class UserKNN(Recommender):
def __init__(self, conf):
super(UserKNN, self).__init__(conf)
super(UserKNN, self).readConfiguration()
self.userSim = SymmetricMatrix(len(self.dao.user))
def readConfiguration(self):
self.sim = self.config['similarity']
self.shrinkage = int(self.config['num.shrinkage'])
self.neighbors = int(self.config['num.neighbors'])
def initModel(self):
self.computeCorr()
def predict(self, u, i):
#find the closest neighbors of user u
topUsers = sorted(self.userSim[u].iteritems(),
key=lambda d: d[1],
reverse=True)
userCount = self.neighbors
if userCount > len(topUsers):
userCount = len(topUsers)
#predict
pred = 0
denom = 0
for n in range(userCount):
#if user n has rating on item i
if self.dao.rating(topUsers[n][0], i) != 0:
corr = topUsers[n][1]
rating = self.dao.rating(topUsers[n][0], i)
pred += corr * rating
denom += topUsers[n][1]
if pred == 0:
#no users have rating on item i,return the average rating of user u
n = self.dao.row(u) > 0
if sum(n[0]) == 0: #no data about current user in training set
return 0
pred = float(self.dao.row(u)[0].sum() / n[0].sum())
return round(pred, 3)
pred = pred / float(denom)
return round(pred, 3)
def computeCorr(self):
'compute correlation among users'
print 'Computing user correlation...'
for u1 in self.dao.testSet_u:
for u2 in self.dao.user:
if u1 <> u2:
if self.userSim.contains(u1, u2):
continue
sim = qmath.similarity(self.dao.row(u1), self.dao.row(u2),
self.sim)
self.userSim.set(u1, u2, sim)
print u1, 'finished.'
print 'The user correlation has been figured out.'
class ItemKNN(Recommender):
def __init__(self, conf):
super(ItemKNN, self).__init__(conf)
super(ItemKNN, self).readConfiguration()
self.itemSim = SymmetricMatrix(len(self.dao.user))
def readConfiguration(self):
self.sim = self.config['similarity']
self.shrinkage = int(self.config['num.shrinkage'])
self.neighbors = int(self.config['num.neighbors'])
def initModel(self):
self.computeCorr()
def predict(self, u, i):
#find the closest neighbors of user u
topItems = sorted(self.itemSim[i].iteritems(),
key=lambda d: d[1],
reverse=True)
itemCount = self.neighbors
if itemCount > len(topItems):
itemCount = len(topItems)
#predict
pred = 0
denom = 0
for n in range(itemCount):
#if user n has rating on item i
if self.dao.rating(u, topItems[n][0]) != 0:
corr = topItems[n][1]
rating = self.dao.rating(u, topItems[n][0])
pred += corr * rating
denom += topItems[n][1]
if pred == 0:
#no users have rating on item i,return the average rating of user u
n = self.dao.col(i) > 0
if n[0].sum() == 0: #no data about current user in training set
return 0
pred = float(self.dao.col(i)[0].sum() / n[0].sum())
return round(pred, 3)
pred = pred / float(denom)
return round(pred, 3)
def computeCorr(self):
'compute correlation among users'
print 'Computing item correlation...'
for i1 in self.dao.testSet_i:
for i2 in self.dao.item:
if i1 <> i2:
if self.itemSim.contains(i1, i2):
continue
sim = qmath.similarity(self.dao.col(i1), self.dao.col(i2),
self.sim)
self.itemSim.set(i1, i2, sim)
print i1, 'finished.'
print 'The item correlation has been figured out.'
