def test_empty_sampling_rate_getSampleUserIDs(self):
numUsers = 4
minSimilarity = 0.0
samplingRate = 0.0
n = NearestNUserNeighborhood(self.similarity, self.model, numUsers,
minSimilarity, samplingRate)
self.assertEquals(0, len(n.getSampleUserIDs()))
def test_identity_estimatePreference(self): numUsers = 4 userID = 'Marcel Caraciolo' otherUserID = 'Marcel Caraciolo' minSimilarity = 0.0 n = NearestNUserNeighborhood(self.similarity,self.model,numUsers,minSimilarity) self.assertEquals(None,n.estimatePreference(thingID=userID,similarity=self.similarity,otherUserID=otherUserID))
def test_user_dissimilar_estimatePreference(self): numUsers = 4 userID = 'Marcel Caraciolo' otherUserID = 'Maria Gabriela' minSimilarity = 0.0 n = NearestNUserNeighborhood(self.similarity,self.model,numUsers,minSimilarity) self.assertAlmostEquals(0.0,n.estimatePreference(thingID=userID,similarity=self.similarity,otherUserID=otherUserID))
def test_estimatePreference(self): numUsers = 4 userID = 'Marcel Caraciolo' otherUserID = 'Luciana Nunes' minSimilarity = 0.0 n = NearestNUserNeighborhood(self.similarity,self.model,numUsers,minSimilarity) self.assertAlmostEquals(0.294298055,n.estimatePreference(thingID=userID,similarity=self.similarity,otherUserID=otherUserID))
def setUp(self):
#SIMILARITY BY RATES.
movies = {
'Marcel Caraciolo': {
'Lady in the Water': 2.5,
'Snakes on a Plane': 3.5,
'Just My Luck': 3.0,
'Superman Returns': 3.5,
'You, Me and Dupree': 2.5,
'The Night Listener': 3.0
},
'Luciana Nunes': {
'Lady in the Water': 3.0,
'Snakes on a Plane': 3.5,
'Just My Luck': 1.5,
'Superman Returns': 5.0,
'The Night Listener': 3.0,
'You, Me and Dupree': 3.5
},
'Leopoldo Pires': {
'Lady in the Water': 2.5,
'Snakes on a Plane': 3.0,
'Superman Returns': 3.5,
'The Night Listener': 4.0
},
'Lorena Abreu': {
'Snakes on a Plane': 3.5,
'Just My Luck': 3.0,
'The Night Listener': 4.5,
'Superman Returns': 4.0,
'You, Me and Dupree': 2.5
},
'Steve Gates': {
'Lady in the Water': 3.0,
'Snakes on a Plane': 4.0,
'Just My Luck': 2.0,
'Superman Returns': 3.0,
'The Night Listener': 3.0,
'You, Me and Dupree': 2.0
},
'Sheldom': {
'Lady in the Water': 3.0,
'Snakes on a Plane': 4.0,
'The Night Listener': 3.0,
'Superman Returns': 5.0,
'You, Me and Dupree': 3.5
},
'Penny Frewman': {
'Snakes on a Plane': 4.5,
'You, Me and Dupree': 1.0,
'Superman Returns': 4.0
},
'Maria Gabriela': {}
}
self.model = DictDataModel(movies)
self.similarity = UserSimilarity(self.model, sim_euclidian)
self.neighbor = NearestNUserNeighborhood(self.similarity, self.model,
4, 0.0)
def test_userNeighborhood(self): numUsers = 4 userID = 'Marcel Caraciolo' minSimilarity = 0.0 scorer = NaiveScorer() self.similarity = UserSimilarity(self.model,sim_tanimoto) n = NearestNUserNeighborhood(self.similarity,self.model,numUsers,minSimilarity) self.assertEquals(['Luciana Nunes', 'Steve Gates', 'Lorena Abreu', 'Sheldom'],n.userNeighborhood(userID,scorer))
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_userNeighborhood(self):
numUsers = 4
userID = 'Marcel Caraciolo'
minSimilarity = 0.0
scorer = NaiveScorer()
self.similarity = UserSimilarity(self.model, sim_tanimoto)
n = NearestNUserNeighborhood(self.similarity, self.model, numUsers,
minSimilarity)
self.assertEquals(
['Luciana Nunes', 'Steve Gates', 'Lorena Abreu', 'Sheldom'],
n.userNeighborhood(userID, scorer))
