fix .rank() method for multiple models

cornac/models/comparer/recom_comparer_obj.pyx

@@ -663,39 +663,39 @@ class ComparERObj(Recommender):
             item_score = self.U2[item_id, :].dot(self.U1[user_id, :]) + self.H2[item_id, :].dot(self.H1[user_id, :])
             return item_score
 
-    def rank(self, user_id, item_ids=None):
+    def rank(self, user_idx, item_indices=None, **kwargs):
         """Rank all test items for a given user.
 
         Parameters
         ----------
-        user_id: int, required
+        user_idx: int, required
             The index of the user for whom to perform item raking.
 
-        item_ids: 1d array, optional, default: None
+        item_indices: 1d array, optional, default: None
             A list of candidate item indices to be ranked by the user.
             If `None`, list of ranked known item indices and their scores will be returned
 
         Returns
         -------
         Tuple of `item_rank`, and `item_scores`. The order of values
-        in item_scores are corresponding to the order of their ids in item_ids
+        in item_scores are corresponding to the order of their ids in item_indices
 
         """
-        X_ = self.U1[user_id, :].dot(self.V.T)
+        X_ = self.U1[user_idx, :].dot(self.V.T)
         most_cared_aspects_indices = (-X_).argsort()[:self.num_most_cared_aspects]
         most_cared_X_ = X_[most_cared_aspects_indices]
         most_cared_Y_ = self.U2.dot(self.V[most_cared_aspects_indices, :].T)
         explicit_scores = most_cared_X_.dot(most_cared_Y_.T) / (self.num_most_cared_aspects * self.rating_scale)
-        item_scores = self.alpha * explicit_scores + (1 - self.alpha) * self.score(user_id)
+        item_scores = self.alpha * explicit_scores + (1 - self.alpha) * self.score(user_idx)
 
-        if item_ids is None:
+        if item_indices is None:
             item_scores = item_scores
             item_rank = item_scores.argsort()[::-1]
         else:
-            num_items = max(self.num_items, max(item_ids) + 1)
+            num_items = max(self.num_items, max(item_indices) + 1)
             item_scores = np.ones(num_items) * np.min(item_scores)
             item_scores[:self.num_items] = item_scores
             item_rank = item_scores.argsort()[::-1]
-            item_rank = intersects(item_rank, item_ids, assume_unique=True)
-            item_scores = item_scores[item_ids]
+            item_rank = intersects(item_rank, item_indices, assume_unique=True)
+            item_scores = item_scores[item_indices]
         return item_rank, item_scores

cornac/models/comparer/recom_comparer_sub.pyx

@@ -759,7 +759,7 @@ class ComparERSub(MTER):
 
         return correct, skipped, loss, bpr_loss
 
-    def rank(self, user_idx, item_indices=None):
+    def rank(self, user_idx, item_indices=None, **kwargs):
         if self.alpha > 0 and self.n_top_aspects > 0:
             n_top_aspects = min(self.n_top_aspects, self.num_aspects)
             ts1 = np.einsum("abc,a->bc", self.G1, self.U[user_idx])

cornac/models/efm/recom_efm.pyx

@@ -468,7 +468,7 @@ class EFM(Recommender):
             item_score = self.U2[item_idx, :].dot(self.U1[user_idx, :]) + self.H2[item_idx, :].dot(self.H1[user_idx, :])
             return item_score
 
-    def rank(self, user_idx, item_indices=None):
+    def rank(self, user_idx, item_indices=None, **kwargs):
         """Rank all test items for a given user.
 
         Parameters

cornac/models/lrppm/recom_lrppm.pyx

@@ -516,7 +516,7 @@ class LRPPM(Recommender):
             item_score = self.I[i_idx].dot(self.U[u_idx])
             return item_score
 
-    def rank(self, user_idx, item_indices=None):
+    def rank(self, user_idx, item_indices=None, **kwargs):
         if self.alpha > 0 and self.num_top_aspects > 0:
             n_items = self.num_items
             num_top_aspects = min(self.num_top_aspects, self.num_aspects)

cornac/models/recommender.py

@@ -473,7 +473,7 @@ def rate(self, user_idx, item_idx, clipping=True):
 
         return rating_pred
 
-    def rank(self, user_idx, item_indices=None, k=-1, **kwargs):
+    def rank(self, user_idx, item_indices=None, **kwargs):
         """Rank all test items for a given user.
 
         Parameters
@@ -485,7 +485,7 @@ def rank(self, user_idx, item_indices=None, k=-1, **kwargs):
             A list of candidate item indices to be ranked by the user.
             If `None`, list of ranked known item indices and their scores will be returned.
 
-        k: int, required
+        k: int, optional
             Cut-off length for recommendations, k=-1 will return ranked list of all items.
             This is more important for ANN to know the limit to avoid exhaustive ranking.
 
@@ -502,6 +502,8 @@ def rank(self, user_idx, item_indices=None, k=-1, **kwargs):
         except ScoreException:
             known_item_scores = np.ones(self.total_items) * self.default_score()
 
+        k = kwargs.get("k", -1)
+
         # check if the returned scores also cover unknown items
         # if not, all unknown items will be given the MIN score
         if len(known_item_scores) == self.total_items: