def test_minUsers_topUsers(self):
userID = 'Luciana Nunes'
numUsers = 0
allUserIDs = self.model.UserIDs()
preferenceEstimator = estimateUserUser
rescorer = TanHScorer()
self.assertEquals([],
topUsers(userID, allUserIDs, numUsers,
preferenceEstimator, self.similarity,
rescorer))
def test_otherUserNeighborhood(self):
numUsers = 4
userID = 'Luciana Nunes'
minSimilarity = 0.0
scorer = TanHScorer()
n = NearestNUserNeighborhood(self.similarity, self.model, numUsers,
minSimilarity)
self.assertEquals(
['Maria Gabriela', 'Penny Frewman', 'Steve Gates', 'Lorena Abreu'],
n.userNeighborhood(userID, scorer))
def test_rescorer_topUsers(self):
userID = 'Luciana Nunes'
numUsers = 4
allUserIDs = self.model.UserIDs()
preferenceEstimator = estimateUserUser
rescorer = TanHScorer()
self.assertEquals(
['Maria Gabriela', 'Penny Frewman', 'Steve Gates', 'Lorena Abreu'],
topUsers(userID, allUserIDs, numUsers, preferenceEstimator,
self.similarity, rescorer))
def test_local_not_existing_rescorer_estimatePreference(self):
userID = 'Leopoldo Pires'
itemID = 'You, Me and Dupree'
recSys = ItemRecommender(self.model, self.similarity, self.strategy,
False)
scorer = TanHScorer()
self.assertAlmostEquals(
3.1471787551,
recSys.estimatePreference(userID=userID,
similarity=self.similarity,
itemID=itemID,
rescorer=scorer))
def test_local_not_existing_rescorer_estimatePreference(self):
userID = 'Leopoldo Pires'
itemID = 'You, Me and Dupree'
recSys = UserRecommender(self.model, self.similarity, self.neighbor,
False)
scorer = TanHScorer()
self.assertAlmostEquals(
2.5761016605,
recSys.estimatePreference(userID=userID,
similarity=self.similarity,
itemID=itemID,
rescorer=scorer))
def test_maxUsers_topUsers(self):
userID = 'Luciana Nunes'
numUsers = 9
allUserIDs = self.model.UserIDs()
preferenceEstimator = estimateUserUser
rescorer = TanHScorer()
self.assertEquals([
'Maria Gabriela', 'Penny Frewman', 'Steve Gates', 'Lorena Abreu',
'Marcel Caraciolo', 'Leopoldo Pires', 'Sheldom'
],
topUsers(userID, allUserIDs, numUsers,
preferenceEstimator, self.similarity,
rescorer))
def test_rescorer_UserItem_topItems(self):
userID = 'Leopoldo Pires'
numItems = 4
allItemIDs = self.model.ItemIDs()
preferenceEstimator = estimateUserItem
rescorer = TanHScorer()
n = NearestNUserNeighborhood(self.similarity, self.model, 4, 0.0)
self.assertEquals([
'Lady in the Water', 'You, Me and Dupree', 'Snakes on a Plane',
'Superman Returns'
],
topItems(userID,
allItemIDs,
numItems,
preferenceEstimator,
self.similarity,
rescorer,
model=self.model,
neighborhood=n))