class Recommender(object):
def __init__(self,conf):
self.config = conf
self.dao = None
self.isSaveModel = False
self.ranking = None
self.isLoadModel = False
self.output = None
self.foldInfo = '[1]'
self.isOutput = True
self.dao = RatingDAO(self.config)
def readConfiguration(self):
self.algorName = self.config['recommender']
self.output = LineConfig(self.config['output.setup'])
self.isOutput = self.output.isMainOn()
self.ranking = LineConfig(self.config['item.ranking'])
def printAlgorConfig(self):
"show algorithm's configuration"
print 'Algorithm:',self.config['recommender']
print 'Ratings dataSet:',abspath(self.config['ratings'])
if LineConfig(self.config['evaluation.setup']).contains('-testSet'):
print 'Test set:',abspath(LineConfig(self.config['evaluation.setup']).getOption('-testSet'))
#print 'Count of the users in training set: ',len()
print 'Training set size: (user count: %d, item count %d, record count: %d)' %(self.dao.trainingSize())
print 'Test set size: (user count: %d, item count %d, record count: %d)' %(self.dao.testSize())
print '='*80
def initModel(self):
pass
def buildModel(self):
'build the model (for model-based algorithms )'
pass
def saveModel(self):
pass
def loadModel(self):
pass
def predict(self,u,i):
pass
def checkRatingBoundary(self,prediction):
if prediction > self.dao.rScale[-1]:
return self.dao.rScale[-1]
elif prediction < self.dao.rScale[0]:
return self.dao.rScale[0]
else:
return round(prediction,3)
def evalRatings(self):
res = [] #used to contain the text of the result
res.append('userId itemId original prediction\n')
#predict
for userId in self.dao.testSet_u:
for ind,item in enumerate(self.dao.testSet_u[userId]):
itemId = item[0]
originRating = item[1]
#predict
prediction = self.predict(userId,itemId)
#denormalize
prediction = denormalize(prediction,self.dao.rScale[-1],self.dao.rScale[0])
#####################################
pred = self.checkRatingBoundary(prediction)
# add prediction in order to measure
self.dao.testSet_u[userId][ind].append(pred)
res.append(userId+' '+itemId+' '+str(originRating)+' '+str(pred)+'\n')
currentTime = strftime("%Y-%m-%d %H-%M-%S",localtime(time()))
#output prediction result
if self.isOutput:
outDir = self.output['-dir']
fileName = self.config['recommender']+'@'+currentTime+'-rating-predictions'+self.foldInfo+'.txt'
FileIO.writeFile(outDir,fileName,res)
print 'The Result has been output to ',abspath(outDir),'.'
#output evaluation result
outDir = self.output['-dir']
fileName = self.config['recommender'] + '@'+currentTime +'-measure'+ self.foldInfo + '.txt'
measure = Measure.ratingMeasure(self.dao.testSet_u)
FileIO.writeFile(outDir, fileName, measure)
def evalRanking(self):
res = [] # used to contain the text of the result
N = int(self.ranking['-topN'])
if N>100 or N<0:
N=100
res.append('userId: recommendations in (itemId, ranking score) pairs\n')
# predict
topNSet = {}
userCount = len(self.dao.testSet_u)
for i,userId in enumerate(self.dao.testSet_u):
itemSet = {}
line = userId+':'
for itemId in self.dao.item:
# predict
prediction = self.predict(userId, itemId)
# denormalize
prediction = denormalize(prediction, self.dao.rScale[-1], self.dao.rScale[0])
#####################################
pred = self.checkRatingBoundary(prediction)
# add prediction in order to measure
itemSet[itemId] = pred
topNSet[userId] = sorted(itemSet.iteritems(),key=lambda d:d[1],reverse=True)[0:N]
if i%100==0:
print 'Progress:'+str(i)+'/'+str(userCount)
for item in topNSet[userId]:
line += '(' + item[0] + ',' + str(item[1]) + ') '
line+='\n'
res.append(line)
currentTime = strftime("%Y-%m-%d %H-%M-%S", localtime(time()))
# output prediction result
if self.isOutput:
outDir = self.output['-dir']
fileName = self.config['recommender'] + '@' + currentTime + '-top-'+str(N)+'items' + self.foldInfo + '.txt'
FileIO.writeFile(outDir, fileName, res)
print 'The Result has been output to ', abspath(outDir), '.'
#output evaluation result
outDir = self.output['-dir']
fileName = self.config['recommender'] + '@' + currentTime + '-measure' + self.foldInfo + '.txt'
measure = Measure.rankingMeasure(self.dao.testSet_u,topNSet,N)
FileIO.writeFile(outDir, fileName, measure)
def execute(self):
self.readConfiguration()
self.printAlgorConfig()
#load model from disk or build model
if self.isLoadModel:
print 'Loading model...'
self.loadModel()
else:
print 'Initializing model...'
self.initModel()
print 'Building Model...'
self.buildModel()
#preict the ratings or item ranking
print 'Predicting...'
if self.ranking.isMainOn():
self.evalRanking()
else:
self.evalRatings()
#save model
if self.isSaveModel:
print 'Saving model...'
