def _createConferenceObject(self, request):
"""Create or update Conference object, returning ConferenceForm/request."""
# preload necessary data items
user = endpoints.get_current_user()
if not user:
raise endpoints.UnauthorizedException('Authorization required')
user_id = util.getUserId(user)
if not request.name:
raise endpoints.BadRequestException("Conference 'name' field required")
# copy ConferenceForm/ProtoRPC Message into dict
data = {field.name: getattr(request, field.name) for field in request.all_fields()}
del data['websafeKey']
del data['organizerDisplayName']
# add default values for those missing (both data model & outbound Message)
for df in DEFAULTS:
if data[df] in (None, []):
data[df] = DEFAULTS[df]
setattr(request, df, DEFAULTS[df])
# convert dates from strings to Date objects; set month based on start_date
if data['startDate']:
data['startDate'] = datetime.strptime(data['startDate'][:10], "%Y-%m-%d").date()
data['month'] = data['startDate'].month
else:
data['month'] = 0
if data['endDate']:
data['endDate'] = datetime.strptime(data['endDate'][:10], "%Y-%m-%d").date()
# set seatsAvailable to be same as maxAttendees on creation
# both for data model & outbound Message
if data["maxAttendees"] > 0:
data["seatsAvailable"] = data["maxAttendees"]
setattr(request, "seatsAvailable", data["maxAttendees"])
# make Profile Key from user ID
p_key = ndb.Key(Profile, user_id)
# allocate new Conference ID with Profile key as parent
c_id = Conference.allocate_ids(size=1, parent=p_key)[0]
# make Conference key from ID
c_key = ndb.Key(Conference, c_id, parent=p_key)
data['key'] = c_key
data['organizerUserId'] = request.organizerUserId = user_id
# create Conference & return (modified) ConferenceForm
Conference(**data).put()
return request
def getConferencesCreated(self, request):
"""Return conferences created by user."""
# make sure user is authed
user = endpoints.get_current_user()
if not user:
raise endpoints.UnauthorizedException('Authorization required')
# make profile key
p_key = ndb.Key(Profile, util.getUserId(user))
# create ancestor query for this user
conferences = Conference.query(ancestor=p_key)
# get the user profile and display name
prof = p_key.get()
displayName = getattr(prof, 'displayName')
# return set of ConferenceForm objects per Conference
return ConferenceForms(
items=[self._copyConferenceToForm(conf, displayName) for conf in conferences]
)
def _getProfileFromUser(self):
"""Return user Profile from datastore, creating new one if non-existent."""
user = endpoints.get_current_user()
if not user:
raise endpoints.UnauthorizedException('Authorization required')
user_id = util.getUserId(user)
p_key = ndb.Key(Profile, user_id)
profile = p_key.get()
if not profile:
profile = Profile(
key = p_key,
displayName = user.nickname(),
mainEmail= user.email(),
teeShirtSize = str(TeeShirtSize.NOT_SPECIFIED),
)
profile.put()
return profile # return Profile
def ldaa(dic, corpus, tfidf, bContentList, newUniBContentListString, topicNum):
corpus_tfidf = tfidf[corpus]
print '------------------type(corpus_tfidf):', type(corpus_tfidf)
# for i in corpus_tfidf:
# print i
lda = models.LdaModel(corpus_tfidf, id2word=dic, num_topics=topicNum)
ldaOut = lda.print_topics(topicNum)
li = 5
vec = [(0, 1), (4, 1)]
vec = dic.doc2bow(bContentList[li])
# get similarity matrix of len(bContentList) * len(bContentList)
index = similarities.MatrixSimilarity(lda[corpus])
simMatrix = []
# get the Similarity Matrix(eg: 100 * 100) of all barrages,
for bIndex in range(len(bContentList)):
vec = bContentList[bIndex]
vec_bow = dic.doc2bow(bContentList[bIndex])
vec_lda = lda[vec_bow]
sims = index[vec_lda]
# print list(enumerate(sims))
# sorted with similarity from high to low
# sims = sorted(enumerate(sims), key=lambda item: -item[1])
# print sims, len(sims), type(sims)
simMatrix.append(list(enumerate(sims)))
# eg: simMatrix[1] = [(0, 0.91061151), (1, 0.99999994), (2, 0.99999994), (3, 0.99999994), (4, 0.73748994), (5, 0.81874228)......]
