public code v1

pygrex/recommender/recommender.py

@@ -0,0 +1,57 @@
+import pandas as pd
+from typing import Optional
+
+from .generic_recommender import GenericRecommender
+
+
+class Recommender(GenericRecommender):
+    def __init__(self, dataset_metadata, model, top_n: int = 10):
+        super(Recommender, self).__init__(dataset_metadata, model, top_n)
+
+    def get_predictions(
+        self,
+        user_id: int,
+        target_item_id: list,
+    ):
+        predictions = self.model.predict(user_id, target_item_id)
+        return predictions
+
+    def recommend(self, user_id: int, target_item_id: list):
+        """
+        Generate recommendations on specific itemId and userId
+        :param user_id: list, user Ids
+        :param target_item_id: list, item Ids
+        :param rated_items: list, of rated interactions.
+        :return: data.frame [userId, itemId, rank], recommendations ranking for the specified pairs of userId and itemId.
+        """
+        predictions = self.get_predictions(user_id, target_item_id)
+
+        return self.rank_prediction(user_id, target_item_id, predictions)
+
+    def recommend_user(
+        self, user_id: Optional[int] = None, user_ratings: Optional[pd.DataFrame] = None
+    ):
+        """
+        Get recommendations for a user.
+        :param user_id: int, a user Id
+        :param user_ratings: list, interactions on the user
+        :return: dataframe [userId, itemId, rank], recommendations ranking for the specified userId.
+        """
+        if user_ratings is None:
+            if user_id is None:
+                raise ValueError("Either 'user_id' or 'user_ratings' must be provided.")
+            user_ratings = self.get_rated(user_id=user_id)
+
+        if user_ratings is None:
+            return pd.DataFrame(
+                columns=["userId", "itemId", "rank"]
+            )  # Return empty recommendations
+
+        if user_id is None:
+            raise ValueError(
+                "Could not determine user_id from the provided user_ratings."
+            )
+
+        unrated_item_id = self.get_unrated(user_ratings["itemId"])
+
+        return self.recommend(user_id=user_id, target_item_id=unrated_item_id)