self.saveModel()
class Recommender(object):
def __init__(self, conf, trainingSet, testSet, fold='[1]'):
self.config = conf
self.data = None
self.isSaveModel = False
self.ranking = None
self.isLoadModel = False
self.output = None
self.isOutput = True
self.data = RatingDAO(self.config, trainingSet, testSet)
self.foldInfo = fold
self.evalSettings = LineConfig(self.config['evaluation.setup'])
self.measure = []
self.record = []
if self.evalSettings.contains('-cold'):
#evaluation on cold-start users
threshold = int(self.evalSettings['-cold'])
removedUser = {}
for user in self.data.testSet_u:
if self.data.trainSet_u.has_key(user) and len(
self.data.trainSet_u[user]) > threshold:
removedUser[user] = 1
for user in removedUser:
del self.data.testSet_u[user]
testData = []
for item in self.data.testData:
if not removedUser.has_key(item[0]):
testData.append(item)
self.data.testData = testData
self.num_users, self.num_items, self.train_size = self.data.trainingSize(
)
def readConfiguration(self):
self.algorName = self.config['recommender']
self.output = LineConfig(self.config['output.setup'])
self.isOutput = self.output.isMainOn()
self.ranking = LineConfig(self.config['item.ranking'])
def printAlgorConfig(self):
"show algorithm's configuration"
print('Algorithm:', self.config['recommender'])
print('Ratings dataset:', abspath(self.config['ratings']))
if LineConfig(self.config['evaluation.setup']).contains('-testSet'):
print(
'Test set:',
abspath(
LineConfig(self.config['evaluation.setup']).getOption(
'-testSet')))
#print 'Count of the users in training set: ',len()
print(
'Training set size: (user count: %d, item count %d, record count: %d)'
% (self.data.trainingSize()))
print(
'Test set size: (user count: %d, item count %d, record count: %d)'
% (self.data.testSize()))
print('=' * 80)
def initModel(self):
pass
def buildModel(self):
'build the model (for model-based algorithms )'
pass
def buildModel_tf(self):
'training model on tensorflow'
pass
def saveModel(self):
pass
def loadModel(self):
pass
def predict(self, u, i):
pass
def predictForRanking(self, u):
pass
def checkRatingBoundary(self, prediction):
if prediction > self.data.rScale[-1]:
return self.data.rScale[-1]
elif prediction < self.data.rScale[0]:
return self.data.rScale[0]
else:
return round(prediction, 3)
def evalRatings(self):
res = [] #used to contain the text of the result
res.append('userId itemId original prediction\n')
#predict
for ind, entry in enumerate(self.data.testData):
user, item, rating = entry
#predict
prediction = self.predict(user, item)
#denormalize
#prediction = denormalize(prediction,self.data.rScale[-1],self.data.rScale[0])
#####################################
pred = self.checkRatingBoundary(prediction)
# add prediction in order to measure
self.data.testData[ind].append(pred)
res.append(user + ' ' + item + ' ' + str(rating) + ' ' +
str(pred) + '\n')
currentTime = strftime("%Y-%m-%d %H-%M-%S", localtime(time()))
#output prediction result
if self.isOutput:
outDir = self.output['-dir']
fileName = self.config[
'recommender'] + '@' + currentTime + '-rating-predictions' + self.foldInfo + '.txt'
FileIO.writeFile(outDir, fileName, res)
print('The result has been output to ', abspath(outDir), '.')
#output evaluation result
outDir = self.output['-dir']
fileName = self.config[
'recommender'] + '@' + currentTime + '-measure' + self.foldInfo + '.txt'
self.measure = Measure.ratingMeasure(self.data.testData)
FileIO.writeFile(outDir, fileName, self.measure)
print('The result of %s %s:\n%s' %
(self.algorName, self.foldInfo, ''.join(self.measure)))
def evalRanking(self):
res = [] # used to contain the text of the result
if self.ranking.contains('-topN'):
top = self.ranking['-topN'].split(',')
top = [int(num) for num in top]
N = int(top[-1])
if N > 100 or N < 0:
print(
'N can not be larger than 100! It has been reassigned with 10'
)
N = 10
if N > len(self.data.item):
N = len(self.data.item)
else:
print('No correct evaluation metric is specified!')
exit(-1)
res.append(
'userId: recommendations in (itemId, ranking score) pairs, * means the item matches.\n'
)
# predict
recList = {}
userN = {}
userCount = len(self.data.testSet_u)
#rawRes = {}
for i, user in enumerate(self.data.testSet_u):
itemSet = {}
line = user + ':'
predictedItems = self.predictForRanking(user)
# predictedItems = denormalize(predictedItems, self.data.rScale[-1], self.data.rScale[0])
for id, rating in enumerate(predictedItems):
# if not self.data.rating(user, self.data.id2item[id]):
# prediction = self.checkRatingBoundary(prediction)
# pred = self.checkRatingBoundary(prediction)
#####################################
# add prediction in order to measure
itemSet[self.data.id2item[id]] = rating
ratedList, ratingList = self.data.userRated(user)
for item in ratedList:
del itemSet[item]
Nrecommendations = []
for item in itemSet:
if len(Nrecommendations) < N:
Nrecommendations.append((item, itemSet[item]))
else:
break
Nrecommendations.sort(key=lambda d: d[1], reverse=True)
recommendations = [item[1] for item in Nrecommendations]
resNames = [item[0] for item in Nrecommendations]
# find the N biggest scores
for item in itemSet:
ind = N
l = 0
r = N - 1
if recommendations[r] < itemSet[item]:
while r >= l:
mid = (r - l) / 2 + l
if recommendations[mid] >= itemSet[item]:
l = mid + 1
elif recommendations[mid] < itemSet[item]:
r = mid - 1
if r < l:
ind = r
break
#move the items backwards
if ind < N - 2:
recommendations[ind + 2:] = recommendations[ind + 1:-1]
resNames[ind + 2:] = resNames[ind + 1:-1]
if ind < N - 1:
recommendations[ind + 1] = itemSet[item]
resNames[ind + 1] = item
recList[user] = zip(resNames, recommendations)
if i % 100 == 0:
print(self.algorName, self.foldInfo,
'progress:' + str(i) + '/' + str(userCount))
for item in recList[user]:
line += ' (' + item[0] + ',' + str(item[1]) + ')'
if self.data.testSet_u[user].has_key(item[0]):
line += '*'
line += '\n'
res.append(line)
currentTime = strftime("%Y-%m-%d %H-%M-%S", localtime(time()))
# output prediction result
if self.isOutput:
fileName = ''
outDir = self.output['-dir']
fileName = self.config[
'recommender'] + '@' + currentTime + '-top-' + str(
N) + 'items' + self.foldInfo + '.txt'
FileIO.writeFile(outDir, fileName, res)
print('The result has been output to ', abspath(outDir), '.')
