Add filtering options for similar_users and similar_items

implicit/cpu/matrix_factorization_base.py

@@ -53,7 +53,7 @@ def recommend(
 
             # check selected items are in the model
             if items.max() >= self.item_factors.shape[0] or items.min() < 0:
-                raise IndexError("Some itemids are not in the model")
+                raise IndexError("Some itemids in the items parameter in are not in the model")
 
         # get a CSR matrix of items to filter per-user
         filter_query_items = None
@@ -136,19 +136,42 @@ def recalculate_user(self, userid, user_items):
     def recalculate_item(self, itemid, react_users):
         raise NotImplementedError("recalculate_item is not supported with this model")
 
-    def similar_users(self, userid, N=10):
-        factor = self.user_factors[userid]
-        factors = self.user_factors
+    def similar_users(self, userid, N=10, filter_users=None, users=None):
+        user_factors = self.user_factors
         norms = self.user_norms
         norm = norms[userid]
-        return self._get_similarity_score(factor, norm, factors, norms, N)
+
+        # if we have an user list to restrict down to, we need to filter the user_factors
+        if users is not None:
+            if filter_users:
+                raise ValueError("Can't set both users and filter_users in similar_users call")
+
+            users = np.array(users)
+            user_factors = user_factors[users]
+            norms = norms[users]
+
+            # check selected items are in the model
+            if users.max() >= self.user_factors.shape[0] or users.min() < 0:
+                raise IndexError("Some userids in the users parameter are not in the model")
+
+        factor = self.user_factors[userid]
+        ids, scores = self._get_similarity_score(
+            factor, norm, user_factors, norms, N, filter_items=filter_users
+        )
+        if users is not None:
+            ids = users[ids]
+
+        return ids, scores
 
     similar_users.__doc__ = RecommenderBase.similar_users.__doc__
 
-    def similar_items(self, itemid, N=10, react_users=None, recalculate_item=False):
+    def similar_items(
+        self, itemid, N=10, react_users=None, recalculate_item=False, filter_items=None, items=None
+    ):
         factor = self._item_factor(itemid, react_users, recalculate_item)
         factors = self.item_factors
         norms = self.item_norms
+
         if recalculate_item:
             if np.isscalar(itemid):
                 norm = np.linalg.norm(factor)
@@ -159,12 +182,30 @@ def similar_items(self, itemid, N=10, react_users=None, recalculate_item=False):
         else:
             norm = norms[itemid]
 
-        return self._get_similarity_score(factor, norm, factors, norms, N)
+        # if we have an item list to restrict down to, we need to filter the item_factors
+        if items is not None:
+            if filter_items:
+                raise ValueError("Can't set both items and filter_items in similar_items call")
+
+            items = np.array(items)
+            factors = factors[items]
+            norms = norms[items]
+
+            # check selected items are in the model
+            if items.max() >= self.item_factors.shape[0] or items.min() < 0:
+                raise IndexError("Some itemids in the items parameter are not in the model")
+
+        ids, scores = self._get_similarity_score(
+            factor, norm, factors, norms, N, filter_items=filter_items
+        )
+        if items is not None:
+            ids = items[ids]
+        return ids, scores
 
     similar_items.__doc__ = RecommenderBase.similar_items.__doc__
 
-    def _get_similarity_score(self, factor, norm, factors, norms, N):
-        ids, scores = topk(factors, factor, N, item_norms=norms)
+    def _get_similarity_score(self, factor, norm, factors, norms, N, filter_items=None):
+        ids, scores = topk(factors, factor, N, item_norms=norms, filter_items=filter_items)
         if np.isscalar(norm):
             ids, scores = ids[0], scores[0]
             scores /= norm

implicit/gpu/knn.cu

@@ -162,9 +162,13 @@ void KnnQuery::topk(const Matrix & items, const Matrix & query, int k,
             auto count = thrust::make_counting_iterator<int>(0);
             float * data = temp_distances.data;
             int * items = item_filter->data;
-            thrust::for_each(count, count + item_filter->size,
+            int items_size = item_filter->size;
+            int cols = temp_distances.cols;
+            thrust::for_each(count, count + items_size * temp_distances.rows,
                [=] __device__(int i) {
-                  data[items[i]] = -FLT_MAX;
+                  int col = items[i % items_size];
+                  int row = i / items_size;
+                  data[row * cols + col] = -FLT_MAX;
             });
         }
 

implicit/gpu/matrix_factorization_base.py

@@ -56,7 +56,7 @@ def recommend(
             if items.max() >= self.item_factors.shape[0] or items.min() < 0:
                 raise IndexError("Some itemids are not in the model")
 
-        if filter_items:
+        if filter_items is not None:
             filter_items = implicit.gpu.IntVector(np.array(filter_items, dtype="int32"))
 
         query_filter = None
@@ -105,11 +105,33 @@ def item_norms(self):
             self._item_norms_host = self._item_norms.to_numpy().reshape(self._item_norms.shape[1])
         return self._item_norms
 