def test_identity_estimatePreference(self):
numUsers = 4
userID = 'Marcel Caraciolo'
otherUserID = 'Marcel Caraciolo'
minSimilarity = 0.0
n = NearestNUserNeighborhood(self.similarity, self.model, numUsers,
minSimilarity)
self.assertEquals(
None,
n.estimatePreference(thingID=userID,
similarity=self.similarity,
otherUserID=otherUserID))
def test_estimatePreference(self):
numUsers = 4
userID = 'Marcel Caraciolo'
otherUserID = 'Luciana Nunes'
minSimilarity = 0.0
n = NearestNUserNeighborhood(self.similarity, self.model, numUsers,
minSimilarity)
self.assertAlmostEquals(
0.294298055,
n.estimatePreference(thingID=userID,
similarity=self.similarity,
otherUserID=otherUserID))
def test_user_dissimilar_estimatePreference(self):
numUsers = 4
userID = 'Marcel Caraciolo'
otherUserID = 'Maria Gabriela'
minSimilarity = 0.0
n = NearestNUserNeighborhood(self.similarity, self.model, numUsers,
minSimilarity)
self.assertAlmostEquals(
0.0,
n.estimatePreference(thingID=userID,
similarity=self.similarity,
otherUserID=otherUserID))
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_invalid_UserID_userNeighborhood(self):
numUsers = 4
userID = 'Marcel'
minSimilarity = 0.0
scorer = NaiveScorer()
self.similarity = UserSimilarity(self.model, sim_tanimoto)
n = NearestNUserNeighborhood(self.similarity, self.model, numUsers,
minSimilarity)
self.assertRaises(ValueError, n.userNeighborhood, userID, scorer)
def test_create_nearestNUserNeighborhood(self):
numUsers = 4
minSimilarity = 0.5
samplingRate = 0.4
n = NearestNUserNeighborhood(self.similarity, self.model, numUsers,
minSimilarity, samplingRate)
self.assertEquals(n.similarity, self.similarity)
self.assertEquals(n.samplingRate, samplingRate)
self.assertEquals(n.numUsers, numUsers)
self.assertEquals(self.model, n.model)
def setUp(self):
# SIMILARITY BY RATES.
movies = {
"Marcel Caraciolo": {
"Lady in the Water": 2.5,
"Snakes on a Plane": 3.5,
"Just My Luck": 3.0,
"Superman Returns": 3.5,
"You, Me and Dupree": 2.5,
"The Night Listener": 3.0,
},
"Luciana Nunes": {
"Lady in the Water": 3.0,
"Snakes on a Plane": 3.5,
"Just My Luck": 1.5,
"Superman Returns": 5.0,
"The Night Listener": 3.0,
"You, Me and Dupree": 3.5,
},
"Leopoldo Pires": {
"Lady in the Water": 2.5,
"Snakes on a Plane": 3.0,
"Superman Returns": 3.5,
"The Night Listener": 4.0,
},
"Lorena Abreu": {
"Snakes on a Plane": 3.5,
"Just My Luck": 3.0,
"The Night Listener": 4.5,
"Superman Returns": 4.0,
"You, Me and Dupree": 2.5,
},
"Steve Gates": {
"Lady in the Water": 3.0,
"Snakes on a Plane": 4.0,
"Just My Luck": 2.0,
"Superman Returns": 3.0,
"The Night Listener": 3.0,
"You, Me and Dupree": 2.0,
},
"Sheldom": {
"Lady in the Water": 3.0,
"Snakes on a Plane": 4.0,
"The Night Listener": 3.0,
"Superman Returns": 5.0,
"You, Me and Dupree": 3.5,
},
"Penny Frewman": {"Snakes on a Plane": 4.5, "You, Me and Dupree": 1.0, "Superman Returns": 4.0},
"Maria Gabriela": {},
}
self.model = DictDataModel(movies)
self.similarity = UserSimilarity(self.model, sim_euclidian)
self.neighbor = NearestNUserNeighborhood(self.similarity, self.model, 4, 0.0)
def test_minItems_UserItem_topItems(self):
userID = 'Leopoldo Pires'
numItems = 0
allItemIDs = self.model.ItemIDs()
preferenceEstimator = estimateUserItem
n = NearestNUserNeighborhood(self.similarity, self.model, 4, 0.0)
self.assertEquals([],