# output evaluation result
outDir = self.output['-dir']
fileName = self.config[
'recommender'] + '@' + currentTime + '-measure' + self.foldInfo + '.txt'
self.measure = Measure.rankingMeasure(self.data.testSet_u, recList,
top)
FileIO.writeFile(outDir, fileName, self.measure)
print('The result of %s %s:\n%s' %
(self.algorName, self.foldInfo, ''.join(self.measure)))
def execute(self):
self.readConfiguration()
if self.foldInfo == '[1]':
self.printAlgorConfig()
#load model from disk or build model
if self.isLoadModel:
print('Loading model %s...' % (self.foldInfo))
self.loadModel()
else:
print('Initializing model %s...' % (self.foldInfo))
self.initModel()
print('Building Model %s...' % (self.foldInfo))
try:
import tensorflow
if self.evalSettings.contains('-tf'):
self.buildModel_tf()
else:
self.buildModel()
except ImportError:
self.buildModel()
#preict the ratings or item ranking
print('Predicting %s...' % (self.foldInfo))
if self.ranking.isMainOn():
self.evalRanking()
else:
self.evalRatings()
#save model
if self.isSaveModel:
print('Saving model %s...' % (self.foldInfo))
self.saveModel()
# with open(self.foldInfo+'measure.txt','w') as f:
# f.writelines(self.record)
return self.measure
class Recommender(object):
def __init__(self, conf, trainingSet=None, testSet=None, fold='[1]'):
self.config = conf
self.isSaveModel = False
self.isLoadModel = False
self.isOutput = True
self.data = Record(self.config, trainingSet, testSet)
self.foldInfo = fold
self.evalConfig = LineConfig(self.config['evaluation.setup'])
if self.evalConfig.contains('-target'):
self.recType = self.evalConfig['-target']
else:
self.recType = 'track'
if LineConfig(self.config['evaluation.setup']).contains('-cold'):
#evaluation on cold-start users
threshold = int(
LineConfig(self.config['evaluation.setup'])['-cold'])
removedUser = {}
for user in self.data.testSet:
if self.data.userRecord.has_key(user) and len(
self.data.userRecord[user]) > threshold:
removedUser[user] = 1
for user in removedUser:
del self.data.testSet[user]
def readConfiguration(self):
self.algorName = self.config['recommender']
self.output = LineConfig(self.config['output.setup'])
self.isOutput = self.output.isMainOn()
self.ranking = LineConfig(self.config['item.ranking'])
def printAlgorConfig(self):
"show algorithm's configuration"
print 'Algorithm:', self.config['recommender']
print 'Training set:', abspath(self.config['record'])
if LineConfig(self.config['evaluation.setup']).contains('-testSet'):
print 'Test set:', abspath(
LineConfig(
self.config['evaluation.setup']).getOption('-testSet'))
#print 'Count of the users in training set: ',len()
self.data.printTrainingSize()
print '=' * 80
def initModel(self):
pass
def buildModel(self):
'build the model (for model-based algorithms )'
pass
def saveModel(self):
pass
def loadModel(self):
pass
def predict(self, user):
return []
def evalRanking(self):
res = [] # used to contain the text of the result
N = 0
threshold = 0
N = int(self.ranking['-topN'])
if N > 100 or N < 0:
print 'N can not be larger than 100! It has been reassigned with 10'
N = 10
res.append(
'userId: recommendations in (itemId, ranking score) pairs, * means the item matches.\n'
)
# predict
recList = {}
userCount = len(self.data.testSet)
rawRes = {}
for i, user in enumerate(self.data.testSet):
itemSet = {}
line = user + ':'
predictedItems = self.predict(user)
recList[user] = predictedItems
if i % 100 == 0:
print self.algorName, self.foldInfo, 'progress:' + str(
i) + '/' + str(userCount)
for item in recList[user]:
if self.data.testSet[user].has_key(item[0]):
line += '*'
line += item + ','
line += '\n'
res.append(line)
currentTime = strftime("%Y-%m-%d %H-%M-%S", localtime(time()))
# output prediction result
if self.isOutput:
fileName = ''
outDir = self.output['-dir']
if self.ranking.contains('-topN'):
fileName = self.config[
'recommender'] + '@' + currentTime + '-top-' + str(
N) + 'items' + self.foldInfo + '.txt'
elif self.ranking.contains('-threshold'):
fileName = self.config[
'recommender'] + '@' + currentTime + '-threshold-' + str(
threshold) + self.foldInfo + '.txt'
FileIO.writeFile(outDir, fileName, res)
print 'The result has been output to ', abspath(outDir), '.'
