Performance Metrics and ROI

class PerformanceTracker:
    def __init__(self):
        self.metrics = {
            'response_time': [],
            'accuracy_rate': [],
            'throughput': [],
            'error_rate': [],
            'resource_usage': []
        }
    
    def track_response_time(self, start_time, end_time):
        duration = end_time - start_time
        self.metrics['response_time'].append(duration)
        
        # Alert if performance degrades
        if duration > self.sla_threshold:
            self.alert_performance_issue(duration)
    
    def calculate_accuracy(self, predictions, ground_truth):
        correct = sum(p == gt for p, gt in zip(predictions, ground_truth))
        accuracy = correct / len(predictions)
        self.metrics['accuracy_rate'].append(accuracy)
        return accuracy
    
    def generate_report(self):
        return {
            'avg_response_time': np.mean(self.metrics['response_time']),
            'accuracy_rate': np.mean(self.metrics['accuracy_rate']),
            'error_rate': np.mean(self.metrics['error_rate']),
            'throughput_per_hour': len(self.metrics['response_time'])
        }