class UserKNN(Recommender):
def __init__(self, conf, trainingSet=None, testSet=None, fold='[1]'):
super(UserKNN, self).__init__(conf, trainingSet, testSet, fold)
self.userSim = SymmetricMatrix(len(self.data.name2id['user']))
self.topUsers = {}
def readConfiguration(self):
super(UserKNN, self).readConfiguration()
self.neighbors = int(self.config['num.neighbors'])
def printAlgorConfig(self):
"show algorithm's configuration"
super(UserKNN, self).printAlgorConfig()
print('Specified Arguments of', self.config['recommender'] + ':')
print('num.neighbors:', self.config['num.neighbors'])
print('=' * 80)
def initModel(self):
self.computeCorr()
def predict(self, u):
recommendations = []
for item in self.data.listened[self.recType]:
sum, denom = 0, 0
for simUser in self.topUsers[u]:
#if user n has rating on item i
if simUser[0] in self.data.listened[self.recType][item]:
similarity = simUser[1]
score = self.data.listened[self.recType][item][simUser[0]]
sum += similarity * score
denom += similarity
if sum != 0:
score = sum / float(denom)
recommendations.append((item, score))
recommendations = sorted(recommendations,
key=lambda d: d[1],
reverse=True)
recommendations = [item[0] for item in recommendations]
return recommendations
def computeCorr(self):
'compute correlation among users'
userListen = defaultdict(dict)
for user in self.data.userRecord:
for item in self.data.userRecord[user]:
if item[self.recType] in userListen[user]:
userListen[user][item[self.recType]] += 1
else:
userListen[user][item[self.recType]] = 0
print('Computing user similarities...')
for ind, u1 in enumerate(userListen):
set1 = set(userListen[u1].keys())
for u2 in userListen:
if u1 != u2:
if self.userSim.contains(u1, u2):
continue
set2 = set(userListen[u2].keys())
sim = self.jaccard(set1, set2)
self.userSim.set(u1, u2, sim)
self.topUsers[u1] = sorted(self.userSim[u1].items(),
key=lambda d: d[1],
reverse=True)[:self.neighbors]
if ind % 100 == 0:
print(ind, '/', len(userListen), 'finished.')
print('The user correlation has been figured out.')
def jaccard(self, s1, s2):
return 2 * len(s1.intersection(s2)) / (len(s1.union(s2)) + 0.0)
class UserKNN(Recommender):
def __init__(self,conf,trainingSet=None,testSet=None,fold='[1]'):
super(UserKNN, self).__init__(conf,trainingSet,testSet,fold)
self.userSim = SymmetricMatrix(len(self.dao.user))
def readConfiguration(self):
super(UserKNN, self).readConfiguration()
self.sim = self.config['similarity']
self.shrinkage =int(self.config['num.shrinkage'])
self.neighbors = int(self.config['num.neighbors'])
def printAlgorConfig(self):
"show algorithm's configuration"
super(UserKNN, self).printAlgorConfig()
print 'Specified Arguments of',self.config['recommender']+':'
print 'num.neighbors:',self.config['num.neighbors']
print 'num.shrinkage:', self.config['num.shrinkage']
print 'similarity:', self.config['similarity']
print '='*80
def initModel(self):
self.computeCorr()
def predict(self,u,i):
#find the closest neighbors of user u
topUsers = sorted(self.userSim[u].iteritems(),key = lambda d:d[1],reverse=True)
userCount = self.neighbors
if userCount > len(topUsers):
userCount = len(topUsers)
#predict
sum,denom = 0,0
for n in range(userCount):
#if user n has rating on item i
similarUser = topUsers[n][0]
if self.dao.rating(similarUser,i) != 0:
similarity = topUsers[n][1]
rating = self.dao.rating(similarUser,i)
sum += similarity*(rating-self.dao.userMeans[similarUser])
denom += similarity
if sum == 0:
#no users have rating on item i,return the average rating of user u
if not self.dao.containsUser(u):
#user u has no ratings in the training set,return the global mean
return self.dao.globalMean
return self.dao.userMeans[u]
pred = self.dao.userMeans[u]+sum/float(denom)
return pred
def computeCorr(self):
'compute correlation among users'
print 'Computing user correlation...'
for u1 in self.dao.testSet_u:
for u2 in self.dao.user:
if u1 <> u2:
if self.userSim.contains(u1,u2):
continue
sim = qmath.similarity(self.dao.row(u1),self.dao.row(u2),self.sim)
self.userSim.set(u1,u2,sim)
print 'user '+u1+' finished.'