# output evaluation result
outDir = self.output['-dir']
fileName = self.config[
'recommender'] + '@' + currentTime + '-measure' + self.foldInfo + '.txt'
if self.ranking.contains('-topN'):
self.measure = Measure.rankingMeasure(self.data.testSet, recList,
rawRes, N)
FileIO.writeFile(outDir, fileName, self.measure)
print 'The result of %s %s:\n%s' % (self.algorName, self.foldInfo,
''.join(self.measure))
def execute(self):
self.readConfiguration()
if self.foldInfo == '[1]':
self.printAlgorConfig()
#load model from disk or build model
if self.isLoadModel:
print 'Loading model %s...' % (self.foldInfo)
self.loadModel()
else:
print 'Initializing model %s...' % (self.foldInfo)
self.initModel()
print 'Building Model %s...' % (self.foldInfo)
self.buildModel()
#preict the ratings or item ranking
print 'Predicting %s...' % (self.foldInfo)
self.evalRanking()
#save model
if self.isSaveModel:
print 'Saving model %s...' % (self.foldInfo)
self.saveModel()
return self.measure
class Recommender(object):
def __init__(self, conf, trainingSet=None, testSet=None, fold='[1]'):
self.config = conf
self.dao = None
self.isSaveModel = False
self.ranking = None
self.isLoadModel = False
self.output = None
self.isOutput = True
self.dao = RatingDAO(self.config, trainingSet, testSet)
self.foldInfo = fold
self.measure = []
def readConfiguration(self):
self.algorName = self.config['recommender']
self.output = LineConfig(self.config['output.setup'])
self.isOutput = self.output.isMainOn()
self.ranking = LineConfig(self.config['item.ranking'])
def printAlgorConfig(self):
"show algorithm's configuration"
print 'Algorithm:', self.config['recommender']
print 'Ratings dataset:', abspath(self.config['ratings'])
if LineConfig(self.config['evaluation.setup']).contains('-testSet'):
print 'Test set:', abspath(
LineConfig(
self.config['evaluation.setup']).getOption('-testSet'))
#print 'Count of the users in training set: ',len()
print 'Training set size: (user count: %d, item count %d, record count: %d)' % (
self.dao.trainingSize())
print 'Test set size: (user count: %d, item count %d, record count: %d)' % (
self.dao.testSize())
print '=' * 80
def initModel(self):
pass
def buildModel(self):
'build the model (for model-based algorithms )'
pass
def saveModel(self):
pass
def loadModel(self):
pass
def predict(self, u, i):
pass
def predictForRanking(self, u):
pass
def checkRatingBoundary(self, prediction):
if prediction > self.dao.rScale[-1]:
return self.dao.rScale[-1]
elif prediction < self.dao.rScale[0]:
return self.dao.rScale[0]
else:
return round(prediction, 3)
def evalRatings(self):
res = [] #used to contain the text of the result
res.append('userId itemId original prediction\n')
#predict
for ind, entry in enumerate(self.dao.testData):
user, item, rating = entry
#predict
prediction = self.predict(user, item)
#denormalize
prediction = denormalize(prediction, self.dao.rScale[-1],
self.dao.rScale[0])
#####################################
pred = self.checkRatingBoundary(prediction)
# add prediction in order to measure
self.dao.testData[ind].append(pred)
res.append(user + ' ' + item + ' ' + str(rating) + ' ' +
str(pred) + '\n')
currentTime = strftime("%Y-%m-%d %H-%M-%S", localtime(time()))
#output prediction result
if self.isOutput:
outDir = self.output['-dir']
fileName = self.config[
'recommender'] + '@' + currentTime + '-rating-predictions' + self.foldInfo + '.txt'
FileIO.writeFile(outDir, fileName, res)
print 'The Result has been output to ', abspath(outDir), '.'
#output evaluation result
outDir = self.output['-dir']
fileName = self.config[
'recommender'] + '@' + currentTime + '-measure' + self.foldInfo + '.txt'
self.measure = Measure.ratingMeasure(self.dao.testData)
FileIO.writeFile(outDir, fileName, self.measure)
def evalRanking(self):
res = [] # used to contain the text of the result
N = 0
threshold = 0
bThres = False
bTopN = False
if self.ranking.contains('-topN'):
bTopN = True
N = int(self.ranking['-topN'])
if N > 100 or N < 0:
print 'N can not be larger than 100! It has been reassigned with 100'
N = 100
elif self.ranking.contains('-threshold'):
threshold = float(self.ranking['-threshold'])
bThres = True
else:
print 'No correct evaluation metric is specified!'
