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 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_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 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_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_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_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_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_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_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 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)