print 'The user correlation has been figured out.'
class UserKNN(Recommender):
def __init__(self, conf, trainingSet=None, testSet=None, fold='[1]'):
super(UserKNN, self).__init__(conf, trainingSet, testSet, fold)
self.userSim = SymmetricMatrix(len(self.data.user))
def readConfiguration(self):
super(UserKNN, self).readConfiguration()
self.sim = self.config['similarity']
self.shrinkage = int(self.config['num.shrinkage'])
self.neighbors = int(self.config['num.neighbors'])
def printAlgorConfig(self):
"show algorithm's configuration"
super(UserKNN, self).printAlgorConfig()
print('Specified Arguments of', self.config['recommender'] + ':')
print('num.neighbors:', self.config['num.neighbors'])
print('num.shrinkage:', self.config['num.shrinkage'])
print('similarity:', self.config['similarity'])
print('=' * 80)
def initModel(self):
self.topUsers = {}
self.computeCorr()
def predict(self, u, i):
#find the closest neighbors of user u
topUsers = self.topUsers[u]
userCount = self.neighbors
if userCount > len(topUsers):
userCount = len(topUsers)
#predict
sum, denom = 0, 0
for n in range(userCount):
#if user n has rating on item i
similarUser = topUsers[n][0]
if self.data.rating(similarUser, i) != -1:
similarity = topUsers[n][1]
rating = self.data.rating(similarUser, i)
sum += similarity * (rating - self.data.userMeans[similarUser])
denom += similarity
if sum == 0:
#no users have rating on item i,return the average rating of user u
if not self.data.containsUser(u):
#user u has no ratings in the training set,return the global mean
return self.data.globalMean
return self.data.userMeans[u]
pred = self.data.userMeans[u] + sum / float(denom)
return pred
def computeCorr(self):
'compute correlation among users'
print('Computing user similarities...')
for idx, u1 in enumerate(self.data.testSet_u):
for u2 in self.data.user:
if u1 != u2:
if self.userSim.contains(u1, u2):
continue
sim = qmath.similarity(self.data.sRow(u1),
self.data.sRow(u2), self.sim)
self.userSim.set(u1, u2, sim)
self.topUsers[u1] = sorted(iter(self.userSim[u1].items()),
key=lambda d: d[1],
reverse=True)
if idx % 100 == 0:
print('progress:', idx, '/', len(self.data.testSet_u))
print('The user similarities have been calculated.')
def predictForRanking(self, u):
print(
'Using Memory based algorithms to rank items is extremely time-consuming. So ranking for all items in UserKNN is not available.'
)
exit(0)
class ItemKNN(Recommender):
def __init__(self, conf, trainingSet=None, testSet=None, fold='[1]'):
super(ItemKNN, self).__init__(conf, trainingSet, testSet, fold)
self.itemSim = SymmetricMatrix(len(
self.data.user)) #used to store the similarity among items
def readConfiguration(self):
super(ItemKNN, self).readConfiguration()
self.sim = self.config['similarity']
self.shrinkage = int(self.config['num.shrinkage'])
self.neighbors = int(self.config['num.neighbors'])
def printAlgorConfig(self):
"show algorithm's configuration"
super(ItemKNN, self).printAlgorConfig()
print 'Specified Arguments of', self.config['recommender'] + ':'
print 'num.neighbors:', self.config['num.neighbors']
print 'num.shrinkage:', self.config['num.shrinkage']
print 'similarity:', self.config['similarity']
print '=' * 80
def initModel(self):
self.topItems = {}
self.computeCorr()
def predict(self, u, i):
#find the closest neighbors of item i
topItems = self.topItems[i]
itemCount = self.neighbors
if itemCount > len(topItems):
itemCount = len(topItems)
#predict
sum = 0
denom = 0
for n in range(itemCount):
similarItem = topItems[n][0]
#if user n has rating on item i
if self.data.contains(u, similarItem):
similarity = topItems[n][1]
rating = self.data.rating(u, similarItem)
sum += similarity * (rating - self.data.itemMeans[similarItem])
denom += similarity
if sum == 0:
#no items have rating on item i,return the average rating of item i
if not self.data.containsItem(i):
# item i has no ratings in the training set
return self.data.globalMean
return self.data.itemMeans[i]
pred = self.data.itemMeans[i] + sum / float(denom)
return pred
def computeCorr(self):
'compute correlation among items'
print 'Computing item similarities...'