exit(-1)
res.append(
'userId: recommendations in (itemId, ranking score) pairs, * means the item matches.\n'
)
# predict
recList = {}
userN = {}
userCount = len(self.dao.testSet_u)
for i, user in enumerate(self.dao.testSet_u):
itemSet = {}
line = user + ':'
for item in self.dao.item:
# predict
prediction = self.predict(user, item)
# denormalize
prediction = denormalize(prediction, self.dao.rScale[-1],
self.dao.rScale[0])
#prediction = self.checkRatingBoundary(prediction)
#pred = self.checkRatingBoundary(prediction)
#####################################
# add prediction in order to measure
if bThres:
if prediction > threshold:
itemSet[item] = prediction
else:
itemSet[item] = prediction
ratedList, ratingList = self.dao.userRated(user)
for item in ratedList:
del itemSet[self.dao.id2item[item]]
itemSet = sorted(itemSet.iteritems(),
key=lambda d: d[1],
reverse=True)
if self.ranking.contains('-topN'):
recList[user] = itemSet[0:N]
elif self.ranking.contains('-threshold'):
recList[user] = itemSet[:]
userN[user] = len(itemSet)
if i % 100 == 0:
print self.algorName, self.foldInfo, 'progress:' + str(
i) + '/' + str(userCount)
for item in recList[user]:
line += ' (' + item[0] + ',' + str(item[1]) + ')'
if self.dao.testSet_u[user].has_key(item[0]):
line += '*'
line += '\n'
res.append(line)
currentTime = strftime("%Y-%m-%d %H-%M-%S", localtime(time()))
# output prediction result
if self.isOutput:
fileName = ''
outDir = self.output['-dir']
if self.ranking.contains('-topN'):
fileName = self.config[
'recommender'] + '@' + currentTime + '-top-' + str(
N) + 'items' + self.foldInfo + '.txt'
elif self.ranking.contains('-threshold'):
fileName = self.config[
'recommender'] + '@' + currentTime + '-threshold-' + str(
threshold) + self.foldInfo + '.txt'
FileIO.writeFile(outDir, fileName, res)
print 'The Result has been output to ', abspath(outDir), '.'
#output evaluation result
outDir = self.output['-dir']
fileName = self.config[
'recommender'] + '@' + currentTime + '-measure' + self.foldInfo + '.txt'
if self.ranking.contains('-topN'):
self.measure = Measure.rankingMeasure(self.dao.testSet_u, recList,
N)
elif self.ranking.contains('-threshold'):
origin = self.dao.testSet_u.copy()
for user in origin:
temp = {}
for item in origin[user]:
if origin[user][item] >= threshold:
temp[item] = threshold
origin[user] = temp
self.measure = Measure.rankingMeasure_threshold(
origin, recList, userN)
FileIO.writeFile(outDir, fileName, self.measure)
def execute(self):
self.readConfiguration()
if self.foldInfo == '[1]':
self.printAlgorConfig()
#load model from disk or build model
if self.isLoadModel:
print 'Loading model %s...' % (self.foldInfo)
self.loadModel()
else:
print 'Initializing model %s...' % (self.foldInfo)
self.initModel()
print 'Building Model %s...' % (self.foldInfo)
self.buildModel()
#preict the ratings or item ranking
print 'Predicting %s...' % (self.foldInfo)
if self.ranking.isMainOn():
self.evalRanking()
else:
self.evalRatings()
#save model
if self.isSaveModel:
print 'Saving model %s...' % (self.foldInfo)
self.saveModel()
return self.measure
def performance(self):
#res = [] # used to contain the text of the result
#res.append('userId itemId original prediction\n')
# predict
res = []
for ind, entry in enumerate(self.dao.testData):
user, item, rating = entry
# predict
prediction = self.predict(user, item)
# denormalize
prediction = denormalize(prediction, self.dao.rScale[-1],
self.dao.rScale[0])
#####################################
pred = self.checkRatingBoundary(prediction)
# add prediction in order to measure
res.append([user, item, rating, pred])
#res.append(user + ' ' + item + ' ' + str(rating) + ' ' + str(pred) + '\n')
#currentTime = strftime("%Y-%m-%d %H-%M-%S", localtime(time()))
