Update handler.py

handler.py

@@ -3,6 +3,7 @@ import pickle
 import numpy as np
 import pandas as pd
 import os
+import dill
 
 class ContentBasedRecommender:
     def __init__(self, train_data):
@@ -15,38 +16,13 @@ class ContentBasedRecommender:
 
         return np.random.choice(recommended_books, size=k, replace=False) if len(recommended_books) >= k else recommended_books
 
-    def evaluate(self, test_data, k=10):
-        user_ids = test_data['user_id'].unique()
-        hit_rate, ndcg_scores = [], []
-
-        for user_id in user_ids[:100]:
-            true_books = set(test_data[test_data['user_id'] == user_id]['book_id'])
-            pred_books = set(self.predict(user_id, k))
-
-            hits = len(true_books & pred_books)
-            hit_rate.append(hits / min(k, len(true_books)))
-
-            dcg = sum(1 / math.log2(rank + 2) for rank, book in enumerate(pred_books) if book in true_books)
-            idcg = sum(1 / math.log2(i + 2) for i in range(min(k, len(true_books))))
-            ndcg = dcg / idcg if idcg > 0 else 0
-            ndcg_scores.append(ndcg)
-
-        return np.mean(hit_rate), np.mean(ndcg_scores)
-
 class EndpointHandler:
     def __init__(self, path=""):
         model_path = os.path.join(path, "model.pkl")
         with open(model_path, 'rb') as f:
-            self.model = pickle.load(f)
+            self.model = dill.load(f)
 
     def __call__(self, data: Dict[str, Any]) -> List[Dict[str, Any]]:
-        """
-        data args:
-            user_id (:obj: `str` or `int`)
-            k (:obj: `int`, optional)
-        Return:
-            A :obj:`list` of :obj:`dict`: will be serialized and returned
-        """
         user_id = data.pop("user_id", None)
         k = data.pop("k", 10)  # Default to 10 if not provided