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 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 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 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 ranking_performance(self):
N = 10
recList = {}
testSample = {}
for user in self.data.testSet_u:
if len(testSample) == 300:
break
testSample[user] = self.data.testSet_u[user]
for user in testSample:
itemSet = {}
predictedItems = self.predictForRanking(user)
for id, rating in enumerate(predictedItems):
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 K biggest scores
for item in itemSet:
ind = N
l = 0
r = N - 1
if recommendations[r] < itemSet[item]:
while True:
mid = (l + r) / 2
if recommendations[mid] >= itemSet[item]:
l = mid + 1
elif recommendations[mid] < itemSet[item]:
r = mid - 1
if r < l:
ind = r
break
# ind = bisect(recommendations, itemSet[item])
if ind < N - 1:
recommendations[ind + 1] = itemSet[item]
resNames[ind + 1] = item
recList[user] = zip(resNames, recommendations)
measure = Measure.rankingMeasure(testSample, recList, [10])
print '-'*80
print 'Ranking Performance '+self.foldInfo+' (Top-10 On 300 sampled users)'
for m in measure[1:]:
print m.strip()
print '-'*80
#self.record.append(measure[3].strip()+' '+measure[4])
return 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, * means the item is matched\n'
)
# predict
topNSet = {}
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 = round(prediction, 4)
#pred = self.checkRatingBoundary(prediction)
#####################################
# add prediction in order to measure
itemSet.append((item, prediction))
itemSet.sort(key=lambda d: d[1], reverse=True)
topNSet[user] = itemSet[0:N]
if i % 100 == 0:
print self.algorName, self.foldInfo, 'progress:' + str(
i) + '/' + str(userCount)
for item in topNSet[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']
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.dao.testSet_u, topNSet, N)
FileIO.writeFile(outDir, fileName, 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
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:
pred = self.predict(userId, itemId)
# 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 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 ranking_performance(self,iteration):
#for quick evaluation, we only rank 2000 items
#results of 1000 users would be evaluated
N = 10
recList = {}
testSample = {}
for user in self.data.testSet_u:
if len(testSample) == 1000:
break
if user not in self.data.trainSet_u:
continue
testSample[user] = self.data.testSet_u[user]
for user in testSample:
itemSet = {}
predictedItems = self.predictForRanking(user)
for id, rating in enumerate(predictedItems[:2000]):
itemSet[self.data.id2item[id]] = rating
ratedList, ratingList = self.data.userRated(user)
for item in ratedList:
if item in itemSet:
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 K biggest scores
for item in itemSet:
ind = N
l = 0
r = N - 1
if recommendations[r] < itemSet[item]:
while True:
mid = int((l + r) / 2)
if recommendations[mid] >= itemSet[item]:
l = mid + 1
elif recommendations[mid] < itemSet[item]:
r = mid - 1
if r < l:
ind = r
break
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))
measure = Measure.rankingMeasure(testSample, recList, [10])
if len(self.bestPerformance)>0:
count = 0
performance = {}
for m in measure[1:]:
k,v = m.strip().split(':')
performance[k]=float(v)
for k in self.bestPerformance[1]:
if self.bestPerformance[1][k] > performance[k]:
count += 1
else:
count -=1
if count<0:
self.bestPerformance[1]=performance
self.bestPerformance[0]=iteration
self.saveModel()
else:
self.bestPerformance.append(iteration)
performance = {}
for m in measure[1:]:
k,v = m.strip().split(':')
performance[k]=float(v)
self.bestPerformance.append(performance)
self.saveModel()
print('-'*120)
print('Quick Ranking Performance '+self.foldInfo+' (Top-10 Item Recommendation On 1000 sampled users)')
measure = [m.strip() for m in measure[1:]]
print('*Current Performance*')
print('iteration:',iteration,' | '.join(measure))
bp = ''
# for k in self.bestPerformance[1]:
# bp+=k+':'+str(self.bestPerformance[1][k])+' | '
bp += 'Precision'+':'+str(self.bestPerformance[1]['Precision'])+' | '
bp += 'Recall' + ':' + str(self.bestPerformance[1]['Recall']) + ' | '
bp += 'F1' + ':' + str(self.bestPerformance[1]['F1']) + ' | '
bp += 'MAP' + ':' + str(self.bestPerformance[1]['MAP']) + ' | '
bp += 'MDCG' + ':' + str(self.bestPerformance[1]['NDCG'])
print('*Best Performance* ')
print('iteration:',self.bestPerformance[0],bp)
print('-'*120)
return measure
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.\n'
)
# predict
recList = {}
userCount = len(self.data.testSet)
for i, user in enumerate(self.data.testSet):
line = user + ':'