-    def similar_users(self, userid, N=10):
+    def similar_users(self, userid, N=10, filter_users=None, users=None):
+        norms = self.user_norms
+        user_factors = self.user_factors
+        if users is not None:
+            if filter_users:
+                raise ValueError("Can't set both users and filter_users in similar_users call")
+
+            users = np.array(users)
+            user_factors = user_factors[users]
+
+            # TODO: we should be able to do this all on the GPU
+            norms = implicit.gpu.Matrix(self._user_norms_host[users].reshape(1, len(users)))
+
+            # check selected items are in the model
+            if users.max() >= self.user_factors.shape[0] or users.min() < 0:
+                raise IndexError("Some userids in the users parameter are not in the model")
+
+        if filter_users is not None:
+            filter_users = implicit.gpu.IntVector(np.array(filter_users, dtype="int32"))
+
         ids, scores = self._knn.topk(
-            self.user_factors, self.user_factors[userid], N, self.user_norms
+            user_factors, self.user_factors[userid], N, norms, item_filter=filter_users
         )
 
+        if users is not None:
+            ids = users[ids]
+
         user_norms = self._user_norms_host[userid]
         if np.isscalar(userid):
             ids, scores = ids[0], scores[0]
@@ -120,13 +142,38 @@ def similar_users(self, userid, N=10):
 
     similar_users.__doc__ = RecommenderBase.similar_users.__doc__
 
-    def similar_items(self, itemid, N=10, react_users=None, recalculate_item=False):
+    def similar_items(
+        self, itemid, N=10, react_users=None, recalculate_item=False, filter_items=None, items=None
+    ):
         if recalculate_item:
             raise NotImplementedError("recalculate_item isn't support on GPU yet")
+
+        item_factors = self.item_factors
+        norms = self.item_norms
+        if items is not None:
+            if filter_items:
+                raise ValueError("Can't set both items and filter_items in similar_items call")
+
+            items = np.array(items)
+
+            # TODO: we should be able to do this all on the GPU
+            norms = implicit.gpu.Matrix(self._item_norms_host[items].reshape(1, len(items)))
+            item_factors = item_factors[items]
+
+            # check selected items are in the model
+            if items.max() >= self.item_factors.shape[0] or items.min() < 0:
+                raise IndexError("Some itemids are not in the model")
+
+        if filter_items is not None:
+            filter_items = implicit.gpu.IntVector(np.array(filter_items, dtype="int32"))
+
         ids, scores = self._knn.topk(
-            self.item_factors, self.item_factors[itemid], N, self.item_norms
+            item_factors, self.item_factors[itemid], N, norms, item_filter=filter_items
         )
 
+        if items is not None:
+            ids = items[ids]
+
         item_norms = self._item_norms_host[itemid]
         if np.isscalar(itemid):
             ids, scores = ids[0], scores[0]

implicit/nearest_neighbours.py

@@ -59,7 +59,7 @@ def recommend(
         if userid >= user_items.shape[0]:
             raise ValueError("userid is out of bounds of the user_items matrix")
 
-        if filter_items and items:
+        if filter_items is not None and items is not None:
             raise ValueError("Can't specify both filter_items and items")
 
         if filter_items is not None:
@@ -96,22 +96,43 @@ def recommend(
 
         return ids, scores
 
-    def similar_users(self, userid, N=10):
-        raise NotImplementedError("Not implemented Yet")
+    def similar_users(self, userid, N=10, filter_users=None, users=None):
+        raise NotImplementedError("similar_users isn't implemented for item-item recommenders")
 
-    def similar_items(self, itemid, N=10, react_users=None, recalculate_item=False):
+    def similar_items(
+        self, itemid, N=10, react_users=None, recalculate_item=False, filter_items=None, items=None
+    ):
         """Returns a list of the most similar other items"""
         if recalculate_item:
             raise NotImplementedError("Recalculate_item isn't implemented")
 
+        print("N", N)
         if not np.isscalar(itemid):
-            return _batch(self.similar_items, itemid, N=N)
+            return _batch(self.similar_items, itemid, N=N, filter_items=filter_items, items=items)
+
+        if filter_items is not None and items is not None:
+            raise ValueError("Can't specify both filter_items and items")
 
         if itemid >= self.similarity.shape[0]:
             return np.array([]), np.array([])
 
         ids = self.similarity[itemid].indices
         scores = self.similarity[itemid].data
+
+        if filter_items is not None:
+            mask = np.in1d(ids, filter_items, invert=True)
+            ids, scores = ids[mask], scores[mask]
+
+        elif items is not None:
+            mask = np.in1d(ids, items)
+            ids, scores = ids[mask], scores[mask]
+
+            # returned items should be equal to input selected items
+            missing = items[np.in1d(items, ids, invert=True)]
+            if missing.size:
+                ids = np.append(ids, missing)
+                scores = np.append(scores, np.full(missing.size, -np.finfo(scores.dtype).max))
+
         best = np.argsort(scores)[::-1][:N]
         return ids[best], scores[best]
 
