def test_local_not_existing_estimatePreference(self):
userID = "Leopoldo Pires"
itemID = "You, Me and Dupree"
# Weighted - With Prune
recSys = SlopeOneRecommender(self.model, True, False, True)
self.assertAlmostEquals(2.333333333333, recSys.estimatePreference(userID=userID, itemID=itemID))
# Weighted - No Prune
recSys = SlopeOneRecommender(self.model, True, False, False)
self.assertAlmostEquals(2.333333333333, recSys.estimatePreference(userID=userID, itemID=itemID))
# No Weighted - No Prune
recSys = SlopeOneRecommender(self.model, False, False, False)
self.assertAlmostEquals(2.395833333333, recSys.estimatePreference(userID=userID, itemID=itemID))
# No Weighted - With Prune
recSys = SlopeOneRecommender(self.model, False, False, True)
self.assertAlmostEquals(2.39583333333, recSys.estimatePreference(userID=userID, itemID=itemID))
# Weighted - StdDev - With Prune
recSys = SlopeOneRecommender(self.model, True, True, True)
self.assertAlmostEquals(2.333333333333, recSys.estimatePreference(userID=userID, itemID=itemID))
# Weighted - StdDev - No Prune
recSys = SlopeOneRecommender(self.model, True, True, False)
self.assertAlmostEquals(2.333333333333, recSys.estimatePreference(userID=userID, itemID=itemID))
# Without Prune- Weighted
recSys = SlopeOneRecommender(
DictDataModel(
{"John": {"A": 5.0, "B": 3.0, "C": 2.0}, "Mark": {"A": 3.0, "B": 4.0}, "Lucy": {"B": 2.0, "C": 5.0}}
),
True,
False,
False,
)
self.assertAlmostEquals(4.3333333333333, recSys.estimatePreference(userID="Lucy", itemID="A"))
def test_local_not_existing_estimatePreference(self):
userID = 'Leopoldo Pires'
itemID = 'You, Me and Dupree'
#Weighted - With Prune
recSys = SlopeOneRecommender(self.model, True, False, True)
self.assertAlmostEquals(
2.333333333333,
recSys.estimatePreference(userID=userID, itemID=itemID))
#Weighted - No Prune
recSys = SlopeOneRecommender(self.model, True, False, False)
self.assertAlmostEquals(
2.333333333333,
recSys.estimatePreference(userID=userID, itemID=itemID))
#No Weighted - No Prune
recSys = SlopeOneRecommender(self.model, False, False, False)
self.assertAlmostEquals(
2.395833333333,
recSys.estimatePreference(userID=userID, itemID=itemID))
#No Weighted - With Prune
recSys = SlopeOneRecommender(self.model, False, False, True)
self.assertAlmostEquals(
2.39583333333,
recSys.estimatePreference(userID=userID, itemID=itemID))
#Weighted - StdDev - With Prune
recSys = SlopeOneRecommender(self.model, True, True, True)
self.assertAlmostEquals(
2.333333333333,
recSys.estimatePreference(userID=userID, itemID=itemID))
#Weighted - StdDev - No Prune
recSys = SlopeOneRecommender(self.model, True, True, False)
self.assertAlmostEquals(
2.333333333333,
recSys.estimatePreference(userID=userID, itemID=itemID))
#Without Prune- Weighted
recSys = SlopeOneRecommender(
DictDataModel({
'John': {
'A': 5.0,
'B': 3.0,
'C': 2.0
},
'Mark': {
'A': 3.0,
'B': 4.0
},
'Lucy': {
'B': 2.0,
'C': 5.0
}
}), True, False, False)
self.assertAlmostEquals(
4.3333333333333,
recSys.estimatePreference(userID='Lucy', itemID='A'))
def test_local_estimatePreference(self):
userID = "Marcel Caraciolo"
itemID = "Superman Returns"
recSys = SlopeOneRecommender(self.model, False, False)
self.assertAlmostEquals(3.5, recSys.estimatePreference(userID=userID, itemID=itemID))
def test_local_estimatePreference(self):
userID = 'Marcel Caraciolo'
itemID = 'Superman Returns'
recSys = SlopeOneRecommender(self.model, False, False)
self.assertAlmostEquals(
3.5, recSys.estimatePreference(userID=userID, itemID=itemID))