for idx, i1 in enumerate(self.data.testSet_i):
for i2 in self.data.item:
if i1 <> i2:
if self.itemSim.contains(i1, i2):
continue
sim = qmath.similarity(self.data.sCol(i1),
self.data.sCol(i2), self.sim)
self.itemSim.set(i1, i2, sim)
self.topItems[i1] = sorted(self.itemSim[i1].iteritems(),
key=lambda d: d[1],
reverse=True)
if idx % 100 == 0:
print 'progress:', idx, '/', len(self.data.testSet_i)
print 'The item similarities have been calculated.'
class CUNE_BPR(IterativeRecommender):
def __init__(self, conf, trainingSet=None, testSet=None, fold='[1]'):
super(CUNE_BPR, self).__init__(conf, trainingSet, testSet, fold)
self.nonLeafVec = {}
self.leafVec = {}
def readConfiguration(self):
super(CUNE_BPR, self).readConfiguration()
options = config.LineConfig(self.config['CUNE-BPR'])
self.walkCount = int(options['-T'])
self.walkLength = int(options['-L'])
self.walkDim = int(options['-l'])
self.winSize = int(options['-w'])
self.topK = int(options['-k'])
self.s = float(options['-s'])
def printAlgorConfig(self):
super(CUNE_BPR, self).printAlgorConfig()
print 'Specified Arguments of', self.config['recommender'] + ':'
print 'Walks count per user', self.walkCount
print 'Length of each walk', self.walkLength
print 'Dimension of user embedding', self.walkDim
print '=' * 80
def buildModel(self):
print 'Kind Note: This method will probably take much time.'
#build C-U-NET
print 'Building collaborative user network...'
#filter isolated nodes
self.itemNet = {}
for item in self.dao.trainSet_i:
if len(self.dao.trainSet_i[item]) > 1:
self.itemNet[item] = self.dao.trainSet_i[item]
self.filteredRatings = defaultdict(list)
for item in self.itemNet:
for user in self.itemNet[item]:
if self.itemNet[item][user] >= 1:
self.filteredRatings[user].append(item)
self.CUNet = defaultdict(list)
for user1 in self.filteredRatings:
for user2 in self.filteredRatings:
if user1 <> user2:
weight = len(
set(self.filteredRatings[user1]).intersection(
set(self.filteredRatings[user2])))
if weight > 0:
self.CUNet[user1] += [user2] * weight
#build Huffman Tree First
#get weight
print 'Building Huffman tree...'
#To accelerate the method, the weight is estimated roughly
nodes = {}
for user in self.CUNet:
nodes[user] = len(self.CUNet[user])
nodes = sorted(nodes.iteritems(), key=lambda d: d[1])
nodes = [HTreeNode(None, None, user[1], user[0]) for user in nodes]
nodeList = OrderedLinkList()
for node in nodes:
listNode = Node()
listNode.val = node
try:
nodeList.insert(listNode)
except AttributeError:
pass
self.HTree = HuffmanTree(vecLength=self.walkDim)
self.HTree.buildTree(nodeList)
print 'Coding for all users...'
self.HTree.coding(self.HTree.root, '', 0)
print 'Generating random deep walks...'
