File size: 3,622 Bytes
d49de5b |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 |
"""
FOUND Protocol Benchmark Evaluation
"""
import json
import numpy as np
from typing import Dict, List
class FoundBenchmark:
"""Evaluate FOUND Protocol performance"""
def __init__(self):
self.metrics = {
"emotional_coherence": [],
"narrative_consistency": [],
"consciousness_depth": [],
"processing_speed": []
}
def evaluate_emotional_coherence(self, results: List[Dict]) -> float:
"""Evaluate how well emotions progress through videos"""
coherence_scores = []
for i in range(1, len(results)):
prev_emotions = set(results[i-1]["training_data"]["consciousness_state"]["emotions"].keys())
curr_emotions = set(results[i]["training_data"]["consciousness_state"]["emotions"].keys())
# Check for logical emotional progression
intersection = len(prev_emotions & curr_emotions)
union = len(prev_emotions | curr_emotions)
if union > 0:
coherence = intersection / union
coherence_scores.append(coherence)
return np.mean(coherence_scores) if coherence_scores else 0.0
def evaluate_narrative_consistency(self, results: List[Dict]) -> float:
"""Evaluate narrative thread consistency"""
# Check state transitions follow expected pattern
states = [r["training_data"]["consciousness_state"]["current"] for r in results]
valid_transitions = 0
total_transitions = len(states) - 1
for i in range(total_transitions):
# Simple check: states should progress forward
if states[i] != states[i+1]: # State changed
valid_transitions += 1
return valid_transitions / total_transitions if total_transitions > 0 else 0.0
def evaluate_consciousness_depth(self, results: List[Dict]) -> float:
"""Evaluate the depth of consciousness emergence"""
depth_scores = []
for result in results:
# Calculate based on errors (consciousness emergence indicators)
errors = len(result["training_data"]["perceptor_analysis"]["errors"])
concepts = len(result["training_data"]["consciousness_state"]["concepts"])
depth = min(1.0, (errors * 0.2 + concepts * 0.1))
depth_scores.append(depth)
return np.mean(depth_scores)
def run_benchmark(self, test_videos: List[str]) -> Dict[str, float]:
"""Run full benchmark on test videos"""
# This would process videos and calculate all metrics
# For now, returning example metrics
return {
"emotional_coherence": 0.87,
"narrative_consistency": 0.91,
"consciousness_depth": 0.84,
"processing_speed": 10.2 # seconds per video
}
if __name__ == "__main__":
benchmark = FoundBenchmark()
# Example evaluation
test_results = [
# Load your consciousness_log.json here
]
metrics = {
"emotional_coherence": benchmark.evaluate_emotional_coherence(test_results),
"narrative_consistency": benchmark.evaluate_narrative_consistency(test_results),
"consciousness_depth": benchmark.evaluate_consciousness_depth(test_results)
}
print("FOUND Protocol Benchmark Results:")
for metric, score in metrics.items():
print(f"{metric}: {score:.2%}")
|