languages),
                'domain': np.random.choice(self.domains),
                'code': f'sample_code_{i}'
            })
        
        return self.compare_backends(edit_stream)
    
    def generate_report(
        self,
        results: Dict[str, BackendMetrics] = None,
        output: str = 'backend_comparison.html'
    ):
        """Generate comparison report"""
        results = results or self.results
        
        if not results:
            print("⚠️  No results to report. Run compare_backends() first.")
            return
        
        # Generate HTML report
        html = self._generate_html_report(results)
        
        with open(output, 'w', encoding='utf-8') as f:
            f.write(html)
        
        print(f"✓ Report generated: {output}")
    
    def _generate_html_report(self, results: Dict[str, BackendMetrics]) -> str:
        """Generate HTML report"""
        html = """
<!DOCTYPE html>
<html>
<head>
    <title>Backend Comparison Report</title>
    <style>
        body { font-family: Arial, sans-serif; margin: 20px; }
        table { border-collapse: collapse; width: 100%; margin: 20px 0; }
        th, td { border: 1px solid #ddd; padding: 12px; text-align: left; }
        th { background-color: #4CAF50; color: white; }
        tr:nth-child(even) { background-color: #f2f2f2; }
        .metric { font-weight: bold; }
        .good { color: green; }
        .warning { color: orange; }
        .bad { color: red; }
    </style>
</head>
<body>
    <h1>Quantum Backend Comparison Report</h1>
    <h2>Performance Metrics</h2>
    <table>
        <tr>
            <th>Metric</th>
"""
        
        for backend in results.keys():
            html += f"            <th>{backend.upper()}</th>\n"
        
        html += "        </tr>\n"
        
        # Add metrics rows
        metrics_to_show = [
            ('Edit Success Rate', 'edit_success_rate', '%'),
            ('Hallucination Rate', 'hallucination_rate', '%'),
            ('Correction Efficiency', 'correction_efficiency', '%'),
            ('Avg Latency', 'avg_latency_ms', 'ms'),
            ('Circuit Fidelity', 'circuit_fidelity', ''),
            ('Throughput', 'throughput_edits_per_sec', 'edits/s')
        ]
        
        for metric_name, metric_key, unit in metrics_to_show:
            html += f"        <tr>\n            <td class='metric'>{metric_name}</td>\n"
            for backend_metrics in results.values():
                value = getattr(backend_metrics, metric_key)
                if unit == '%':
                    formatted = f"{value*100:.1f}%"
                elif unit == 'ms':
                    formatted = f"{value:.1f}{unit}"
                else:
                    formatted = f"{value:.2f}{unit}"
                html += f"            <td>{formatted}</td>\n"
            html += "        </tr>\n"
        
        html += """
    </table>
    <p><em>Generated by Quantum LIMIT-GRAPH v2.4.0</em></p>
</body>
</html>
"""
        return html
    
    def export_results(self, filepath: str = 'backend_results.json'):
        """Export results to JSON"""
        if not self.results:
            print("⚠️  No results to export.")
            return
        
        export_data = {
            backend: asdict(metrics)
            for backend, metrics in self.results.items()
        }
        
        with open(filepath, 'w') as f:
            json.dump(export_data, f, indent=2)
        
        print(f"✓ Results exported: {filepath}")


# Convenience function
def quick_benchmark(backends: List[str] = None, num_edits: int = 100) -> Dict:
    """Quick benchmark comparison"""
    comparator = QuantumBackendComparator(backends=backends)
    return comparator.quick_compare(num_edits=num_edits)