Let models manage total_users and total_items instead of train_set, t…

…rain_set shouldn't worry about users and items in validation and test

.gitignore

@@ -8,6 +8,9 @@ __pycache__/
 
 # C extensions
 *.so
+cornac/models/*/*.cpp
+cornac/models/*/cython/*.cpp
+cornac/utils/*.cpp
 
 # Distribution / packaging
 bin/

cornac/data/dataset.py

@@ -98,8 +98,6 @@ def __init__(
         self.min_rating = np.min(r_values)
         self.global_mean = np.mean(r_values)
 
-        self.__total_users = None
-        self.__total_items = None
         self.__user_ids = None
         self.__item_ids = None
 
@@ -111,28 +109,6 @@ def __init__(
         self.__csc_matrix = None
         self.__dok_matrix = None
 
-    @property
-    def total_users(self):
-        """Total number of users including test and validation users if exists"""
-        return self.__total_users if self.__total_users is not None else self.num_users
-
-    @total_users.setter
-    def total_users(self, input_value):
-        """Set total number of users for the dataset"""
-        assert input_value >= self.num_users
-        self.__total_users = input_value
-
-    @property
-    def total_items(self):
-        """Total number of items including test and validation items if exists"""
-        return self.__total_items if self.__total_items is not None else self.num_items
-
-    @total_items.setter
-    def total_items(self, input_value):
-        """Set total number of items for the dataset"""
-        assert input_value >= self.num_items
-        self.__total_items = input_value
-
     @property
     def user_ids(self):
         """Return the list of raw user ids"""

cornac/eval_methods/base_method.py

@@ -540,9 +540,6 @@ def _build_datasets(self, train_data, test_data, val_data=None):
             print("Total users = {}".format(self.total_users))
             print("Total items = {}".format(self.total_items))
 
-        self.train_set.total_users = self.total_users
-        self.train_set.total_items = self.total_items
-
     def _build_modalities(self):
         for user_modality in [
             self.user_feature,

cornac/models/causalrec/recom_causalrec.py

@@ -185,8 +185,8 @@ def fit(self, train_set, val_set=None):
         train_features = train_set.item_image.features[: self.total_items]
         train_features = train_features.astype(np.float32)
         self._init(
-            n_users=train_set.total_users,
-            n_items=train_set.total_items,
+            n_users=self.total_users,
+            n_items=self.total_items,
             features=train_features,
         )
 

cornac/models/fm/recom_fm.pyx

@@ -265,7 +265,7 @@ class FM(Recommender):
         (uid, iid, val) = train_set.uir_tuple
         cdef Data *train = _prepare_data(
             uid, 
-            iid + train_set.total_users, 
+            iid + self.total_users, 
             val.astype(np.float32), 
             num_feature, 
             self.method in ["als", "mcmc"],