# output prediction result
# if self.isOutput:
# outDir = self.output['-dir']
# fileName = self.config['recommender'] + '@' + currentTime + '-rating-predictions' + self.foldInfo + '.txt'
# FileIO.writeFile(outDir, fileName, res)
# print 'The Result has been output to ', abspath(outDir), '.'
# output evaluation result
# outDir = self.output['-dir']
# fileName = self.config['recommender'] + '@' + currentTime + '-measure' + self.foldInfo + '.txt'
self.measure = Measure.ratingMeasure(res)
return self.measure
class Recommender(object):
def __init__(self, conf, trainingSet, testSet, fold='[1]'):
self.currentTime = strftime("%Y-%m-%d %H-%M-%S", localtime(time()))
self.config = conf
self.data = None
self.isSaveModel = False
self.ranking = None
self.isLoadModel = False
self.output = None
self.isOutput = True
self.data = RatingDAO(self.config, trainingSet, testSet)
self.foldInfo = fold
self.evalSettings = LineConfig(self.config['evaluation.setup'])
self.measure = []
self.record = []
if self.evalSettings.contains('-cold'):
# evaluation on cold-start users
threshold = int(self.evalSettings['-cold'])
removedUser = {}
for user in self.data.testSet_u:
if user in self.data.trainSet_u and len(
self.data.trainSet_u[user]) > threshold:
removedUser[user] = 1
for user in removedUser:
del self.data.testSet_u[user]
testData = []
for item in self.data.testData:
if item[0] not in removedUser:
testData.append(item)
self.data.testData = testData
self.num_users, self.num_items, self.train_size = self.data.trainingSize(
)
def get_test_sample_data(self, max_sample=1000):
testSample = {}
keys = list(self.data.testSet_u.keys())
if len(self.data.testSet_u) <= max_sample:
testSample = self.data.testSet_u
else:
while True:
if len(testSample) == max_sample:
break
index = np.random.choice(len(self.data.testSet_u))
user = keys[index]
testSample[user] = self.data.testSet_u[user]
self.testSample = testSample
def get_test_map(self, K=1, L=-1):
self.K = K
self.L = L
if not hasattr(self, 'accountDAO') or self.accountDAO is None:
self.map_from_true_to_identify = {
i: i
for i in list(self.data.testSet_u.keys())
}
elif self.evaluation_conf.contains('-ul') and eval(
self.evaluation_conf['-ul']) > 0:
self.map_from_true_to_identify = self.get_map_from_true_to_identify(
k=K, index=L)
else:
self.map_from_true_to_identify = self.accountDAO.map_from_user_to_account
def readConfiguration(self):
self.algorName = self.config['recommender']
self.output = LineConfig(self.config['output.setup'])
self.isOutput = self.output.isMainOn()
self.ranking = LineConfig(self.config['item.ranking'])
def printAlgorConfig(self):
"show algorithm's configuration"
print('Algorithm:', self.config['recommender'])
print('Ratings dataset:', abspath(self.config['ratings']))
if LineConfig(self.config['evaluation.setup']).contains('-testSet'):
print(
'Test set:',
abspath(
LineConfig(self.config['evaluation.setup']).getOption(
'-testSet')))
# print 'Count of the users in training set: ',len()
print(
'Training set size: (user count: %d, item count %d, record count: %d)'
% (self.data.trainingSize()))
print(
'Test set size: (user count: %d, item count %d, record count: %d)'
% (self.data.testSize()))
print('=' * 80)
def initModel(self):
pass
def buildModel(self):
'build the model (for model-based algorithms )'
pass
def buildModel_tf(self):
'training model on tensorflow'
pass
def saveModel(self):
pass
def loadModel(self):
pass
def predict(self, u, i):
pass
def predictForRanking(self, u):
pass
def checkRatingBoundary(self, prediction):
if prediction > self.data.rScale[-1]:
return self.data.rScale[-1]
elif prediction < self.data.rScale[0]:
return self.data.rScale[0]
else:
return round(prediction, 3)
def evalRatings(self):
res = [] # used to contain the text of the result
res.append('userId itemId original prediction\n')
# predict
for ind, entry in enumerate(self.data.testData):
user, item, rating = entry
# predict
prediction = self.predict(user, item)
# denormalize
# prediction = denormalize(prediction,self.data.rScale[-1],self.data.rScale[0])
#####################################
pred = self.checkRatingBoundary(prediction)
# add prediction in order to measure
self.data.testData[ind].append(pred)
res.append(user + ' ' + item + ' ' + str(rating) + ' ' +
str(pred) + '\n')
currentTime = strftime("%Y-%m-%d %H-%M-%S", localtime(time()))
# output prediction result
if self.isOutput:
outDir = self.output['-dir']
fileName = self.config[
'recommender'] + '@' + currentTime + '-rating-predictions' + self.foldInfo + '.txt'
FileIO.writeFile(outDir, fileName, res)
print('The result has been output to ', abspath(outDir), '.')
# output evaluation result
outDir = self.output['-dir']
fileName = self.config[
'recommender'] + '@' + currentTime + '-measure' + self.foldInfo + '.txt'
self.measure = Measure.ratingMeasure(self.data.testData)
FileIO.writeFile(outDir, fileName, self.measure)
print('The result of %s %s:\n%s' %
(self.algorName, self.foldInfo, ''.join(self.measure)))
def get_map_from_true_to_identify(self, k=1, index=-1):
map_from_true_to_identify = {}
table = self.accountDAO.test_table[self.accountDAO.test_table.k == k]
reserve_list = [
ind for ind, users in enumerate(table['identify_user'])
if len(users)
]
table = table.iloc[reserve_list].copy()
table['identify_user_index'] = [