topItems(
userID,
allItemIDs,
numItems,
preferenceEstimator,
self.similarity,
None,
model=self.model,
neighborhood=n,
))
def test_UserItem_topItems(self):
userID = 'Leopoldo Pires'
numItems = 4
allItemIDs = self.model.ItemIDs()
preferenceEstimator = estimateUserItem
rescorer = NaiveScorer()
n = NearestNUserNeighborhood(self.similarity, self.model, 4, 0.0)
self.assertEquals([
'The Night Listener', 'Superman Returns', 'Snakes on a Plane',
'Just My Luck'
],
topItems(userID,
allItemIDs,
numItems,
preferenceEstimator,
self.similarity,
None,
model=self.model,
neighborhood=n))
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))
def test_maxItems_UserItem_topItems(self):
userID = 'Leopoldo Pires'
numItems = 9
allItemIDs = self.model.ItemIDs()
preferenceEstimator = estimateUserItem
n = NearestNUserNeighborhood(self.similarity, self.model, 4, 0.0)
self.assertEquals([
'The Night Listener', 'Superman Returns', 'Snakes on a Plane',
'Just My Luck', 'Lady in the Water', 'You, Me and Dupree'
],
topItems(
userID,
allItemIDs,
numItems,
preferenceEstimator,
self.similarity,
None,
model=self.model,
neighborhood=n,
))
def setUp(self):
#SIMILARITY BY RATES.
movies={'Marcel Caraciolo': {'Lady in the Water': 2.5, 'Snakes on a Plane': 3.5,
'Just My Luck': 3.0, 'Superman Returns': 3.5, 'You, Me and Dupree': 2.5,
'The Night Listener': 3.0},
'Luciana Nunes': {'Lady in the Water': 3.0, 'Snakes on a Plane': 3.5,
'Just My Luck': 1.5, 'Superman Returns': 5.0, 'The Night Listener': 3.0,
'You, Me and Dupree': 3.5},
'Leopoldo Pires': {'Lady in the Water': 2.5, 'Snakes on a Plane': 3.0,
'Superman Returns': 3.5, 'The Night Listener': 4.0},
'Lorena Abreu': {'Snakes on a Plane': 3.5, 'Just My Luck': 3.0,
'The Night Listener': 4.5, 'Superman Returns': 4.0,
'You, Me and Dupree': 2.5},
'Steve Gates': {'Lady in the Water': 3.0, 'Snakes on a Plane': 4.0,
'Just My Luck': 2.0, 'Superman Returns': 3.0, 'The Night Listener': 3.0,
'You, Me and Dupree': 2.0},
'Sheldom': {'Lady in the Water': 3.0, 'Snakes on a Plane': 4.0,
'The Night Listener': 3.0, 'Superman Returns': 5.0, 'You, Me and Dupree': 3.5},
'Penny Frewman': {'Snakes on a Plane':4.5,'You, Me and Dupree':1.0,'Superman Returns':4.0},
'Maria Gabriela': {}}
self.model = DictDataModel(movies)
self.similarity = UserSimilarity(self.model,sim_euclidian)
self.neighbor = NearestNUserNeighborhood(self.similarity,self.model,4,0.0)
def test_min_numUsers_getSampleUserIDs(self): numUsers = 4 minSimilarity = 0.0 n = NearestNUserNeighborhood(self.similarity,self.model,numUsers,minSimilarity) self.assertEquals(8,len(n.getSampleUserIDs()))
def test_empty_sampling_rate_getSampleUserIDs(self): numUsers = 4 minSimilarity = 0.0 samplingRate = 0.0 n = NearestNUserNeighborhood(self.similarity,self.model,numUsers,minSimilarity,samplingRate) self.assertEquals(0,len(n.getSampleUserIDs()))
def test_min_numUsers_getSampleUserIDs(self):
numUsers = 4
minSimilarity = 0.0
n = NearestNUserNeighborhood(self.similarity, self.model, numUsers,
minSimilarity)
self.assertEquals(8, len(n.getSampleUserIDs()))
def test_maximum_limit_getSampleUserIDs(self):
numUsers = 9
minSimilarity = 0.0
n = NearestNUserNeighborhood(self.similarity, self.model, numUsers,
minSimilarity)
self.assertEquals(8, n.numUsers)
class TestUserBasedRecommender(unittest.TestCase):
def setUp(self):