self.walks = []
self.visited = defaultdict(dict)
for user in self.CUNet:
for t in range(self.walkCount):
currentNode = user
path = [user]
for i in range(1, self.walkLength):
nextNode = self.CUNet[user][
randint(0, len(self.CUNet[user])) - 1]
count = 0
while (self.visited[user].has_key(nextNode)):
nextNode = self.CUNet[randint(0, len(self.CUNet[user]))
- 1]
#break infinite loop
count += 1
if count == 10:
break
path.append(nextNode)
self.walks.append(path)
#print path
shuffle(self.walks)
#Training get top-k friends
print 'Generating user embedding...'
iteration = 1
while iteration <= self.maxIter:
loss = 0
for walk in self.walks:
for user in walk:
centerUser = walk[len(walk) / 2]
if user <> centerUser:
code = self.HTree.code[user]
centerCode = self.HTree.code[centerUser]
x = self.HTree.vector[centerCode]
for i in range(1, len(code)):
prefix = code[0:i]
w = self.HTree.vector[prefix]
self.HTree.vector[prefix] += self.lRate * (
1 - sigmoid(w.dot(x))) * x
self.HTree.vector[centerCode] += self.lRate * (
1 - sigmoid(w.dot(x))) * w
loss += -log(sigmoid(w.dot(x)), 2)
print 'iteration:', iteration, 'loss:', loss
iteration += 1
print 'User embedding generated.'
print 'Constructing similarity matrix...'
self.Sim = SymmetricMatrix(len(self.CUNet))
for user1 in self.CUNet:
for user2 in self.CUNet:
if user1 <> user2:
prefix1 = self.HTree.code[user1]
vec1 = self.HTree.vector[prefix1]
prefix2 = self.HTree.code[user2]
vec2 = self.HTree.vector[prefix2]
if self.Sim.contains(user1, user2):
continue
sim = cosine(vec1, vec2)
self.Sim.set(user1, user2, sim)
self.topKSim = {}
for user in self.CUNet:
self.topKSim[user] = sorted(self.Sim[user].iteritems(),
key=lambda d: d[1],
reverse=True)[:self.topK]
print 'Similarity matrix finished.'
#print self.topKSim
#prepare Pu set, IPu set, and Nu set
print 'Preparing item sets...'
self.PositiveSet = defaultdict(dict)
self.IPositiveSet = defaultdict(list)
self.NegativeSet = defaultdict(list)
for user in self.topKSim:
for item in self.dao.trainSet_u[user]:
if self.dao.trainSet_u[user][item] >= 1:
self.PositiveSet[user][item] = 1
else:
self.NegativeSet[user].append(item)
for friend in self.topKSim[user]:
for item in self.dao.trainSet_u[friend[0]]:
if not self.PositiveSet[user].has_key(item):
self.IPositiveSet[user].append(item)
print 'Training...'
iteration = 0
while iteration < self.maxIter:
self.loss = 0
for user in self.PositiveSet:
u = self.dao.user[user]
for item in self.PositiveSet[user]:
if len(self.IPositiveSet[user]) > 0:
item_k = self.IPositiveSet[user][randint(
0,
len(self.IPositiveSet[user]) - 1)]
i = self.dao.item[item]
k = self.dao.item[item_k]
self.P[u] += self.lRate * (
1 - sigmoid(self.P[u].dot(self.Q[i]) -
self.P[u].dot(self.Q[k]))) * (
self.Q[i] - self.Q[k])
self.Q[i] += self.lRate * (
1 - sigmoid(self.P[u].dot(self.Q[i]) -
self.P[u].dot(self.Q[k]))) * self.P[u]
self.Q[k] -= self.lRate * (
1 - sigmoid(self.P[u].dot(self.Q[i]) -
self.P[u].dot(self.Q[k]))) * self.P[u]
item_j = ''
if len(self.NegativeSet[user]) > 0:
item_j = self.NegativeSet[user][randint(
0,
len(self.NegativeSet[user]) - 1)]
