From 45469ff10352b4b643e9025d636e1313de1805a4 Mon Sep 17 00:00:00 2001
From: cuom1999 <lephuocdinh99@gmail.com>
Date: Tue, 26 Mar 2024 12:23:13 -0500
Subject: [PATCH] Update collab filter to use dict result

---
 judge/ml/collab_filter.py | 39 ++++++++++++++-------------------------
 1 file changed, 14 insertions(+), 25 deletions(-)

diff --git a/judge/ml/collab_filter.py b/judge/ml/collab_filter.py
index d19c5e5..0bdafa8 100644
--- a/judge/ml/collab_filter.py
+++ b/judge/ml/collab_filter.py
@@ -20,29 +20,21 @@ class CollabFilter:
         )
         _, problem_arr = self.embeddings.files
         self.name = name
-        self.problem_embeddings = self.embeddings[problem_arr]
+        self.problem_embeddings = self.embeddings[problem_arr].item()
 
     def __str__(self):
         return self.name
 
     def compute_scores(self, query_embedding, item_embeddings, measure=DOT):
-        """Computes the scores of the candidates given a query.
-        Args:
-        query_embedding: a vector of shape [k], representing the query embedding.
-        item_embeddings: a matrix of shape [N, k], such that row i is the embedding
-            of item i.
-        measure: a string specifying the similarity measure to be used. Can be
-            either DOT or COSINE.
-        Returns:
-        scores: a vector of shape [N], such that scores[i] is the score of item i.
-        """
+        """Return {id: score}"""
         u = query_embedding
-        V = item_embeddings
+        V = np.stack(list(item_embeddings.values()))
         if measure == self.COSINE:
             V = V / np.linalg.norm(V, axis=1, keepdims=True)
             u = u / np.linalg.norm(u)
         scores = u.dot(V.T)
-        return scores
+        scores_by_id = {id_: s for id_, s in zip(item_embeddings.keys(), scores)}
+        return scores_by_id
 
     def _get_embedding_version(self):
         first_problem = self.problem_embeddings[0]
@@ -54,8 +46,8 @@ class CollabFilter:
     @cache_wrapper(prefix="CFgue", timeout=86400)
     def _get_user_embedding(self, user_id, embedding_version):
         user_arr, _ = self.embeddings.files
-        user_embeddings = self.embeddings[user_arr]
-        if user_id >= len(user_embeddings):
+        user_embeddings = self.embeddings[user_arr].item()
+        if user_id not in user_embeddings:
             return user_embeddings[0]
         return user_embeddings[user_id]
 
@@ -67,27 +59,24 @@ class CollabFilter:
     def user_recommendations(self, user_id, problems, measure=DOT, limit=None):
         user_embedding = self.get_user_embedding(user_id)
         scores = self.compute_scores(user_embedding, self.problem_embeddings, measure)
-
         res = []  # [(score, problem)]
         for pid in problems:
-            if pid < len(scores):
+            if pid in scores:
                 res.append((scores[pid], pid))
 
         res.sort(reverse=True, key=lambda x: x[0])
-        res = res[:limit]
-        return res
+        return res[:limit]
 
     # return a list of pid
     def problem_neighbors(self, problem, problemset, measure=DOT, limit=None):
         pid = problem.id
-        if pid >= len(self.problem_embeddings):
-            return []
-        scores = self.compute_scores(
-            self.problem_embeddings[pid], self.problem_embeddings, measure
-        )
+        if pid not in self.problem_embeddings:
+            return None
+        embedding = self.problem_embeddings[pid]
+        scores = self.compute_scores(embedding, self.problem_embeddings, measure)
         res = []
         for p in problemset:
-            if p < len(scores):
+            if p in scores:
                 res.append((scores[p], p))
         res.sort(reverse=True, key=lambda x: x[0])
         return res[:limit]