def test_FriendSuggestion_have_direction(self):
train_uu = matrix2dataframe(train_mat_yes)
train_ui = raw2std(train_user_item)
result = RecommendSocial(train_uu, train_ui, N=4)
print(result)
self.assertEqual(result["user1"][0], 7)
self.assertEqual(result["user9"][3], 7)
def test_recommend_cold_start(self):
train = raw2std(train1)
cold_start_items = []
for i in train.index:
if train.ix[i].any() == 0:
cold_start_items.append(i)
result = RecommendRandom(cold_start_items, 3)
self.assertEqual(len(result), 3)
self.assertGreater(result[1], 4)
def test_PersonalRank(self):
train = {
# 1 2 3 4 5 6 7 8 9 10
"user1": [1, 1, 1, 1, 0, 0, 1, 0, 0, 1],
"user2": [1, 0, 0, 0, 1, 0, 1, 0, 1, 1],
"user3": [0, 1, 1, 1, 0, 1, 0, 0, 0, 0],
"user4": [0, 1, 1, 0, 1, 0, 1, 0, 1, 0],
"user5": [1, 1, 1, 0, 1, 0, 0, 1, 0, 1]
}
std_index = [
"item1", "item2", "item3", "item4", "item5", "item6", "item7",
"item8", "item9", "item10"
]
train = raw2std(train, index=std_index)
rank = PersonalRank(train, p=0.8, repeat_times=10000)
self.assertAlmostEqual(rank["user1"]["item1"], 0)
#注意，随机游走算法的结果会变化，但是随着训练次数的增多，理论上应该不会错
self.assertGreater(rank["user1"]["item5"], 0.4)
recommend = FilterAndSort(train, rank)
self.assertEqual(recommend["user1"][0], "item5")
"user2": ["item1", "item4", "item5"],
"user3": ["item2", "item6", "item7"],
"user4": ["item3", "item4", "item6"],
"user5": ["item5", "item6", "item7"]
}
raw_data2 = {
"user1": [1, 1, 1, 0, 0, 0, 0],
"user2": [1, 0, 0, 1, 1, 0, 0],
"user3": [0, 1, 0, 0, 0, 1, 1],
"user4": [0, 0, 1, 1, 0, 1, 0],
"user5": [0, 0, 0, 0, 1, 1, 1]
}
std_index = ["item1", "item2", "item3", "item4", "item5", "item6", "item7"]
std_data = raw2std(raw_data2, index=std_index)
'''
class SimilarityTest(unittest.TestCase):
def test_usersimilarity(self):
train = std_data
similarity = UserSimilarityCF(train)
self.assertEqual(similarity["user1"]["user5"], 0)
self.assertAlmostEqual(similarity["user1"]["user2"], 0.3333333)
def test_usersimilarity_down_hot(self):
train = {
# 1 2 3 4 5 6 7 8 9 10
"user1": [1, 1, 1, 1, 0, 0, 1, 0, 0, 1],
"user2": [1, 0, 0, 0, 1, 0, 1, 0, 1, 1],
"user3": [0, 1, 1, 1, 0, 1, 0, 0, 0, 0],
def test_FriendSuggestion_no_direction(self):
train_uu = graph2dataframe(raw_data_no)
train_ui = raw2std(train_user_item)
result = RecommendSocial(train_uu, train_ui, N=4)
self.assertEqual(result["user1"][0], 7)
self.assertEqual(result["user9"][3], 7)
def test_most_hot(self):
train = raw2std(train1)
result = RecommendMostHotEver(train)
self.assertEqual(result[0], 1)