else:
item_j = self.dao.item.keys()[randint(
0,
len(self.dao.item) - 1)]
while (self.PositiveSet[user].has_key(item_j)):
item_j = self.dao.item.keys()[randint(
0,
len(self.dao.item) - 1)]
j = self.dao.item[item_j]
self.P[u] += (1 / self.s) * self.lRate * (1 - sigmoid(
(1 / self.s) *
(self.P[u].dot(self.Q[k]) - self.P[u].dot(
self.Q[j])))) * (self.Q[k] - self.Q[j])
self.Q[k] += (1 / self.s) * self.lRate * (1 - sigmoid(
(1 / self.s) *
(self.P[u].dot(self.Q[k]) -
self.P[u].dot(self.Q[j])))) * self.P[u]
self.Q[j] -= (1 / self.s) * self.lRate * (1 - sigmoid(
(1 / self.s) *
(self.P[u].dot(self.Q[k]) -
self.P[u].dot(self.Q[j])))) * self.P[u]
self.P[u] += self.lRate * self.regU * self.P[u]
self.Q[i] += self.lRate * self.regI * self.Q[i]
self.Q[j] += self.lRate * self.regI * self.Q[j]
self.Q[k] += self.lRate * self.regI * self.Q[k]
self.loss += log(sigmoid(self.P[u].dot(self.Q[i])-self.P[u].dot(self.Q[k]))) + \
log(sigmoid((1/self.s)*(self.P[u].dot(self.Q[i]) - self.P[u].dot(self.Q[k]))))
self.loss += self.regU * (self.P * self.P).sum() + self.regI * (
self.Q * self.Q).sum()
iteration += 1
if self.isConverged(iteration):
break
def predictForRanking(self, u):
'invoked to rank all the items for the user'
if self.dao.containsUser(u):
u = self.dao.getUserId(u)
return self.Q.dot(self.P[u])
else:
return [self.dao.globalMean] * len(self.dao.item)
class CUNE_MF(IterativeRecommender):
def __init__(self, conf, trainingSet=None, testSet=None, fold='[1]'):
super(CUNE_MF, self).__init__(conf, trainingSet, testSet, fold)
self.nonLeafVec = {}
self.leafVec = {}
def readConfiguration(self):
super(CUNE_MF, self).readConfiguration()
options = config.LineConfig(self.config['CUNE-MF'])
self.walkCount = int(options['-T'])
self.walkLength = int(options['-L'])
self.walkDim = int(options['-l'])
self.winSize = int(options['-w'])
self.topK = int(options['-k'])
self.alpha = float(options['-a'])
self.epoch = int(options['-ep'])
def printAlgorConfig(self):
super(CUNE_MF, self).printAlgorConfig()
print 'Specified Arguments of', self.config['recommender'] + ':'
print 'Walks count per user', self.walkCount
print 'Length of each walk', self.walkLength
print 'Dimension of user embedding', self.walkDim
print '=' * 80
def buildModel(self):
print 'Kind Note: This method will probably take much time.'
#build C-U-NET
print 'Building collaborative user network...'
#filter isolated nodes and low ratings
self.itemNet = {}
for item in self.dao.trainSet_i:
if len(self.dao.trainSet_i[item]) > 1:
self.itemNet[item] = self.dao.trainSet_i[item]
self.filteredRatings = defaultdict(list)
for item in self.itemNet:
for user in self.itemNet[item]:
if self.itemNet[item][user] > 0.75:
self.filteredRatings[user].append(item)
self.CUNet = defaultdict(list)
for user1 in self.filteredRatings:
s1 = set(self.filteredRatings[user1])
for user2 in self.filteredRatings:
if user1 <> user2:
s2 = set(self.filteredRatings[user2])
weight = len(s1.intersection(s2))
if weight > 0:
self.CUNet[user1] += [user2] * weight
#build Huffman Tree First
#get weight
print 'Building Huffman tree...'
