fix typos unkown -> unknown

examples/building_custom_algorithms/with_baselines_or_sim.py

@@ -32,7 +32,7 @@ def fit(self, trainset):
     def estimate(self, u, i):
 
         if not (self.trainset.knows_user(u) and self.trainset.knows_item(i)):
-            raise PredictionImpossible('User and/or item is unkown.')
+            raise PredictionImpossible('User and/or item is unknown.')
 
         # Compute similarities between u and v, where v describes all other
         # users that have also rated item i.

surprise/prediction_algorithms/algo_base.py

@@ -61,7 +61,8 @@ def predict(self, uid, iid, r_ui=None, clip=True, verbose=False):
         The ``predict`` method converts raw ids to inner ids and then calls the
         ``estimate`` method which is defined in every derived class. If the
         prediction is impossible (e.g. because the user and/or the item is
-        unkown), the prediction is set according to :meth:`default_prediction()
+        unknown), the prediction is set according to
+        :meth:`default_prediction()
         <surprise.prediction_algorithms.algo_base.AlgoBase.default_prediction>`.
 
         Args:

surprise/prediction_algorithms/knns.py

@@ -103,7 +103,7 @@ def fit(self, trainset):
     def estimate(self, u, i):
 
         if not (self.trainset.knows_user(u) and self.trainset.knows_item(i)):
-            raise PredictionImpossible('User and/or item is unkown.')
+            raise PredictionImpossible('User and/or item is unknown.')
 
         x, y = self.switch(u, i)
 
@@ -186,7 +186,7 @@ def fit(self, trainset):
     def estimate(self, u, i):
 
         if not (self.trainset.knows_user(u) and self.trainset.knows_item(i)):
-            raise PredictionImpossible('User and/or item is unkown.')
+            raise PredictionImpossible('User and/or item is unknown.')
 
         x, y = self.switch(u, i)
 
@@ -387,7 +387,7 @@ def fit(self, trainset):
     def estimate(self, u, i):
 
         if not (self.trainset.knows_user(u) and self.trainset.knows_item(i)):
-            raise PredictionImpossible('User and/or item is unkown.')
+            raise PredictionImpossible('User and/or item is unknown.')
 
         x, y = self.switch(u, i)
 

surprise/prediction_algorithms/matrix_factorization.pyx

@@ -275,7 +275,7 @@ class SVD(AlgoBase):
             if known_user and known_item:
                 est = np.dot(self.qi[i], self.pu[u])
             else:
-                raise PredictionImpossible('User and item are unkown.')
+                raise PredictionImpossible('User and item are unknown.')
 
         return est
 
@@ -737,6 +737,6 @@ class NMF(AlgoBase):
             if known_user and known_item:
                 est = np.dot(self.qi[i], self.pu[u])
             else:
-                raise PredictionImpossible('User and item are unkown.')
+                raise PredictionImpossible('User and item are unknown.')
 
         return est

surprise/prediction_algorithms/slope_one.pyx

@@ -82,7 +82,7 @@ class SlopeOne(AlgoBase):
     def estimate(self, u, i):
 
         if not (self.trainset.knows_user(u) and self.trainset.knows_item(i)):
-            raise PredictionImpossible('User and/or item is unkown.')
+            raise PredictionImpossible('User and/or item is unknown.')
 
         # Ri: relevant items for i. This is the set of items j rated by u that
         # also have common users with i (i.e. at least one user has rated both

tests/test_algorithms.py

@@ -39,8 +39,8 @@ def test_unknown_user_or_item(toy_data):
         algo = klass()
         algo.fit(trainset)
         algo.predict('user0', 'unknown_item', None)
-        algo.predict('unkown_user', 'item0', None)
-        algo.predict('unkown_user', 'unknown_item', None)
+        algo.predict('unknown_user', 'item0', None)
+        algo.predict('unknown_user', 'unknown_item', None)
 
     # unrelated, but test the fit().test() one-liner:
     trainset, testset = train_test_split(toy_data, test_size=2)

tests/test_dataset.py

@@ -75,12 +75,12 @@ def test_trainset_testset(toy_data_reader):
     for i in range(4):
         assert trainset.to_inner_uid('user' + str(i)) == i
     with pytest.raises(ValueError):
-        trainset.to_inner_uid('unkown_user')
+        trainset.to_inner_uid('unknown_user')
 
     for i in range(2):
         assert trainset.to_inner_iid('item' + str(i)) == i
     with pytest.raises(ValueError):
-        trainset.to_inner_iid('unkown_item')
+        trainset.to_inner_iid('unknown_item')
 
     # test inner2raw
     assert trainset._inner2raw_id_users is None