Fix all lint errors (#2627)

evaluation/metrics/llm_judge.py

@@ -1,8 +1,9 @@
-from openai import OpenAI
+import argparse
 import json
 from collections import defaultdict
+
 import numpy as np
-import argparse
+from openai import OpenAI
 
 client = OpenAI()
 

evaluation/metrics/utils.py

@@ -10,22 +10,17 @@ Borrowed from https://github.com/WujiangXu/AgenticMemory/blob/main/utils.py
 }
 """
 
-import re
-import string
-import numpy as np
-from typing import List, Dict, Union
 import statistics
 from collections import defaultdict
-from rouge_score import rouge_scorer
-from nltk.translate.bleu_score import sentence_bleu, SmoothingFunction
-from bert_score import score as bert_score
+from typing import Dict, List, Union
+
 import nltk
+from bert_score import score as bert_score
+from nltk.translate.bleu_score import SmoothingFunction, sentence_bleu
 from nltk.translate.meteor_score import meteor_score
+from rouge_score import rouge_scorer
 from sentence_transformers import SentenceTransformer
-import logging
-from dataclasses import dataclass
-from pathlib import Path
-from openai import OpenAI
+
 # from load_dataset import load_locomo_dataset, QA, Turn, Session, Conversation
 from sentence_transformers.util import pytorch_cos_sim
 
@@ -71,7 +66,7 @@ def calculate_bleu_scores(prediction: str, reference: str) -> Dict[str, float]:
     for n, weights in enumerate(weights_list, start=1):
         try:
             score = sentence_bleu(ref_tokens, pred_tokens, weights=weights, smoothing_function=smooth)
-        except Exception:
+        except Exception as e:
             print(f"Error calculating BLEU score: {e}")
             score = 0.0
         scores[f'bleu{n}'] = score
@@ -158,21 +153,13 @@ def calculate_metrics(prediction: str, reference: str) -> Dict[str, float]:
         f1 = 2 * precision * recall / (precision + recall) if (precision + recall) > 0 else 0.0
     
     # Calculate all scores
-    rouge_scores = 0 #calculate_rouge_scores(prediction, reference)
     bleu_scores = calculate_bleu_scores(prediction, reference)
-    bert_scores = 0 # calculate_bert_scores(prediction, reference)
-    meteor = 0 # calculate_meteor_score(prediction, reference)
-    sbert_similarity = 0 # calculate_sentence_similarity(prediction, reference)
     
     # Combine all metrics
     metrics = {
         "exact_match": exact_match,
         "f1": f1,
-        # **rouge_scores,
         **bleu_scores,
-        # **bert_scores,
-        # "meteor": meteor,
-        # "sbert_similarity": sbert_similarity
     }
 
     return metrics