i_list[index] if len(i_list) and len(i_list) >= index + 1 else None
for i_list in table['identify_user']
]
# table['identify_user_index'].astype(int)
# table.groupby
identify_list = table.groupby(
'truth_user')['identify_user_index'].aggregate(list)
for truth, idens in identify_list.items():
i_users, counts = np.unique(np.array(idens)[np.array(idens) > 0],
return_counts=True)
if len(i_users) == 0:
continue
map_from_true_to_identify[truth] = i_users[np.argmax(counts)]
# identification_result = dict(zip(table['truth_user'].to_list(), table['identify_user'].to_list()))
# for key, value in identification_result.items():
# if len(value) and len(value) >= index + 1:
# try:
# map_from_true_to_identify[key] = value[index]
# except:
# print(key, value)
# map_from_true_to_identify[key] = value[index]
return map_from_true_to_identify
def get_recommendation(self, data_user, N):
user, identified_user, testSample_user = data_user
itemSet = {}
line = str(user) + ':'
predictedItems = self.predictForRanking(identified_user)
for id, rating in enumerate(predictedItems):
itemSet[self.data.id2item[id]] = rating
# if not hasattr(self, 'accountDAO') or self.accountDAO is None:
# ratedList, ratingList = self.data.userRated(user)
# else:
# ratedList = list(self.accountDAO.ground_visit[user].keys())
# for item in ratedList:
# del itemSet[item]
Nrecommendations = []
for item in itemSet:
if len(Nrecommendations) < N:
Nrecommendations.append((item, itemSet[item]))
else:
break
# Nrecommendations = list(itemSet.items())[:N]
Nrecommendations.sort(key=lambda d: d[1], reverse=True)
recommendations = [item[1] for item in Nrecommendations]
resNames = [item[0] for item in Nrecommendations]
# find the N biggest scores
for item in itemSet:
ind = N
l = 0
r = N - 1
if recommendations[r] < itemSet[item]:
while r >= l:
mid = (r - l) // 2 + l
if recommendations[mid] >= itemSet[item]:
l = mid + 1
elif recommendations[mid] < itemSet[item]:
r = mid - 1
if r < l:
ind = r
break
# move the items backwards
if ind < N - 2:
recommendations[ind + 2:] = recommendations[ind + 1:-1]
resNames[ind + 2:] = resNames[ind + 1:-1]
if ind < N - 1:
recommendations[ind + 1] = itemSet[item]
resNames[ind + 1] = item
# recList[user] = list(zip(resNames, recommendations))
# recList[user] = list(itemSet_sorted.items())[:N]
recList_user = list(zip(resNames, recommendations))
for item in recList_user:
line += ' (' + str(item[0]) + ',' + str(item[1]) + ')'
if item[0] in testSample_user:
line += '*'
line += '\n'
return user, line, recList_user
def evalRanking(self, write_to_file=True, use_now_time=False):
res = [] # used to contain the text of the result
if self.ranking.contains('-topN'):
top = self.ranking['-topN'].split(',')
top = [int(num) for num in top]
N = max(top)
if N > 100 or N < 0:
print(
'N can not be larger than 100! It has been reassigned with 10'
)
N = 10
if N > len(self.data.item):
N = len(self.data.item)
else:
print('No correct evaluation metric is specified!')
exit(-1)
res.append(
'userId: recommendations in (itemId, ranking score) pairs, * means the item matches.\n'
)
# predict
recList = {}
userN = {}
testSample = self.testSample
# # multiprocessing way
# pool = Pool(12)
# dataset = []
# for user, testSample_u in testSample.items():
# identified_user = self.map_from_true_to_identify.get(user, -1)
# if identified_user == -1:
# continue
# dataset.append([user, identified_user, testSample_u])
#
# result_generator = pool.imap_unordered(partial(self.get_recommendation, N=N), dataset)
# for result in tqdm(result_generator, total=len(dataset), desc='Measuring [{}]'):
# user, line, recList_user = result
# recList[user] = recList_user
# res.append(line)
# pool.close()
# pool.join()
testSample_copy = testSample.copy()
for i, user in tqdm(enumerate(testSample),
total=len(testSample),
desc='Measuring [{}]'.format(self.algorName)):
identified_user = self.map_from_true_to_identify.get(user, -1)
if identified_user == -1:
del testSample_copy[user]
continue
user, line, recList_user = self.get_recommendation(
(user, identified_user, testSample[user]), N)
recList[user] = recList_user
res.append(line)
self.measure = Measure.rankingMeasure(testSample_copy, recList, top)
try:
self.measure.append("C:{}\n".format(self.C))
except:
pass
try:
self.measure.append("L:{}\n".format(self.L))
except:
pass
try:
self.measure.append("K:{}\n".format(self.K))
except:
pass
try:
self.measure.append("N:{}\n".format(self.N))
except:
pass
if use_now_time:
currentTime = strftime("%Y-%m-%d %H-%M-%S", localtime(time()))
else:
currentTime = self.currentTime
if write_to_file:
# output prediction result
if False and self.isOutput:
fileName = ''
outDir = self.output['-dir']
fileName = self.config[
'recommender'] + '@' + currentTime + '-top-' + str(
N) + 'items' + self.foldInfo + '.txt'
FileIO.writeFile(outDir, fileName, res)
# output evaluation result
outDir = self.output['-dir']
try:
fileName = self.config[
'recommender'] + '@' + currentTime + '-measure' + self.foldInfo + '_C{}'.format(
self.C) + '.txt'
except:
fileName = self.config[
'recommender'] + '@' + currentTime + '-measure' + self.foldInfo + '.txt'
FileIO.writeFile(outDir, fileName, self.measure)
# FileIO.writeFile(outDir, fileName, "C:{}".format(self.C))
print('The result has been output to ', abspath(outDir), '.')
