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3d015cd | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 | """Fusion Engine - Confidence-weighted Score Fusion"""
from typing import Dict, Tuple, Optional
from config import Config
class FusionEngine:
"""Combines scores from all modules with confidence weighting"""
def __init__(self):
# Base weights (when no domain evidence)
self.base_weights = {
'universal': Config.UNIVERSAL_WEIGHT,
'personality': Config.PERSONALITY_WEIGHT,
'text': Config.TEXT_WEIGHT
}
# Extended weights (when domain evidence exists)
self.extended_weights = {
'universal': 0.30, # Reduced from base
'personality': 0.25,
'text': 0.25,
'domain': 0.20 # New domain component
}
def fuse_scores(
self,
universal_score: float,
universal_confidence: float,
personality_score: float,
personality_confidence: float,
text_score: float,
text_confidence: float,
domain_score: Optional[float] = None,
domain_confidence: Optional[float] = None
) -> Tuple[float, Dict]:
"""
Fuse scores with confidence weighting
Supports optional domain score for pluggable domain evidence
Returns: (final_score, breakdown)
"""
# Determine which weights to use
has_domain = domain_score is not None and domain_confidence is not None and domain_confidence > 0
weights = self.extended_weights if has_domain else self.base_weights
# Calculate effective weights (weight * confidence)
effective_weights = {
'universal': weights['universal'] * universal_confidence,
'personality': weights['personality'] * personality_confidence,
'text': weights['text'] * text_confidence
}
# Add domain if available
if has_domain:
effective_weights['domain'] = weights['domain'] * domain_confidence
# Sum of effective weights (for normalization)
total_effective_weight = sum(effective_weights.values())
# Prevent division by zero
if total_effective_weight == 0:
breakdown = {
'final_score': 0.0,
'component_scores': {
'universal': 0.0,
'personality': 0.0,
'text': 0.0
},
'confidences': {
'universal': 0.0,
'personality': 0.0,
'text': 0.0
},
'effective_weights': effective_weights,
'has_domain': False
}
if has_domain:
breakdown['component_scores']['domain'] = 0.0
breakdown['confidences']['domain'] = 0.0
return 0.0, breakdown
# Calculate fused score
fused_score = (
effective_weights['universal'] * universal_score +
effective_weights['personality'] * personality_score +
effective_weights['text'] * text_score
)
if has_domain:
fused_score += effective_weights['domain'] * domain_score
fused_score /= total_effective_weight
# Prepare breakdown
breakdown = {
'final_score': round(fused_score, 4),
'component_scores': {
'universal': round(universal_score, 4),
'personality': round(personality_score, 4),
'text': round(text_score, 4)
},
'confidences': {
'universal': round(universal_confidence, 4),
'personality': round(personality_confidence, 4),
'text': round(text_confidence, 4)
},
'effective_weights': {
k: round(v / total_effective_weight, 4)
for k, v in effective_weights.items()
},
'base_weights': weights,
'has_domain': has_domain
}
# Add domain info if present
if has_domain:
breakdown['component_scores']['domain'] = round(domain_score, 4)
breakdown['confidences']['domain'] = round(domain_confidence, 4)
return fused_score, breakdown
def get_grade(self, final_score: float) -> str:
"""Convert score to letter grade"""
if final_score >= 0.9:
return 'A+'
elif final_score >= 0.85:
return 'A'
elif final_score >= 0.8:
return 'A-'
elif final_score >= 0.75:
return 'B+'
elif final_score >= 0.7:
return 'B'
elif final_score >= 0.65:
return 'B-'
elif final_score >= 0.6:
return 'C+'
elif final_score >= 0.55:
return 'C'
elif final_score >= 0.5:
return 'C-'
else:
return 'D'
def get_percentile(self, final_score: float) -> int:
"""Estimate percentile (mock for MVP)"""
# In production, this would query actual distribution
return min(int(final_score * 100), 99)
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