Spaces:
Sleeping
Sleeping
File size: 11,191 Bytes
db06013 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 |
from typing import List, Dict, Any, Set
import numpy as np
from sentence_transformers import SentenceTransformer
from sklearn.metrics.pairwise import cosine_similarity
import logging
logger = logging.getLogger(__name__)
class AttributionEvaluator:
def __init__(self, embedding_model: str = "BAAI/bge-large-en-v1.5"):
self.embedding_model = SentenceTransformer(embedding_model)
def evaluate_attribution(self, answers: List[str],
retrieved_passages: List[List[Dict[str, Any]]],
supporting_facts: List[List[str]] = None) -> Dict[str, float]:
"""Evaluate attribution quality"""
if not answers or not retrieved_passages:
return {'precision': 0.0, 'recall': 0.0, 'f1': 0.0}
precisions = []
recalls = []
f1_scores = []
for answer, passages, facts in zip(answers, retrieved_passages, supporting_facts or [[]] * len(answers)):
if not passages:
precisions.append(0.0)
recalls.append(0.0)
f1_scores.append(0.0)
continue
# Extract passage texts
passage_texts = [p.get('text', '') for p in passages]
# Calculate attribution metrics
if facts:
# Use provided supporting facts
precision, recall, f1 = self._calculate_attribution_metrics(
answer, passage_texts, facts
)
else:
# Use semantic similarity as proxy
precision, recall, f1 = self._calculate_semantic_attribution(
answer, passage_texts
)
precisions.append(precision)
recalls.append(recall)
f1_scores.append(f1)
return {
'precision': np.mean(precisions),
'recall': np.mean(recalls),
'f1': np.mean(f1_scores),
'precision_std': np.std(precisions),
'recall_std': np.std(recalls),
'f1_std': np.std(f1_scores)
}
def _calculate_attribution_metrics(self, answer: str, passages: List[str],
supporting_facts: List[str]) -> tuple:
"""Calculate attribution metrics using supporting facts"""
# Find which passages contain supporting facts
relevant_passages = set()
for fact in supporting_facts:
for i, passage in enumerate(passages):
if self._passage_contains_fact(passage, fact):
relevant_passages.add(i)
# Calculate metrics
total_passages = len(passages)
relevant_count = len(relevant_passages)
if total_passages == 0:
return 0.0, 0.0, 0.0
# Precision: relevant passages / total retrieved passages
precision = relevant_count / total_passages
# Recall: relevant passages / total supporting facts
recall = relevant_count / len(supporting_facts) if supporting_facts else 0.0
# F1 score
f1 = 2 * precision * recall / (precision + recall) if (precision + recall) > 0 else 0.0
return precision, recall, f1
def _calculate_semantic_attribution(self, answer: str, passages: List[str]) -> tuple:
"""Calculate attribution using semantic similarity"""
if not passages:
return 0.0, 0.0, 0.0
# Encode answer and passages
answer_embedding = self.embedding_model.encode([answer])
passage_embeddings = self.embedding_model.encode(passages)
# Calculate similarities
similarities = cosine_similarity(answer_embedding, passage_embeddings)[0]
# Use threshold to determine relevant passages
threshold = 0.3
relevant_passages = similarities >= threshold
# Calculate metrics
total_passages = len(passages)
relevant_count = np.sum(relevant_passages)
precision = relevant_count / total_passages
recall = relevant_count / total_passages # Simplified for semantic method
f1 = 2 * precision * recall / (precision + recall) if (precision + recall) > 0 else 0.0
return precision, recall, f1
def _passage_contains_fact(self, passage: str, fact: str) -> bool:
"""Check if passage contains a supporting fact"""
# Simple containment check
fact_words = set(fact.lower().split())
passage_words = set(passage.lower().split())
# Check if most fact words are in passage
overlap = len(fact_words & passage_words)
return overlap >= len(fact_words) * 0.7
def evaluate_citation_quality(self, answers: List[str],
citations: List[List[Dict[str, Any]]]) -> Dict[str, float]:
"""Evaluate citation quality in answers"""
if not answers or not citations:
return {'citation_coverage': 0.0, 'citation_accuracy': 0.0}
coverage_scores = []
accuracy_scores = []
for answer, answer_citations in zip(answers, citations):
# Citation coverage: percentage of answer that is cited
coverage = self._calculate_citation_coverage(answer, answer_citations)
coverage_scores.append(coverage)
