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))