def test_RandomRecommender(self):
backend = [config_global.SAVE_RANDOM_RECOMMENDER]
self.saveJson(backend)
fb = Random_Recommender()
additional_filter_1 = {'domainid': 'domain1'}
fb.train(userid, additional_filter_1) # train for the specific user and the filter
resultSet_1 = fb.get_recommendation(userid, additional_filter_1, N=N, remove=False)
def test_Get_Recommendation_userid_disctinction(self):
N = 3
fb = Random_Recommender( )
additional_filter_1 = { 'domainid' : 'domain1' }
userid1 = 123
self.insertRecommendables(additional_filter_1, 0, 10)
fb.train( userid1, additional_filter_1 ) # train for the specific user and the filter
userid2 = 456
self.insertRecommendables(additional_filter_1, 0, 10)
fb.train( userid2, additional_filter_1 ) # train for the specific user and the filter
resultSet_1 = fb.get_recommendation( userid1, additional_filter_1, N=N, remove = False )
if self.debug: print resultSet_1
self.assertEqual(len(resultSet_1), N, 'the resulting recommendation have the wrong number')
resultSet_2 = fb.get_recommendation( userid2, additional_filter_1, N=N, remove = False )
if self.debug: print resultSet_2
self.assertEqual(len(resultSet_2), N, 'the resulting recommendation have the wrong number')
def testSimpleMergeList(self):
fb = Random_Recommender( )
additional_filter_1 = { 'domainid' : 'domain1' }
userid = 123
N = 3
resultSet_1 = fb.get_recommendation( userid, additional_filter_1, N=N, remove = False, ranked = True )
if self.debug: print resultSet_1
resultSet_2 = fb.get_recommendation( userid, additional_filter_1, N=N, remove = False, ranked = True )
if self.debug: print resultSet_2
slm = SimpleListMerge()
slm.add('a', resultSet_1)
slm.add('b', resultSet_2)
unchanged_list = slm.merge_naive( {'a':0.5, 'b' : 0.5} )
self.assertEqual(unchanged_list, resultSet_1, "merging two same lists results the same list")
def test_Get_Recommendation_with_multiple_constraints(self):
userid = 123
N = 3
fb = Random_Recommender( )
additional_filter_1 = { 'domainid' : 'domain1', 'channelid' : 'channel1' }
self.insertRecommendables(additional_filter_1, 0, 10)
fb.train( userid, additional_filter_1 ) # train for the specific user and the filter
resultSet_1 = fb.get_recommendation(userid, additional_filter_1, N=N, remove = False )
if self.debug: print resultSet_1
self.assertEqual(len(resultSet_1), N, 'the resulting recommendation have the wrong number')
def test_Get_Recommendation_with_constraint(self):
userid = 123
N = 3
fb = Random_Recommender( )
additional_filter_1 = { 'domainid' : 'domain1' }
self.insertRecommendables(additional_filter_1, 0, 10)
additional_filter_2 = { 'domainid' : 'domain2' }
self.insertRecommendables(additional_filter_2, 20, 30)
self.insertRecommendables({}, 20, 30)
fb.train( userid, additional_filter_1 ) # train for the specific user and the filter
resultSet_1 = fb.get_recommendation( userid, additional_filter_1, N=N, remove = False )
if self.debug: print resultSet_1
self.assertEqual(len(resultSet_1), N, 'the resulting recommendation have the wrong number')
resultSet_2 = fb.get_recommendation( userid, additional_filter_2, N=N, remove = False )
self.assertEqual(len(resultSet_2), 0, 'the resulting recommendation have the wrong number')
resultSet_new = fb.get_recommendation( userid, {}, N=N, remove = True )
self.assertEqual(len(resultSet_new), 0, 'the resulting recommendation have the wrong number')
fb.train( userid, {} ) # train for the specific user but with no constraint, meaning we can recommend everything
resultSet_new = fb.get_recommendation( userid, {}, N=N, remove = True, ranked=True )
if self.debug: print resultSet_new
self.assertEqual(len(resultSet_new), N, 'the resulting recommendation have the wrong number')
resultSet_new_1 = fb.get_recommendation( userid, {}, N=N, remove = True, ranked=True )
if self.debug: print resultSet_new_1
self.assertEqual(len(resultSet_new_1), N, 'the resulting recommendation have the wrong number')
self.assertNotEqual(resultSet_new_1,resultSet_new, 'items were not removed from the recommendation Set')
def test_Get_Recommendation_Ranked(self):
userid = 123
N = 3
fb = Random_Recommender( )
additional_filter_1 = { 'domainid' : 'domain1' }
self.insertRecommendables(additional_filter_1, 0, 10)
additional_filter_2 = { 'domainid' : 'domain2' }
self.insertRecommendables(additional_filter_2, 20, 30)
self.insertRecommendables({}, 20, 30)
fb.train( userid, additional_filter_1 ) # train for the specific user and the filter
resultSet_1 = fb.get_recommendation( userid, additional_filter_1, N=N, remove = False, ranked = True )
if self.debug: print resultSet_1
self.assertEqual(len(resultSet_1), N, 'the resulting recommendation have the wrong number')
self.backends = backends
fullParsedDataModel = FullContestMessageParser()
fullParsedDataModel.parse(json_string)
domain_id = fullParsedDataModel.domain_id
user_id = fullParsedDataModel.user_id
# now compile the constraints, which are important for an onsite Recommender: do not recommend item from another domain
constraints = {'domainid': domain_id}
random_recommender = Random_Recommender()
N = 4
# TODO initialize a new training session if necessary
random_recommender.train(user_id, constraints)
self.resultSet = random_recommender.get_recommendation(user_id, constraints, N=N, remove=True)
def recommend(self):
""" lets hope there are recommendation ready for this user/constraint """
return self.resultSet
