correct tests

surprise/prediction_algorithms/factorization_machines.py

@@ -7,12 +7,9 @@
 import numpy as np
 import tensorflow as tf
 import scipy.sparse as sps
-from six import iteritems
-import heapq
 from tffm import TFFMClassifier, TFFMRegressor
 from polylearn import FactorizationMachineRegressor
 
-from .predictions import PredictionImpossible
 from .algo_base import AlgoBase
 
 

tests/custom_test

@@ -1,6 +1,6 @@
 There are three lines to be ignored
 Line format is user - item - rating
 
-user3 item0 None None 5
-user0 item1 None None 1
-user_neverseen item_neverseen None None 5
+user3 item0 5
+user0 item1 1
+user_neverseen item_neverseen 5

tests/custom_train

@@ -1,9 +1,9 @@
 There are three lines to be ignored
 Line format is user - item - rating
 
-user0 item0 None None 4
-user1 item0 None None 4
-user1 item1 None None 2
-user2 item0 None None 1
-user2 item1 None None 1
-user3 item1 None None 5
+user0 item0 4
+user1 item0 4
+user1 item1 2
+user2 item0 1
+user2 item1 1
+user3 item1 5

tests/test_dataset.py

@@ -145,12 +145,12 @@ def test_trainset_testset():
     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
@@ -167,19 +167,19 @@ def test_trainset_testset():
     algo.fit(trainset)
     testset = trainset.build_testset()
     algo.test(testset)  # ensure an algorithm can manage the data
-    assert ('user0', 'item0', 4) in testset
-    assert ('user3', 'item1', 5) in testset
-    assert ('user3', 'item1', 0) not in testset
+    assert ('user0', 'item0', None, None, 4) in testset
+    assert ('user3', 'item1', None, None, 5) in testset
+    assert ('user3', 'item1', None, None, 0) not in testset
 
     # Test the build_anti_testset() method
     algo = BaselineOnly()
     algo.fit(trainset)
     testset = trainset.build_anti_testset()
     algo.test(testset)  # ensure an algorithm can manage the data
-    assert ('user0', 'item0', trainset.global_mean) not in testset
-    assert ('user3', 'item1', trainset.global_mean) not in testset
-    assert ('user0', 'item1', trainset.global_mean) in testset
-    assert ('user3', 'item0', trainset.global_mean) in testset
+    assert ('user0', 'item0', None, None, trainset.global_mean) not in testset
+    assert ('user3', 'item1', None, None, trainset.global_mean) not in testset
+    assert ('user0', 'item1', None, None, trainset.global_mean) in testset
+    assert ('user3', 'item0', None, None, trainset.global_mean) in testset
 
 
 def test_load_form_df():
@@ -238,11 +238,11 @@ def test_build_anti_testset():
     # fill with some specific value
     for fillvalue in (0, 42., -1):
         anti = trainset.build_anti_testset(fill=fillvalue)
-        for (u, i, r) in anti:
+        for (u, i, u_f, i_f, r) in anti:
             assert r == fillvalue
     # fill with global_mean
     anti = trainset.build_anti_testset(fill=None)
-    for (u, i, r) in anti:
+    for (u, i, u_f, i_f, r) in anti:
         assert r == trainset.global_mean
     expect = trainset.n_users * trainset.n_items
     assert trainset.n_ratings + len(anti) == expect

tests/test_split.py

@@ -299,7 +299,7 @@ def test_LeaveOneOut():
     # Make sure only one rating per user is present in the testset
     loo = LeaveOneOut()
     for _, testset in loo.split(data):
-        cnt = Counter([uid for (uid, _, _) in testset])
+        cnt = Counter([uid for (uid, _, _, _, _) in testset])
         assert all(val == 1 for val in itervalues(cnt))
 
     # test the min_n_ratings parameter

tests/test_train2fit.py

@@ -35,7 +35,7 @@ def fit(self, trainset):
             self.bu, self.bi = 1, 1
             self.cnt += 1
 
-        def estimate(self, u, i):
+        def estimate(self, u, i, *_):
             return self.est
 
     algo = CustomAlgoFit()
@@ -91,7 +91,7 @@ def train(self, trainset):
             self.bu, self.bi = 1, 1
             self.cnt += 1
 
-        def estimate(self, u, i):
+        def estimate(self, u, i, *_):
             return self.est
 
     with pytest.warns(UserWarning):