public code v1

pygrex/explain/individual/post_hoc_knn.py

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+from scipy import sparse
+from sklearn.metrics.pairwise import cosine_similarity
+import numpy as np
+from typing import Dict, Any
+
+from .explainer import Explainer
+
+
+class KNNPostHocExplainer(Explainer):
+    def __init__(self, model, recommendations, data, knn=10):
+        super(KNNPostHocExplainer, self).__init__(model, recommendations, data)
+
+        self.knn = knn
+        # Initialize as an empty dictionary to prevent subscripting None
+        self.knn_items_dict: Dict[int, np.ndarray] = {}
+
+    def get_nn_for_getting(self, item_id: int) -> np.ndarray:
+        # Check if the KNN dictionary has been computed
+        if not self.knn_items_dict:
+            self.compute_knn_items_for_all_items()
+
+        # Return the neighbors for the item, or an empty array if not found
+        return self.knn_items_dict.get(item_id, np.array([]))
+
+    def compute_knn_items_for_all_items(self):
+        ds = np.zeros((self.num_items, self.num_users))
+        # Assuming self.dataset has attributes itemId, userId, and rating
+        ds[self.dataset.itemId, self.dataset.userId] = self.dataset.rating
+
+        ds = sparse.csr_matrix(ds)
+        sim_matrix = cosine_similarity(ds)
+        min_val = sim_matrix.min() - 1
+
+        for i in range(self.num_items):
+            sim_matrix[i, i] = min_val
+            knn_to_item_i = (-sim_matrix[i, :]).argsort()[: self.knn]
+            self.knn_items_dict[i] = knn_to_item_i
+
+    def explain_recommendation_to_user(
+        self, user_id: int, item_id: int
+    ) -> Dict[str, Any]:
+        user_ratings = self.get_user_items(user_id)
+        sim_items = self.get_nn_for_getting(item_id)
+        explanations = set(sim_items) & set(user_ratings)
+
+        return {"explanations": explanations}