# SIMILARITY BY RATES.
movies = {
"Marcel Caraciolo": {
"Lady in the Water": 2.5,
"Snakes on a Plane": 3.5,
"Just My Luck": 3.0,
"Superman Returns": 3.5,
"You, Me and Dupree": 2.5,
"The Night Listener": 3.0,
},
"Luciana Nunes": {
"Lady in the Water": 3.0,
"Snakes on a Plane": 3.5,
"Just My Luck": 1.5,
"Superman Returns": 5.0,
"The Night Listener": 3.0,
"You, Me and Dupree": 3.5,
},
"Leopoldo Pires": {
"Lady in the Water": 2.5,
"Snakes on a Plane": 3.0,
"Superman Returns": 3.5,
"The Night Listener": 4.0,
},
"Lorena Abreu": {
"Snakes on a Plane": 3.5,
"Just My Luck": 3.0,
"The Night Listener": 4.5,
"Superman Returns": 4.0,
"You, Me and Dupree": 2.5,
},
"Steve Gates": {
"Lady in the Water": 3.0,
"Snakes on a Plane": 4.0,
"Just My Luck": 2.0,
"Superman Returns": 3.0,
"The Night Listener": 3.0,
"You, Me and Dupree": 2.0,
},
"Sheldom": {
"Lady in the Water": 3.0,
"Snakes on a Plane": 4.0,
"The Night Listener": 3.0,
"Superman Returns": 5.0,
"You, Me and Dupree": 3.5,
},
"Penny Frewman": {"Snakes on a Plane": 4.5, "You, Me and Dupree": 1.0, "Superman Returns": 4.0},
"Maria Gabriela": {},
}
self.model = DictDataModel(movies)
self.similarity = UserSimilarity(self.model, sim_euclidian)
self.neighbor = NearestNUserNeighborhood(self.similarity, self.model, 4, 0.0)
def test_create_UserBasedRecommender(self):
recSys = UserRecommender(self.model, self.similarity, self.neighbor, True)
self.assertEquals(recSys.similarity, self.similarity)
self.assertEquals(recSys.capper, True)
self.assertEquals(recSys.neighborhood, self.neighbor)
self.assertEquals(recSys.model, self.model)
def test_all_watched_allOtherItems(self):
userID = "Luciana Nunes"
recSys = UserRecommender(self.model, self.similarity, self.neighbor, True)
nearestN = self.neighbor.userNeighborhood(userID)
self.assertEquals([], recSys.allOtherItems(userID, nearestN))
def test_semi_watched_allOtherItems(self):
userID = "Leopoldo Pires"
recSys = UserRecommender(self.model, self.similarity, self.neighbor, True)
nearestN = self.neighbor.userNeighborhood(userID)
self.assertEquals(["Just My Luck", "You, Me and Dupree"], recSys.allOtherItems(userID, nearestN))
def test_non_watched_allOtherItems(self):
userID = "Maria Gabriela"
recSys = UserRecommender(self.model, self.similarity, self.neighbor, True)
nearestN = self.neighbor.userNeighborhood(userID)
self.assertEquals(
[
"Lady in the Water",
"Snakes on a Plane",
"Just My Luck",
"Superman Returns",
"You, Me and Dupree",
"The Night Listener",
],
recSys.allOtherItems(userID, nearestN),
)
def test_mostSimilarUserIDs(self):
