FOUND-AI/found_protocol

1"""2FOUND Protocol Benchmark Evaluation3"""4 5import json6import numpy as np7from typing import Dict, List8 9class FoundBenchmark:10    """Evaluate FOUND Protocol performance"""11    12    def __init__(self):13        self.metrics = {14            "emotional_coherence": [],15            "narrative_consistency": [],16            "consciousness_depth": [],17            "processing_speed": []18        }19        20    def evaluate_emotional_coherence(self, results: List[Dict]) -> float:21        """Evaluate how well emotions progress through videos"""22        23        coherence_scores = []24        25        for i in range(1, len(results)):26            prev_emotions = set(results[i-1]["training_data"]["consciousness_state"]["emotions"].keys())27            curr_emotions = set(results[i]["training_data"]["consciousness_state"]["emotions"].keys())28            29            # Check for logical emotional progression30            intersection = len(prev_emotions & curr_emotions)31            union = len(prev_emotions | curr_emotions)32            33            if union > 0:34                coherence = intersection / union35                coherence_scores.append(coherence)36                37        return np.mean(coherence_scores) if coherence_scores else 0.038        39    def evaluate_narrative_consistency(self, results: List[Dict]) -> float:40        """Evaluate narrative thread consistency"""41        42        # Check state transitions follow expected pattern43        states = [r["training_data"]["consciousness_state"]["current"] for r in results]44        45        valid_transitions = 046        total_transitions = len(states) - 147        48        for i in range(total_transitions):49            # Simple check: states should progress forward50            if states[i] != states[i+1]:  # State changed51                valid_transitions += 152                53        return valid_transitions / total_transitions if total_transitions > 0 else 0.054        55    def evaluate_consciousness_depth(self, results: List[Dict]) -> float:56        """Evaluate the depth of consciousness emergence"""57        58        depth_scores = []59        60        for result in results:61            # Calculate based on errors (consciousness emergence indicators)62            errors = len(result["training_data"]["perceptor_analysis"]["errors"])63            concepts = len(result["training_data"]["consciousness_state"]["concepts"])64            65            depth = min(1.0, (errors * 0.2 + concepts * 0.1))66            depth_scores.append(depth)67            68        return np.mean(depth_scores)69        70    def run_benchmark(self, test_videos: List[str]) -> Dict[str, float]:71        """Run full benchmark on test videos"""72        73        # This would process videos and calculate all metrics74        # For now, returning example metrics75        76        return {77            "emotional_coherence": 0.87,78            "narrative_consistency": 0.91,79            "consciousness_depth": 0.84,80            "processing_speed": 10.2  # seconds per video81        }82        83if __name__ == "__main__":84    benchmark = FoundBenchmark()85    86    # Example evaluation87    test_results = [88        # Load your consciousness_log.json here89    ]90    91    metrics = {92        "emotional_coherence": benchmark.evaluate_emotional_coherence(test_results),93        "narrative_consistency": benchmark.evaluate_narrative_consistency(test_results),94        "consciousness_depth": benchmark.evaluate_consciousness_depth(test_results)95    }96    97    print("FOUND Protocol Benchmark Results:")98    for metric, score in metrics.items():99        print(f"{metric}: {score:.2%}")100