#To accelerate the method, the weight is estimated roughly
nodes = {}
for user in self.CUNet:
nodes[user] = len(self.CUNet[user])
nodes = sorted(nodes.iteritems(), key=lambda d: d[1])
nodes = [HTreeNode(None, None, user[1], user[0]) for user in nodes]
nodeList = OrderedLinkList()
for node in nodes:
listNode = Node()
listNode.val = node
try:
nodeList.insert(listNode)
except AttributeError:
pass
self.HTree = HuffmanTree(vecLength=self.walkDim)
self.HTree.buildTree(nodeList)
print 'Coding for all users...'
self.HTree.coding(self.HTree.root, '', 0)
print 'Generating random deep walks...'
self.walks = []
self.visited = defaultdict(dict)
for user in self.CUNet:
for t in range(self.walkCount):
path = [user]
for i in range(1, self.walkLength):
nextNode = choice(self.CUNet[user])
count = 0
while (self.visited[user].has_key(nextNode)):
nextNode = choice(self.CUNet[user])
#break infinite loop
count += 1
if count == 10:
break
path.append(nextNode)
self.visited[user][nextNode] = 1
self.walks.append(path)
#print path
shuffle(self.walks)
#Training get top-k friends
print 'Generating user embedding...'
iteration = 1
while iteration <= self.epoch:
loss = 0
for walk in self.walks:
for user in walk:
centerUser = walk[len(walk) / 2]
if user <> centerUser:
code = self.HTree.code[user]
centerCode = self.HTree.code[centerUser]
x = self.HTree.vector[centerCode]
for i in range(1, len(code)):
prefix = code[0:i]
w = self.HTree.vector[prefix]
self.HTree.vector[prefix] += self.lRate * (
1 - sigmoid(w.dot(x))) * x
self.HTree.vector[centerCode] += self.lRate * (
1 - sigmoid(w.dot(x))) * w
loss += -log(sigmoid(w.dot(x)))
print 'iteration:', iteration, 'loss:', loss
iteration += 1
print 'User embedding generated.'
print 'Constructing similarity matrix...'
self.Sim = SymmetricMatrix(len(self.CUNet))
for user1 in self.CUNet:
for user2 in self.CUNet:
if user1 <> user2:
prefix1 = self.HTree.code[user1]
vec1 = self.HTree.vector[prefix1]
prefix2 = self.HTree.code[user2]
vec2 = self.HTree.vector[prefix2]
if self.Sim.contains(user1, user2):
continue
sim = cosine(vec1, vec2)
self.Sim.set(user1, user2, sim)
self.topKSim = {}
for user in self.CUNet:
self.topKSim[user] = sorted(self.Sim[user].iteritems(),
key=lambda d: d[1],
reverse=True)[:self.topK]
print 'Similarity matrix finished.'
#print self.topKSim
#matrix decomposition
print 'Decomposing...'
iteration = 0
while iteration < self.maxIter:
self.loss = 0
for entry in self.dao.trainingData:
user, item, rating = entry
u = self.dao.user[user] #get user id
i = self.dao.item[item] #get item id
error = rating - self.P[u].dot(self.Q[i])
self.loss += error**2
p = self.P[u]
q = self.Q[i]
#update latent vectors
self.P[u] += self.lRate * (error * q - self.regU * p)
self.Q[i] += self.lRate * (error * p - self.regI * q)
for user in self.CUNet:
u = self.dao.user[user]
friends = self.topKSim[user]
for friend in friends:
uf = self.dao.user[friend[0]]
self.P[u] -= self.lRate * (self.P[u] -
self.P[uf]) * self.alpha
self.loss += self.alpha * (
self.P[u] - self.P[uf]).dot(self.P[u] - self.P[uf])
self.loss += self.regU * (self.P * self.P).sum() + self.regI * (
self.Q * self.Q).sum()
iteration += 1
if self.isConverged(iteration):
break
class TSWalker(Recommender):
def __init__(self, conf, trainingSet=None, testSet=None, fold='[1]'):