userID = "Marcel Caraciolo"
recSys = UserRecommender(self.model, self.similarity, self.neighbor, True)
self.assertEquals(
["Leopoldo Pires", "Steve Gates", "Lorena Abreu", "Penny Frewman"], recSys.mostSimilarUserIDs(userID, 4)
)
def test_user_no_preference_mostSimilarUserIDs(self):
userID = "Maria Gabriela"
recSys = UserRecommender(self.model, self.similarity, self.neighbor, True)
self.assertEquals(
["Leopoldo Pires", "Lorena Abreu", "Luciana Nunes", "Marcel Caraciolo"],
recSys.mostSimilarUserIDs(userID, 4),
)
def test_empty_mostSimilarUserIDs(self):
userID = "Maria Gabriela"
recSys = UserRecommender(self.model, self.similarity, self.neighbor, True)
self.assertEquals([], recSys.mostSimilarUserIDs(userID, 0))
def test_local_estimatePreference(self):
userID = "Marcel Caraciolo"
itemID = "Superman Returns"
recSys = UserRecommender(self.model, self.similarity, self.neighbor, True)
self.assertAlmostEquals(
3.5, recSys.estimatePreference(userID=userID, similarity=self.similarity, itemID=itemID)
)
def test_local_not_existing_estimatePreference(self):
userID = "Leopoldo Pires"
itemID = "You, Me and Dupree"
recSys = UserRecommender(self.model, self.similarity, self.neighbor, True)
self.assertAlmostEquals(
2.065394689, recSys.estimatePreference(userID=userID, similarity=self.similarity, itemID=itemID)
)
def test_local_not_existing_capper_False_estimatePreference(self):
userID = "Leopoldo Pires"
itemID = "You, Me and Dupree"
recSys = UserRecommender(self.model, self.similarity, self.neighbor, False)
self.assertAlmostEquals(
2.065394689, recSys.estimatePreference(userID=userID, similarity=self.similarity, itemID=itemID)
)
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_recommend(self):
userID = "Leopoldo Pires"
recSys = UserRecommender(self.model, self.similarity, self.neighbor, False)
self.assertEquals(["Just My Luck", "You, Me and Dupree"], recSys.recommend(userID, 4))
def test_empty_recommend(self):
userID = "Marcel Caraciolo"
recSys = UserRecommender(self.model, self.similarity, self.neighbor, False)
self.assertEquals([], recSys.recommend(userID, 4))
def test_full_recommend(self):
userID = "Maria Gabriela"
recSys = UserRecommender(self.model, self.similarity, self.neighbor, False)
self.assertEquals([], recSys.recommend(userID, 4))
def test_semi_recommend(self):
userID = "Leopoldo Pires"
recSys = UserRecommender(self.model, self.similarity, self.neighbor, False)
self.assertEquals(["Just My Luck"], recSys.recommend(userID, 1))
class TestUserBasedRecommender(unittest.TestCase):
def setUp(self):
#SIMILARITY BY RATES.
