def test_save_load():
r = save_load(BaseRecommender())
assert_equal(type(r), BaseRecommender)
r = MyRecommender()
r2 = save_load(r)
assert_equal(type(r2), type(r))
assert_array_equal(r2.foo, r.foo)
assert_equal(r2.description, r.description)
def test_zero_known_item_scores():
train = get_random_coo_matrix().tocsr()
predictions = np.random.random_sample(train.shape)
r = BaseRecommender()
safe = r._zero_known_item_scores(predictions, train)
num_users, num_items = predictions.shape
for u in xrange(num_users):
for i in xrange(num_items):
if i in train[u].indices:
assert_less_equal(safe[u, i], 0)
else:
assert_equal(safe[u, i], predictions[u, i])
def test_read_recommender_description():
check_read_description(BaseRecommender())
check_read_description(MyRecommender())
def test_save_filepath_condition():
r = BaseRecommender()
invalid_filepath = 'no suffix'
assert_raises(ValueError, r.save, invalid_filepath)
