Formatting (#2750)

evaluation/generate_scores.py

@@ -3,7 +3,7 @@ import json
 import pandas as pd
 
 # Load the evaluation metrics data
-with open('evaluation_metrics.json', 'r') as f:
+with open("evaluation_metrics.json", "r") as f:
     data = json.load(f)
 
 # Flatten the data into a list of question items
@@ -15,28 +15,20 @@ for key in data:
 df = pd.DataFrame(all_items)
 
 # Convert category to numeric type
-df['category'] = pd.to_numeric(df['category'])
+df["category"] = pd.to_numeric(df["category"])
 
 # Calculate mean scores by category
-result = df.groupby('category').agg({
-    'bleu_score': 'mean',
-    'f1_score': 'mean',
-    'llm_score': 'mean'
-}).round(4)
+result = df.groupby("category").agg({"bleu_score": "mean", "f1_score": "mean", "llm_score": "mean"}).round(4)
 
 # Add count of questions per category
-result['count'] = df.groupby('category').size()
+result["count"] = df.groupby("category").size()
 
 # Print the results
 print("Mean Scores Per Category:")
 print(result)
 
 # Calculate overall means
-overall_means = df.agg({
-    'bleu_score': 'mean',
-    'f1_score': 'mean',
-    'llm_score': 'mean'
-}).round(4)
+overall_means = df.agg({"bleu_score": "mean", "f1_score": "mean", "llm_score": "mean"}).round(4)
 
 print("\nOverall Mean Scores:")
-print(overall_means)
+print(overall_means)