movies = {
'Marcel Caraciolo': {
'Lady in the Water': 2.5,
'Snakes on a Plane': 3.5,
'Just My Luck': 3.0,
'Superman Returns': 3.5,
'You, Me and Dupree': 2.5,
'The Night Listener': 3.0
},
'Luciana Nunes': {
'Lady in the Water': 3.0,
'Snakes on a Plane': 3.5,
'Just My Luck': 1.5,
'Superman Returns': 5.0,
'The Night Listener': 3.0,
'You, Me and Dupree': 3.5
},
'Leopoldo Pires': {
'Lady in the Water': 2.5,
'Snakes on a Plane': 3.0,
'Superman Returns': 3.5,
'The Night Listener': 4.0
},
'Lorena Abreu': {
'Snakes on a Plane': 3.5,
'Just My Luck': 3.0,
'The Night Listener': 4.5,
'Superman Returns': 4.0,
'You, Me and Dupree': 2.5
},
'Steve Gates': {
'Lady in the Water': 3.0,
'Snakes on a Plane': 4.0,
'Just My Luck': 2.0,
'Superman Returns': 3.0,
'The Night Listener': 3.0,
'You, Me and Dupree': 2.0
},
'Sheldom': {
'Lady in the Water': 3.0,
'Snakes on a Plane': 4.0,
'The Night Listener': 3.0,
'Superman Returns': 5.0,
'You, Me and Dupree': 3.5
},
'Penny Frewman': {
'Snakes on a Plane': 4.5,
'You, Me and Dupree': 1.0,
'Superman Returns': 4.0
},
'Maria Gabriela': {}
}
self.model = DictDataModel(movies)
self.similarity = UserSimilarity(self.model, sim_euclidian)
self.neighbor = NearestNUserNeighborhood(self.similarity, self.model,
4, 0.0)
def test_create_UserBasedRecommender(self):
recSys = UserRecommender(self.model, self.similarity, self.neighbor,
True)
self.assertEquals(recSys.similarity, self.similarity)
self.assertEquals(recSys.capper, True)
self.assertEquals(recSys.neighborhood, self.neighbor)
self.assertEquals(recSys.model, self.model)
def test_all_watched_allOtherItems(self):
userID = 'Luciana Nunes'
recSys = UserRecommender(self.model, self.similarity, self.neighbor,
True)
nearestN = self.neighbor.userNeighborhood(userID)
self.assertEquals([], recSys.allOtherItems(userID, nearestN))
def test_semi_watched_allOtherItems(self):
userID = 'Leopoldo Pires'
recSys = UserRecommender(self.model, self.similarity, self.neighbor,
True)
nearestN = self.neighbor.userNeighborhood(userID)
self.assertEquals(['Just My Luck', 'You, Me and Dupree'],
recSys.allOtherItems(userID, nearestN))
def test_non_watched_allOtherItems(self):
userID = 'Maria Gabriela'
recSys = UserRecommender(self.model, self.similarity, self.neighbor,
True)
nearestN = self.neighbor.userNeighborhood(userID)
self.assertEquals([
'Lady in the Water', 'Snakes on a Plane', 'Just My Luck',
'Superman Returns', 'You, Me and Dupree', 'The Night Listener'
], recSys.allOtherItems(userID, nearestN))
def test_mostSimilarUserIDs(self):
userID = 'Marcel Caraciolo'
recSys = UserRecommender(self.model, self.similarity, self.neighbor,
True)
self.assertEquals(
['Leopoldo Pires', 'Steve Gates', 'Lorena Abreu', 'Penny Frewman'],
recSys.mostSimilarUserIDs(userID, 4))
def test_user_no_preference_mostSimilarUserIDs(self):
userID = 'Maria Gabriela'
recSys = UserRecommender(self.model, self.similarity, self.neighbor,
True)
self.assertEquals([
'Leopoldo Pires', 'Lorena Abreu', 'Luciana Nunes',
'Marcel Caraciolo'
], recSys.mostSimilarUserIDs(userID, 4))
def test_empty_mostSimilarUserIDs(self):
userID = 'Maria Gabriela'
recSys = UserRecommender(self.model, self.similarity, self.neighbor,
True)
self.assertEquals([], recSys.mostSimilarUserIDs(userID, 0))
def test_local_estimatePreference(self):
userID = 'Marcel Caraciolo'
