Let models use knows_user() and knows_item() instead of using train_set

cornac/data/dataset.py

@@ -578,14 +578,6 @@ def item_iter(self, batch_size=1, shuffle=False):
         for batch_ids in self.idx_iter(len(item_indices), batch_size, shuffle):
             yield item_indices[batch_ids]
 
-    def contains_user(self, user_idx):
-        """Return whether given user index is in the dataset"""
-        return user_idx >= 0 and user_idx < self.num_users
-
-    def contains_item(self, item_idx):
-        """Return whether given item index is in the dataset"""
-        return item_idx >= 0 and item_idx < self.num_items
-
     def add_modalities(self, **kwargs):
         self.user_feature = kwargs.get("user_feature", None)
         self.item_feature = kwargs.get("item_feature", None)

cornac/eval_methods/base_method.py

@@ -160,7 +160,7 @@ def ranking_eval(
     avg_results = []
     user_results = [{} for _ in enumerate(metrics)]
 
-    gt_mat = test_set.csr_matrix
+    test_mat = test_set.csr_matrix
     train_mat = train_set.csr_matrix
     val_mat = None if val_set is None else val_set.csr_matrix
 
@@ -175,7 +175,7 @@ def pos_items(csr_row):
     for user_idx in tqdm(
         test_user_indices, desc="Ranking", disable=not verbose, miniters=100
     ):
-        test_pos_items = pos_items(gt_mat.getrow(user_idx))
+        test_pos_items = pos_items(test_mat.getrow(user_idx))
         if len(test_pos_items) == 0:
             continue
 
@@ -186,7 +186,7 @@ def pos_items(csr_row):
         val_pos_items = [] if val_mat is None else pos_items(val_mat.getrow(user_idx))
         train_pos_items = (
             pos_items(train_mat.getrow(user_idx))
-            if train_set.contains_user(user_idx)
+            if user_idx < train_mat.shape[0]
             else []
         )
 

cornac/models/recommender.py

@@ -421,7 +421,7 @@ def recommend(self, user_id, k=-1, remove_seen=False, train_set=None):
             seen_mask = np.zeros(len(item_indices), dtype="bool")
             if train_set is None:
                 raise ValueError("train_set must be provided to remove seen items.")
-            if train_set.contains_user(user_idx):
+            if user_idx < train_set.csr_matrix.shape[0]:
                 seen_mask[train_set.csr_matrix.getrow(user_idx).indices] = True
                 item_indices = item_indices[~seen_mask]
 

tests/cornac/data/test_dataset.py

@@ -40,12 +40,6 @@ def test_init(self):
         self.assertEqual(train_set.num_users, 10)
         self.assertEqual(train_set.num_items, 10)
 
-        self.assertTrue(train_set.contains_user(7))
-        self.assertFalse(train_set.contains_item(13))
-
-        self.assertTrue(train_set.contains_item(3))
-        self.assertFalse(train_set.contains_item(16))
-
         self.assertEqual(train_set.uid_map["768"], 1)
         self.assertEqual(train_set.iid_map["195"], 7)
 

tests/cornac/models/test_recommender.py

@@ -16,16 +16,24 @@
 import unittest
 
 from cornac.data import Reader, Dataset
-from cornac.eval_methods import RatioSplit
 from cornac.models import MF
-from cornac.metrics import MAE, RMSE
-from cornac.experiment.experiment import Experiment
 
 
 class TestRecommender(unittest.TestCase):
     def setUp(self):
         self.data = Reader().read("./tests/data.txt")
 
+    def test_knows_x(self):
+        mf = MF(1, 1, seed=123)
+        dataset = Dataset.from_uir(self.data)
+        mf.fit(dataset)
+
+        self.assertTrue(mf.knows_user(7))
+        self.assertFalse(mf.knows_item(13))
+
+        self.assertTrue(mf.knows_item(3))
+        self.assertFalse(mf.knows_item(16))
+
     def test_recommend(self):
         mf = MF(1, 1, seed=123)
         dataset = Dataset.from_uir(self.data)