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 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 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
def rating_performance(self):
res = []
for ind, entry in enumerate(self.data.testData):
user, item, rating = entry
# predict
prediction = self.predict(user, item)
pred = self.checkRatingBoundary(prediction)
res.append([user,item,rating,pred])
self.measure = Measure.ratingMeasure(res)
return self.measure
def rating_performance(self):
res = []
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
res.append([user,item,rating,pred])
self.measure = Measure.ratingMeasure(res)
return self.measure