itemID = 'Superman Returns'
recSys = UserRecommender(self.model, self.similarity, self.neighbor,
True)
self.assertAlmostEquals(
3.5,
recSys.estimatePreference(userID=userID,
similarity=self.similarity,
itemID=itemID))
def test_local_not_existing_estimatePreference(self):
userID = 'Leopoldo Pires'
itemID = 'You, Me and Dupree'
recSys = UserRecommender(self.model, self.similarity, self.neighbor,
True)
self.assertAlmostEquals(
2.065394689,
recSys.estimatePreference(userID=userID,
similarity=self.similarity,
itemID=itemID))
def test_local_not_existing_capper_False_estimatePreference(self):
userID = 'Leopoldo Pires'
itemID = 'You, Me and Dupree'
recSys = UserRecommender(self.model, self.similarity, self.neighbor,
False)
self.assertAlmostEquals(
2.065394689,
recSys.estimatePreference(userID=userID,
similarity=self.similarity,
itemID=itemID))
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_recommend(self):
userID = 'Leopoldo Pires'
recSys = UserRecommender(self.model, self.similarity, self.neighbor,
False)
self.assertEquals(['Just My Luck', 'You, Me and Dupree'],
recSys.recommend(userID, 4))
def test_empty_recommend(self):
userID = 'Marcel Caraciolo'
recSys = UserRecommender(self.model, self.similarity, self.neighbor,
False)
self.assertEquals([], recSys.recommend(userID, 4))
def test_full_recommend(self):
userID = 'Maria Gabriela'
recSys = UserRecommender(self.model, self.similarity, self.neighbor,
False)
self.assertEquals([], recSys.recommend(userID, 4))
def test_semi_recommend(self):
userID = 'Leopoldo Pires'
recSys = UserRecommender(self.model, self.similarity, self.neighbor,
False)
self.assertEquals(['Just My Luck'], recSys.recommend(userID, 1))
class TestUserBasedRecommender(unittest.TestCase):
def setUp(self):
#SIMILARITY BY RATES.
movies={'Marcel Caraciolo': {'Lady in the Water': 2.5, 'Snakes on a Plane': 3.5,
'Just My Luck': 3.0, 'Superman Returns': 3.5, 'You, Me and Dupree': 2.5,
'The Night Listener': 3.0},
'Luciana Nunes': {'Lady in the Water': 3.0, 'Snakes on a Plane': 3.5,
'Just My Luck': 1.5, 'Superman Returns': 5.0, 'The Night Listener': 3.0,
'You, Me and Dupree': 3.5},
'Leopoldo Pires': {'Lady in the Water': 2.5, 'Snakes on a Plane': 3.0,
'Superman Returns': 3.5, 'The Night Listener': 4.0},
'Lorena Abreu': {'Snakes on a Plane': 3.5, 'Just My Luck': 3.0,
'The Night Listener': 4.5, 'Superman Returns': 4.0,
'You, Me and Dupree': 2.5},
'Steve Gates': {'Lady in the Water': 3.0, 'Snakes on a Plane': 4.0,
'Just My Luck': 2.0, 'Superman Returns': 3.0, 'The Night Listener': 3.0,
'You, Me and Dupree': 2.0},
'Sheldom': {'Lady in the Water': 3.0, 'Snakes on a Plane': 4.0,
'The Night Listener': 3.0, 'Superman Returns': 5.0, 'You, Me and Dupree': 3.5},
'Penny Frewman': {'Snakes on a Plane':4.5,'You, Me and Dupree':1.0,'Superman Returns':4.0},
'Maria Gabriela': {}}
self.model = DictDataModel(movies)
self.similarity = UserSimilarity(self.model,sim_euclidian)
self.neighbor = NearestNUserNeighborhood(self.similarity,self.model,4,0.0)
def test_create_UserBasedRecommender(self):
recSys = UserRecommender(self.model,self.similarity,self.neighbor,True)
self.assertEquals(recSys.similarity,self.similarity)
self.assertEquals(recSys.capper,True)
self.assertEquals(recSys.neighborhood,self.neighbor)
self.assertEquals(recSys.model,self.model)
def test_all_watched_allOtherItems(self):