super(TSWalker, self).__init__(conf, trainingSet, testSet, fold)
self.userSim = SymmetricMatrix(len(self.dao.user))
self.itemSim = SymmetricMatrix(len(self.dao.item))
def readConfiguration(self):
super(TSWalker, self).readConfiguration()
self.sim = self.config['similarity']
TW = LineConfig(self.config['TSWalker'])
self.k = int(TW['-k'])
self.v = float(TW['-v'])
self.tw = int(TW['-tw'])
def printAlgorConfig(self):
"show algorithm's configuration"
super(TSWalker, self).printAlgorConfig()
print 'Specified Arguments of', self.config['recommender'] + ':'
print 'similarity:', self.config['similarity']
print 'step: %d' % self.k
print 'Random Walk times: %d' % self.tw
print 'The trust value of u: %f' % self.v
print '=' * 80
def initModel(self):
self.computeICorr()
self.computeUCorr()
def predict(self, u, i):
u0 = u
twcount = 0
pre = []
rating = 0
while twcount < self.tw:
tk = 0
while tk < self.k:
u1 = choice(list(self.dao.user))
if (u0 <> u1) and (u1 not in pre):
pre.append(u1)
else:
continue
pu = self.dao.getUserId(u1)
if self.userSim[u][u1] != 1:
continue
else:
if self.dao.rating(u1, i) != 0:
rating += self.dao.rating(u1, i)
tk += 1
twcount += 1
print 'Finished TSWalker for %d time in %d step-1' % (
twcount, tk)
else:
tk += 1
pk = self.proOfK(u1, i, tk)
pv = random()
if pv < pk:
uj = self.dao.trainingMatrix.matrix_User[pu].keys()
temp = 0
bitem = 0
for j in uj:
if self.itemSim[i][self.dao.id2item[j]] > temp:
temp = self.itemSim[i][self.dao.id2item[j]]
bitem = j
rating += self.dao.rating(u1,
self.dao.id2item[bitem])
twcount += 1
print 'Finished TSWalker for %d time in %d step-2' % (
twcount, tk)
else:
u0 = u1
print rating
pred = rating / float(self.tw)
return pred
def computeUCorr(self):
'compute correlation among users'
print 'Computing user correlation...'
for u1 in self.dao.testSet_u:
for u2 in self.dao.user:
if u1 <> u2:
if self.userSim.contains(u1, u2):
continue
sim = qmath.similarity(self.dao.sRow(u1),
self.dao.sRow(u2), self.sim)
if sim >= self.v:
self.userSim.set(u1, u2, 1)
else:
self.userSim.set(u1, u2, 0)
tcount = 0
for i in range(len(self.userSim[u1])):
us = list(self.userSim[u1].iteritems())
if us[i][1] == 1:
tcount += 1
print 'user ' + u1 + ' finished.'
print 'The user correlation has been figured out.'
def computeICorr(self):
'compute correlation among items'
for i in self.dao.item:
d1 = 0
for r in self.dao.user:
ui = self.dao.rating(r, i)
um = self.dao.userMeans[r]
d1 += (ui - um)**2
for j in self.dao.item:
if i <> j:
if self.itemSim.contains(i, j):
continue
aui, rui = self.dao.itemRated(i)
auj, ruj = self.dao.itemRated(j)
cuser = set(aui).intersection(set(auj))
sum = 0
d2 = 0
for cu in cuser:
rui = self.dao.rating(self.dao.id2user[cu], i)
ruj = self.dao.rating(self.dao.id2user[cu], j)
umean = self.dao.userMeans[self.dao.id2user[cu]]
sum += (rui - umean) * (ruj - umean)
d2 += (rui - umean)**2
try:
denom = sqrt(d1 * d2)
corr = float(sum) / denom
except ZeroDivisionError:
corr = 0
finally:
l = float(len(cuser)) / 2
sim = corr / (1 + exp(-l))
self.itemSim.set(i, j, sim)
print 'item ' + i + ' finished.'
print 'The item correlation has been figured out.'
def proOfK(self, u, i, k):
res = []
nk = float(k) / 2
ui, ur = self.dao.userRated(u)
for j in ui:
res.append(self.itemSim[i][self.dao.id2item[j]])
denom = 1 + exp(-nk)
nres = map(lambda x: (x / denom), res)
return max(nres)