print('The result of %s %s:\n%s' %
(self.algorName, self.foldInfo, ''.join(self.measure)))
def execute(self):
self.readConfiguration()
if self.foldInfo == '[1]':
self.printAlgorConfig()
# load model from disk or build model
if self.isLoadModel:
print('Loading model %s...' % (self.foldInfo))
self.loadModel()
else:
print('Initializing model %s...' % (self.foldInfo))
self.initModel()
print('Building Model %s...' % (self.foldInfo))
try:
import tensorflow
if self.evalSettings.contains('-tf'):
self.buildModel_tf()
else:
self.buildModel()
except ImportError:
self.buildModel()
# preict the ratings or item ranking
print('Predicting %s...' % (self.foldInfo))
if self.ranking.isMainOn():
self.evalRanking()
else:
self.evalRatings()
# save model
if self.isSaveModel:
print('Saving model %s...' % (self.foldInfo))
self.saveModel()
# with open(self.foldInfo+'measure.txt','w') as f:
# f.writelines(self.record)
return self.measure
class Recommender(object):
def __init__(self, conf, trainingSet=None, testSet=None, fold='[1]'):
self.config = conf
self.isSaveModel = False
self.isLoadModel = False
self.isOutput = True
self.data = Record(self.config, trainingSet, testSet)
self.foldInfo = fold
self.evalConfig = LineConfig(self.config['evaluation.setup'])
if self.evalConfig.contains('-target'):
self.recType = self.evalConfig['-target']
else:
self.recType = 'track'
if LineConfig(self.config['evaluation.setup']).contains('-cold'):
#evaluation on cold-start users
threshold = int(
LineConfig(self.config['evaluation.setup'])['-cold'])
removedUser = []
removedTrack = defaultdict(list)
#for user in self.data.testSet:
# if user in self.data.userRecord and len(self.data.userRecord[user])>threshold:
# removedUser.append(user)
for user in self.data.testSet:
if user in self.data.userRecord:
for item in self.data.testSet[user]:
if len(self.data.trackRecord[item]) > threshold:
removedTrack[user].append(item)
for user in removedTrack:
for item in removedTrack[user]:
del self.data.testSet[user][item]
if len(self.data.testSet[user]) == 0:
del self.data.testSet[user]
#for user in removedUser:
# del self.data.testSet[user]
if LineConfig(self.config['evaluation.setup']).contains('-sample'):
userList = list(self.data.testSet.keys())
removedUser = userList[:int(len(userList) * 0.9)]
for user in removedUser:
del self.data.testSet[user]
def readConfiguration(self):
self.algorName = self.config['recommender']
self.output = LineConfig(self.config['output.setup'])
self.isOutput = self.output.isMainOn()
self.ranking = LineConfig(self.config['item.ranking'])
def printAlgorConfig(self):
"show algorithm's configuration"
print('Algorithm:', self.config['recommender'])
print('Training set:', abspath(self.config['record']))
if LineConfig(self.config['evaluation.setup']).contains('-testSet'):
print(
'Test set:',
abspath(
LineConfig(self.config['evaluation.setup']).getOption(
'-testSet')))
#print 'Count of the users in training set: ',len()
self.data.printTrainingSize()
print('=' * 80)
def initModel(self):
pass
def buildModel(self):
'build the model (for model-based algorithms )'
pass
def saveModel(self):
pass
def loadModel(self):
pass
def predict(self, user):
return []
def evalRanking(self):
res = [] # used to contain the text of the result
N = 0
threshold = 0
top = self.ranking['-topN'].split(',')
top = [int(num) for num in top]
N = int(top[-1])
if N > 100 or N < 0:
print(
'N can not be larger than 100! It has been reassigned with 10')
N = 10
res.append(
'userId: recommendations in (itemId, ranking score) pairs, * means the item matches, $ means the unpop item\n'
)
# predict
recList = {}
userCount = len(self.data.testSet)
for i, user in enumerate(self.data.testSet):
num_pop = 0
line = user + ':'
if user in self.data.userRecord:
predictedItems = self.predict(user)
else:
predictedItems = ['0'] * N
predicted = {}
for k, item in enumerate(predictedItems):
predicted[item] = k
for item in self.data.userRecord[user]:
if item[self.recType] in predicted:
del predicted[item[self.recType]]
predicted = sorted(predicted.items(), key=lambda d: d[1])
predictedItems = [item[0] for item in predicted]
recList[user] = predictedItems[:N]
#print('user', user, 'the recList:', type(self.data.testSet[user]))
if i % 100 == 0:
print(self.algorName, self.foldInfo,
'progress:' + str(i) + '/' + str(userCount))
for item in recList[user]:
if item in self.data.testSet[user]:
line += '*'
if item in self.data.PopTrack:
num_pop += 1
line += '$'
line += item + ','
line += '\n'
res.append(line)
currentTime = strftime("%Y-%m-%d %H-%M-%S", localtime(time()))
# output prediction result
if self.isOutput:
fileName = ''
outDir = self.output['-dir']
if self.ranking.contains('-topN'):
fileName = self.config['recommender'] + '@' + currentTime + '-top-' + self.ranking['-topN']\
+ 'items' + self.foldInfo + '.txt'
FileIO.writeFile(outDir, fileName, res)
print('The result has been output to ', abspath(outDir), '.')
# output evaluation result
outDir = self.output['-dir']
fileName = self.config[
'recommender'] + '@' + currentTime + '-measure' + self.foldInfo + '.txt'
self.measure = Measure.rankingMeasure(self.data.testSet, recList, top,
self.data.getSize(self.recType))
FileIO.writeFile(outDir, fileName, self.measure)
print('The result of %s %s:\n%s' %
(self.algorName, self.foldInfo, ''.join(self.measure)))
def execute(self):
self.readConfiguration()
if self.foldInfo == '[1]':
self.printAlgorConfig()
#load model from disk or build model
if self.isLoadModel:
print('Loading model %s...' % (self.foldInfo))
self.loadModel()
else:
print('Initializing model %s...' % (self.foldInfo))
self.initModel()
print('Building Model %s...' % (self.foldInfo))
self.buildModel()
#preict the ratings or item ranking
print('Predicting %s...' % (self.foldInfo))
self.evalRanking()
#save model
if self.isSaveModel:
print('Saving model %s...' % (self.foldInfo))
self.saveModel()
return self.measure