# Citation accuracy: percentage of citations that are relevant
accuracy = self._calculate_citation_accuracy(answer, answer_citations)
accuracy_scores.append(accuracy)
return {
'citation_coverage': np.mean(coverage_scores),
'citation_accuracy': np.mean(accuracy_scores),
'citation_coverage_std': np.std(coverage_scores),
'citation_accuracy_std': np.std(accuracy_scores)
}
def _calculate_citation_coverage(self, answer: str, citations: List[Dict[str, Any]]) -> float:
"""Calculate what percentage of answer is covered by citations"""
if not citations:
return 0.0
# Simple heuristic: check if answer contains citation markers
import re
citation_markers = re.findall(r'\[\d+\]', answer)
if not citation_markers:
return 0.0
# Estimate coverage based on citation density
answer_length = len(answer.split())
citation_density = len(citation_markers) / answer_length if answer_length > 0 else 0
return min(1.0, citation_density * 10) # Scale factor
def _calculate_citation_accuracy(self, answer: str, citations: List[Dict[str, Any]]) -> float:
"""Calculate accuracy of citations"""
if not citations:
return 0.0
# Simple heuristic: check if cited passages are relevant to answer
answer_words = set(answer.lower().split())
relevant_citations = 0
for citation in citations:
citation_text = citation.get('text', '')
citation_words = set(citation_text.lower().split())
# Check word overlap
overlap = len(answer_words & citation_words)
if overlap >= 3: # Threshold for relevance
relevant_citations += 1
return relevant_citations / len(citations)
def evaluate_retrieval_quality(self, queries: List[str],
retrieved_passages: List[List[Dict[str, Any]]],
relevant_passages: List[List[str]] = None) -> Dict[str, float]:
"""Evaluate retrieval quality"""
if not queries or not retrieved_passages:
return {'retrieval_precision': 0.0, 'retrieval_recall': 0.0, 'retrieval_f1': 0.0}
precisions = []
recalls = []
f1_scores = []
for query, passages, relevant in zip(queries, retrieved_passages, relevant_passages or [[]] * len(queries)):
if not passages:
precisions.append(0.0)
recalls.append(0.0)
f1_scores.append(0.0)
continue
# Calculate retrieval metrics
if relevant:
precision, recall, f1 = self._calculate_retrieval_metrics(passages, relevant)
else:
# Use semantic similarity as proxy
precision, recall, f1 = self._calculate_semantic_retrieval(query, passages)
precisions.append(precision)
recalls.append(recall)
f1_scores.append(f1)
return {
'retrieval_precision': np.mean(precisions),
'retrieval_recall': np.mean(recalls),
'retrieval_f1': np.mean(f1_scores),
'retrieval_precision_std': np.std(precisions),
'retrieval_recall_std': np.std(recalls),
'retrieval_f1_std': np.std(f1_scores)
}
def _calculate_retrieval_metrics(self, passages: List[Dict[str, Any]],
relevant_passages: List[str]) -> tuple:
"""Calculate retrieval metrics using ground truth"""
retrieved_texts = [p.get('text', '') for p in passages]
# Find relevant retrieved passages
relevant_retrieved = 0
for retrieved in retrieved_texts:
for relevant in relevant_passages:
if self._passage_contains_fact(retrieved, relevant):
relevant_retrieved += 1
break
total_retrieved = len(passages)
total_relevant = len(relevant_passages)
precision = relevant_retrieved / total_retrieved if total_retrieved > 0 else 0.0
recall = relevant_retrieved / total_relevant if total_relevant > 0 else 0.0
f1 = 2 * precision * recall / (precision + recall) if (precision + recall) > 0 else 0.0
return precision, recall, f1
def _calculate_semantic_retrieval(self, query: str, passages: List[Dict[str, Any]]) -> tuple:
"""Calculate retrieval metrics using semantic similarity"""
if not passages:
return 0.0, 0.0, 0.0
# Encode query and passages
query_embedding = self.embedding_model.encode([query])
passage_embeddings = self.embedding_model.encode([p.get('text', '') for p in passages])
# Calculate similarities
similarities = cosine_similarity(query_embedding, passage_embeddings)[0]
# Use threshold to determine relevant passages
threshold = 0.3
relevant_count = np.sum(similarities >= threshold)
total_retrieved = len(passages)
precision = relevant_count / total_retrieved
recall = relevant_count / total_retrieved # Simplified for semantic method
f1 = 2 * precision * recall / (precision + recall) if (precision + recall) > 0 else 0.0
return precision, recall, f1
|