userID = 'Luciana Nunes'
recSys = UserRecommender(self.model,self.similarity,self.neighbor,True)
nearestN = self.neighbor.userNeighborhood(userID)
self.assertEquals([],recSys.allOtherItems(userID,nearestN))
def test_semi_watched_allOtherItems(self):
userID = 'Leopoldo Pires'
recSys = UserRecommender(self.model,self.similarity,self.neighbor,True)
nearestN = self.neighbor.userNeighborhood(userID)
self.assertEquals(['Just My Luck', 'You, Me and Dupree'],recSys.allOtherItems(userID,nearestN))
def test_non_watched_allOtherItems(self):
userID = 'Maria Gabriela'
recSys = UserRecommender(self.model,self.similarity,self.neighbor,True)
nearestN = self.neighbor.userNeighborhood(userID)
self.assertEquals(['Lady in the Water', 'Snakes on a Plane', 'Just My Luck', 'Superman Returns',
'You, Me and Dupree', 'The Night Listener'],recSys.allOtherItems(userID,nearestN))
def test_mostSimilarUserIDs(self):
userID = 'Marcel Caraciolo'
recSys = UserRecommender(self.model,self.similarity,self.neighbor,True)
self.assertEquals(['Leopoldo Pires', 'Steve Gates', 'Lorena Abreu', 'Penny Frewman'],recSys.mostSimilarUserIDs(userID,4))
def test_user_no_preference_mostSimilarUserIDs(self):
userID = 'Maria Gabriela'
recSys = UserRecommender(self.model,self.similarity,self.neighbor,True)
self.assertEquals(['Leopoldo Pires', 'Lorena Abreu', 'Luciana Nunes', 'Marcel Caraciolo'],recSys.mostSimilarUserIDs(userID,4))
def test_empty_mostSimilarUserIDs(self):
userID = 'Maria Gabriela'
recSys = UserRecommender(self.model,self.similarity,self.neighbor,True)
self.assertEquals([],recSys.mostSimilarUserIDs(userID,0))
def test_local_estimatePreference(self):
userID = 'Marcel Caraciolo'
itemID = 'Superman Returns'
recSys = UserRecommender(self.model,self.similarity,self.neighbor,True)
self.assertAlmostEquals(3.5,recSys.estimatePreference(userID=userID,similarity=self.similarity,itemID=itemID))
def test_local_not_existing_estimatePreference(self):
userID = 'Leopoldo Pires'
itemID = 'You, Me and Dupree'
recSys = UserRecommender(self.model,self.similarity,self.neighbor,True)
self.assertAlmostEquals(2.065394689,recSys.estimatePreference(userID=userID,similarity=self.similarity,itemID=itemID))
def test_local_not_existing_capper_False_estimatePreference(self):
userID = 'Leopoldo Pires'
itemID = 'You, Me and Dupree'
recSys = UserRecommender(self.model,self.similarity,self.neighbor,False)
self.assertAlmostEquals(2.065394689,recSys.estimatePreference(userID=userID,similarity=self.similarity,itemID=itemID))
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_recommend(self):
userID = 'Leopoldo Pires'
recSys = UserRecommender(self.model,self.similarity,self.neighbor,False)
self.assertEquals(['Just My Luck', 'You, Me and Dupree'],recSys.recommend(userID,4))
def test_empty_recommend(self):
userID = 'Marcel Caraciolo'
recSys = UserRecommender(self.model,self.similarity,self.neighbor,False)
self.assertEquals([],recSys.recommend(userID,4))
def test_full_recommend(self):
userID = 'Maria Gabriela'
recSys = UserRecommender(self.model,self.similarity,self.neighbor,False)
self.assertEquals([],recSys.recommend(userID,4))
def test_semi_recommend(self):
userID = 'Leopoldo Pires'
recSys = UserRecommender(self.model,self.similarity,self.neighbor,False)
self.assertEquals(['Just My Luck'],recSys.recommend(userID,1))
