def test_recommend(self):
userID = "Leopoldo Pires"
recSys = SlopeOneRecommender(self.model, True, False, False)
self.assertEquals(["You, Me and Dupree", "Just My Luck"], recSys.recommend(userID, 4))
recSys = SlopeOneRecommender(self.model, True, False, True)
self.assertEquals(["You, Me and Dupree", "Just My Luck"], recSys.recommend(userID, 4))
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.assertEquals(["A"], recSys.recommend("Lucy", 4))
users2 = {
"Amy": {"Dr. Dog": 4, "Lady Gaga": 3, "Phoenix": 4},
"Ben": {"Dr. Dog": 5, "Lady Gaga": 2},
"Clara": {"Lady Gaga": 3.5, "Phoenix": 4},
}
recSys = SlopeOneRecommender(DictDataModel(users2), True, False, False)
self.assertEquals(["Phoenix"], recSys.recommend("Ben", 1))
def test_empty_recommend(self):
userID = "Marcel Caraciolo"
recSys = SlopeOneRecommender(self.model, True, False, False)
self.assertEquals([], recSys.recommend(userID, 4))
recSys = SlopeOneRecommender(self.model, True, False, True)
self.assertEquals([], recSys.recommend(userID, 4))
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_semi_recommend(self):
userID = 'Leopoldo Pires'
recSys = SlopeOneRecommender(self.model, True, False, False)
self.assertEquals(['You, Me and Dupree'], recSys.recommend(userID, 1))
recSys = SlopeOneRecommender(self.model, True, False, True)
self.assertEquals(['You, Me and Dupree'], recSys.recommend(userID, 1))
def test_full_recommend(self):
userID = 'Maria Gabriela'
recSys = SlopeOneRecommender(self.model, True, False, False)
self.assertEquals([], recSys.recommend(userID, 4))
recSys = SlopeOneRecommender(self.model, True, False, True)
self.assertEquals([], recSys.recommend(userID, 4))
def test_limits_AvgDistanceRecSys(self):
evaluator = AverageAbsoluteDifferenceRecommenderEvaluator()
recommender = SlopeOneRecommender(self.model, True, False, False)
self.assertRaises(Exception, evaluator.evaluate, recommender,
self.model, 1.3, -0.3)
def test_semi_recommend(self):
userID = "Leopoldo Pires"
recSys = SlopeOneRecommender(self.model, True, False, False)
self.assertEquals(["You, Me and Dupree"], recSys.recommend(userID, 1))
recSys = SlopeOneRecommender(self.model, True, False, True)
self.assertEquals(["You, Me and Dupree"], recSys.recommend(userID, 1))
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_create_ItemBasedRecommender(self):
recSys = SlopeOneRecommender(self.model, True, True)
self.assertEquals(recSys.weighted, True)
self.assertEquals(recSys.stdDevWeighted, True)
self.assertEquals(recSys.model, self.model)
def test_limits_RMSRecommenderEvaluator(self):
evaluator = RMSRecommenderEvaluator()
recommender = SlopeOneRecommender(self.model, True, False, False)
self.assertRaises(Exception, evaluator.evaluate, recommender,
self.model, 0, -0.3)
def test_Slope_IRStatsRecommenderEvaluator(self):
evaluator = IRStatsRecommenderEvaluator()
recommender = SlopeOneRecommender(self.model, True, False, False)
result = evaluator.evaluate(recommender, self.model, 2, 1.0)
print result
def test_Slope_RMSRecommenderEvaluator(self):
evaluator = RMSRecommenderEvaluator()
recommender = SlopeOneRecommender(self.model, True, False, False)
result = evaluator.evaluate(recommender, self.model, 0.7, 1.0)
def test_empty_recommend(self):
userID = 'Marcel Caraciolo'
recSys = SlopeOneRecommender(self.model, True, False, False)
self.assertEquals([], recSys.recommend(userID, 4))
recSys = SlopeOneRecommender(self.model, True, False, True)
self.assertEquals([], recSys.recommend(userID, 4))
def test_full_recommend(self):
userID = "Maria Gabriela"
recSys = SlopeOneRecommender(self.model, True, False, False)
self.assertEquals([], recSys.recommend(userID, 4))
recSys = SlopeOneRecommender(self.model, True, False, True)
self.assertEquals([], recSys.recommend(userID, 4))
def test_recommend(self):
userID = 'Leopoldo Pires'
recSys = SlopeOneRecommender(self.model, True, False, False)
self.assertEquals(['You, Me and Dupree', 'Just My Luck'],
recSys.recommend(userID, 4))
recSys = SlopeOneRecommender(self.model, True, False, True)
self.assertEquals(['You, Me and Dupree', 'Just My Luck'],
recSys.recommend(userID, 4))
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.assertEquals(['A'], recSys.recommend('Lucy', 4))
users2 = {
"Amy": {
"Dr. Dog": 4,
"Lady Gaga": 3,
"Phoenix": 4
},
"Ben": {
"Dr. Dog": 5,
"Lady Gaga": 2
},
"Clara": {
"Lady Gaga": 3.5,
"Phoenix": 4
}
}
recSys = SlopeOneRecommender(DictDataModel(users2), True, False, False)
self.assertEquals(['Phoenix'], recSys.recommend('Ben', 1))
sys.path.append('/Users/marcelcaraciolo/Desktop/crab/crab/crab')
from models.datamodel import *
from recommender.recommender import SlopeOneRecommender
from recommender.utils import DiffStorage
from evaluation.statistics import *
#Load the dataset
input = open('model.pk1', 'rb')
model = pickle.load(input)
model = DictDataModel(model)
#Instantiate the Evaluator
evaluator = RMSRecommenderEvaluator()
#Instantiate the Slope One Recommender
recommender = SlopeOneRecommender(model, True, False, False)
#Calculate the Root Mean Squared Error
rmse = evaluator.evaluate(recommender, model, 0.7, 1.0)
#Instantiate the IR Stats Recommender Evaluator
evaluator = IRStatsRecommenderEvaluator()
#Calculate the Precision, Recall and F1Score
result = evaluator.evaluate(recommender, model, 25, 1.0)
#1.04078332386 0.17222222222222 0.1722222222222 0.17222222222222
print rmse, result['precision'], result['recall'], result['f1Score']
#For plotting the graph.
import scipy as sc
def test_Slope_AvgDistanceRecSys(self):
evaluator = AverageAbsoluteDifferenceRecommenderEvaluator()
recommender = SlopeOneRecommender(self.model, True, False, False)
result = evaluator.evaluate(recommender, self.model, 0.7, 1.0)