@@ -226,8 +247,9 @@ def _batch(func, ids, *args, N=10, **kwargs):
         batch_ids, batch_scores = func(idx, *args, N=N, **kwargs)
 
         # pad out to N items if we're returned fewer
-        missing_items = len(batch_ids) - N
-        if missing_items:
+        missing_items = N - len(batch_ids)
+        print("i", i, "idx", idx, " missing ", missing_items)
+        if missing_items > 0:
             batch_ids = np.append(batch_ids, np.full(missing_items, -1))
             batch_scores = np.append(
                 batch_scores, np.full(missing_items, -np.finfo(np.float32).max)

implicit/recommender_base.py

@@ -74,25 +74,32 @@ def recommend(
         """
 
     @abstractmethod
-    def similar_users(self, userid, N=10):
+    def similar_users(self, userid, N=10, filter_users=None, users=None):
         """
-        Calculates a list of similar users
+        Calculates the most similar users for a userid or array of userids
 
         Parameters
         ----------
         userid : Union[int, array_like]
-            The userid or an array of userids to retrieve similar users for
+            The userid or an array of userids to retrieve similar users for.
         N : int, optional
             The number of similar users to return
+        filter_users: array_like, optional
+            An array of user ids to filter out from the results being returned
+        users: array_like, optional
+            An array of user ids to include in the output. If not set all users in the training
+            set will be included. Cannot be used with the filter_users options
 
         Returns
         -------
         tuple
-            Tuple of (itemids, scores) arrays
+            Tuple of (userids, scores) arrays
         """
 
     @abstractmethod
-    def similar_items(self, itemid, N=10, react_users=None, recalculate_item=False):
+    def similar_items(
+        self, itemid, N=10, react_users=None, recalculate_item=False, filter_items=None, items=None
+    ):
         """
         Calculates a list of similar items
 
@@ -108,6 +115,11 @@ def similar_items(self, itemid, N=10, react_users=None, recalculate_item=False):
         recalculate_item : bool, optional
             When true, don't rely on stored item state and instead recalculate from the
             passed in react_users
+        filter_items: array_like, optional
+            An array of item ids to filter out from the results being returned
+        items: array_like, optional
+            An array of item ids to include in the output. If not set all items in the training
+            set will be included. Cannot be used with the filter_items options
 
         Returns
         -------

tests/recommender_base_test.py

@@ -23,7 +23,7 @@ def get_checker_board(X):
     return csr_matrix(ret - np.eye(X))
 
 
-class RecommenderBaseTestMixin(object):
+class RecommenderBaseTestMixin:
     """Mixin to test a bunch of common functionality in models
     deriving from RecommenderBase"""
 
@@ -163,6 +163,25 @@ def test_similar_users_batch(self):
             for r in ids[userid]:
                 self.assertEqual(r % 2, userid % 2)
 
+    def test_similar_users_filter(self):
+        model = self._get_model()
+        # calculating similar users in nearest-neighbours is not implemented yet
+        if isinstance(model, ItemItemRecommender):
+            return
+
+        model.fit(get_checker_board(256), show_progress=False)
+        userids = np.arange(50)
+
+        ids, _ = model.similar_users(userids, N=10, filter_users=np.arange(52) * 5)
+        for userid in userids:
+            for r in ids[userid]:
+                self.assertTrue(r % 5 != 0)
+
+        selected = np.arange(10)
+        ids, _ = model.similar_users(userids, N=10, users=selected)
+        for userid in userids:
+            self.assertEqual(set(ids[userid]), set(selected))
+
     def test_similar_items(self):
         model = self._get_model()
         model.fit(get_checker_board(256), show_progress=False)
@@ -198,6 +217,22 @@ def check_results(ids):
             # some models don't support recalculating user on the fly, and that's ok
             pass
 
+    def test_similar_items_filter(self):
+        model = self._get_model()
+
+        model.fit(get_checker_board(256), show_progress=False)
+        itemids = np.arange(50)
+
+        ids, _ = model.similar_items(itemids, N=10, filter_items=np.arange(52) * 5)
+        for itemid in itemids:
+            for r in ids[itemid]:
+                self.assertTrue(r % 5 != 0)
+
+        selected = np.arange(10)
+        ids, _ = model.similar_items(itemids, N=10, items=selected)
+        for itemid in itemids:
+            self.assertEqual(set(ids[itemid]), set(selected))
+
     def test_zero_length_row(self):
         # get a matrix where a row/column is 0
         item_users = get_checker_board(50).todense()