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 evalRanking(self):
res = [] # used to contain the text of the result
N = 0
threshold = 0
if self.ranking.contains('-topN'):
N = int(self.ranking['-topN'])
if N>100 or N<0:
N=100
elif self.ranking.contains('-threshold'):
threshold = float(self.ranking['-threshold'])
res.append('userId: recommendations in (itemId, ranking score) pairs, * means the item is matched\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:
if not self.dao.rating(user,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 self.ranking.contains('-threshold'):
if prediction > threshold:
itemSet.append((item,prediction))
else:
itemSet.append((item,prediction))
itemSet = sorted(itemSet, 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:
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'):
self.measure = Measure.rankingMeasure_threshold(self.dao.testSet_u, recList, userN)
FileIO.writeFile(outDir, fileName, self.measure)