if self.data.userRecord.has_key(user):
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 predicted.has_key(item[self.recType]):
del predicted[item[self.recType]]
predicted = sorted(predicted.iteritems(), key=lambda d: d[1])
predictedItems = [item[0] for item in predicted]
recList[user] = predictedItems[:N]
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):
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 evalRanking(self):
res = [] # used to contain the text of the result
N = 0
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
res.append('userId: recommendations in (itemId, ranking score) pairs, * means the item matches.\n')
# predict
recList = {}
userCount = len(self.data.testSet)
for i, user in enumerate(self.data.testSet):
itemSet = {}
line = user + ':'
predictedItems = self.predict(user)
for id, score in enumerate(predictedItems):
itemSet[self.data.id2name[self.recType][id]] = score
for item in self.data.userRecord[user]:
try:
del itemSet[item[self.recType]]
except KeyError:
pass
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]
# itemSet = sorted(itemSet.iteritems(), key=lambda d: d[1], reverse=True)
# if bTopN:
# find the K biggest scores
for item in itemSet:
ind = N
l = 0
r = N - 1
if recommendations[r] < itemSet[item]:
while True:
mid = (l + r) // 2
if recommendations[mid] >= itemSet[item]:
l = mid + 1
elif recommendations[mid] < itemSet[item]:
r = mid - 1
else:
ind = mid
break
if r < l:
ind = r
break
# ind = bisect(recommendations, itemSet[item])
if ind < N - 1:
recommendations[ind + 1] = itemSet[item]
resNames[ind + 1] = item
recList[user] = resNames
if i % 100 == 0:
print (self.algorName, self.foldInfo, 'progress:' + str(i) + '/' + str(userCount))
for item in recList[user]:
line += item
if item in self.data.testSet[user]:
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-' + 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 ranking_performance(self):
N = 10
itemcount = 0
recList = {}
testSample = {}
for user in self.data.testSet:
itemcount+=len(self.data.testSet[user])
if len(testSample) == 300:
break
testSample[user] = self.data.testSet[user]
for user in testSample:
itemSet = {}
predictedItems = self.predict(user)
for id, rating in enumerate(predictedItems):
itemSet[self.data.id2name['track'][id]] = rating
ratedList = self.data.testSet[user].keys()
ratingList = self.data.testSet[user].values()
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 K biggest scores
for item in itemSet:
ind = N
l = 0
r = N - 1
if recommendations[r] < itemSet[item]:
while True:
mid = int((l + r) / 2)
if recommendations[mid] >= itemSet[item]:
l = mid + 1
elif recommendations[mid] < itemSet[item]:
r = mid - 1
if r < l:
ind = r
break
# ind = bisect(recommendations, itemSet[item])
if ind < N - 1:
recommendations[ind + 1] = itemSet[item]
resNames[ind + 1] = item
recList[user] = resNames
measure = Measure.rankingMeasure(testSample, recList, [10], itemcount)
print ('-'*80)
print ('Ranking Performance '+self.foldInfo+' (Top-10 On 300 sampled users)')
for m in measure[1:]:
print (m.strip())
print ('-'*80)
return 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 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
if N > len(self.dao.item):
N = len(self.dao.item)
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 + ':'
predictedItems = self.predictForRanking(user)
# predictedItems = denormalize(predictedItems, self.dao.rScale[-1], self.dao.rScale[0])
for id, rating in enumerate(predictedItems):
# if not self.dao.rating(user, self.dao.id2item[id]):
# prediction = self.checkRatingBoundary(prediction)
# pred = self.checkRatingBoundary(prediction)
#####################################
# add prediction in order to measure
# if bThres:
# if rating > threshold:
# itemSet[self.dao.id2item[id]]= rating
# else:
itemSet[self.dao.id2item[id]] = rating
ratedList, ratingList = self.dao.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]
# itemSet = sorted(itemSet.iteritems(), key=lambda d: d[1], reverse=True)
# if bTopN:
# find the K biggest scores
for item in itemSet:
ind = N
l = 0
r = N - 1
if recommendations[r] < itemSet[item]:
while True:
mid = (l + r) / 2
if recommendations[mid] >= itemSet[item]:
l = mid + 1
elif recommendations[mid] < itemSet[item]:
r = mid - 1
else:
ind = mid
break
if r < l:
ind = r
break
# ind = bisect(recommendations, itemSet[item])
if ind < N - 1:
recommendations[ind + 1] = itemSet[item]
resNames[ind + 1] = item
recList[user] = zip(resNames, recommendations)
# elif bThres:
# itemSet = sorted(itemSet.iteritems(), key=lambda d: d[1], reverse=True)
# 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)
print 'The result of %s %s:\n%s' % (self.algorName, self.foldInfo,
''.join(self.measure))
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)))