# print len(simMatrix), simMatrix[1]
# sys.exit()
# print all lda topics words
# such as:
# 0.002*"合影" + 0.002*"钱" + 0.002*"撒花" + 0.002*"没" + 0.002*"完结" + 0.002*"看" + 0.002*"啊" + 0.002*"之" + 0.002*"湫" + 0.002*"一个"
# 0.002*"买" + 0.002*"第一次" + 0.002*"支持" + 0.002*"啊" + 0.002*"没" + 0.002*"完结" + 0.002*"湫" + 0.002*"国漫" + 0.002*"撒花" + 0.002*"b"
# 0.004*"第一次" + 0.003*"湫" + 0.003*"合影" + 0.003*"在" + 0.003*"存活" + 0.003*"买" + 0.003*"确认" + 0.003*"啊" + 0.003*"椿" + 0.002*"撒花"
# 0.003*"完结" + 0.003*"撒花" + 0.003*"钱" + 0.003*"合影" + 0.002*"再见" + 0.002*"没" + 0.002*"啊" + 0.002*"湫" + 0.002*"好" + 0.001*"第一次"
# 0.003*"存活" + 0.003*"确认" + 0.002*"合影" + 0.002*"没" + 0.002*"钱" + 0.002*"秋水共长天一色" + 0.002*"第一次" + 0.001*"靠" + 0.001*"也" + 0.001*"生日"
for i in ldaOut:
r = i[1].encode('utf-8')
print r
for i in ldaOut:
r = i[1].encode('utf-8')
print 'Topic', ldaOut.index(i), ':',
util.printList(rmNum(r))
# sys.exit()
print type(ldaOut[0])
print type(ldaOut[0][0])
corpus_lda = lda[corpus_tfidf]
resList = []
iii = 0
# eg: doc [(0, 0.033333333333334041), (1, 0.033333333333659149), (2, 0.03333333333337106), (3, 0.033333333333336511), (4, 0.033333333333333631), (5, 0.033333333577374141), (6, 0.033333333333333381), (7, 0.53333330176939997), (8, 0.033333333641347308), (9, 0.033333333333333388), (10, 0.033333333333333409), (11, 0.033333358397907714), (12, 0.033333333333333381), (13, 0.033333333333333368), (14, 0.033333339280269603)]
for doc in corpus_lda:
# eg: res = (3, 0.72867093662442284), res has 72% posibility to be in type 3
res = getMax(doc)
resList.append(res)
print '---type(corpus_tfidf), type(corpus_lda)', type(corpus_tfidf), type(
corpus_lda)
print '---len(resList)', len(resList)
# len = topicNum
simMatrixTopicList = []
for topicId in range(topicNum):
simMatrixTopic = [
i for i in range(len(resList)) if resList[i][0] == topicId
]
print topicId, 'topic has:', len(simMatrixTopic), 'barrage'
simMatrixTopicList.append(simMatrixTopic)
# print len(simMatrixTopic), simMatrixTopic
# without square
# # inner distance
# # sum of all similarity of i'th row
# iRow = 0.0
# num = 0
# innDisMatrix = [0.0 for i in range(topicNum)]
# for topicId in range(topicNum):
# for i in range(len(simMatrixTopicList[topicId])-1):
# for j in range(i+1, len(simMatrixTopicList[topicId])):
# # print simMatrix[simMatrixTopicList[topicId][i]][simMatrixTopicList[topicId][j]][1]
# iRow += simMatrix[simMatrixTopicList[topicId][i]][simMatrixTopicList[topicId][j]][1]
# # print topicId, 'topic, num:', num
# lenOfIRow = len(simMatrixTopicList[topicId])
# numOfIRow = (1 + lenOfIRow - 1) * (lenOfIRow - 1) / 2
# innDisMatrix[topicId] = iRow/numOfIRow
# iRow = 0.0
# print 'inner distance:', innDisMatrix
#
# aveInnDis = sum(innDisMatrix) / len(innDisMatrix)
# print 'average inner distance:', aveInnDis
#
# # external distance
# cols = topicNum
# rows = topicNum
# extDisMatrix = [[0.0 for col in range(cols)] for row in range(rows)]
# iRow = 0.0
# for topicId in range(topicNum):
# for ti2 in range(topicId+1, topicNum):
# for i in range(len(simMatrixTopicList[topicId])):
# for j in range(len(simMatrixTopicList[ti2])):
# iRow += simMatrix[simMatrixTopicList[topicId][i]][simMatrixTopicList[ti2][j]][1]
# # iRow += iRow
# # print iRow
# lenOfIRow = len(simMatrixTopicList[topicId]) * len(simMatrixTopicList[ti2])
# extDisMatrix[topicId][ti2] = iRow / float(lenOfIRow)
# iRow = 0.0
#
# print 'external distance:', extDisMatrix
#
# totExtDis = 0
# aveExtDis = 0
# num = 0
# for i in extDisMatrix:
# for j in i:
# if j != 0:
# totExtDis += j
# num += 1
# aveExtDis = totExtDis / float(num)
#
# print 'average external distance:', aveExtDis
# print 'inner/external value:', aveInnDis/aveExtDis
# within square(**2)
# inner distance
# sum of all similarity of i'th row
iRow = 0.0
num = 0
innDisMatrix = [0.0 for i in range(topicNum)]
# innDisMatrixNum[0]: the number of similarity value every topic
innDisMatrixNum = [0.0 for i in range(topicNum)]
for topicId in range(topicNum):
for i in range(len(simMatrixTopicList[topicId]) - 1):
for j in range(i + 1, len(simMatrixTopicList[topicId])):
iRow += (simMatrix[simMatrixTopicList[topicId][i]][
simMatrixTopicList[topicId][j]][1])**2
# print topicId, 'topic, num:', num
lenOfIRow = len(simMatrixTopicList[topicId])
numOfIRow = (1 + lenOfIRow - 1) * (lenOfIRow - 1) / 2
innDisMatrix[topicId] = iRow / numOfIRow
# innDisMatrixNum[topicId] = numOfIRow
iRow = 0.0
print 'inner distance:', innDisMatrix
aveInnDis = 1 / (sum(innDisMatrix) / topicNum)
print 'average inner distance:', aveInnDis
# external distance
cols = topicNum
rows = topicNum
extDisMatrix = [[0.0 for col in range(cols)] for row in range(rows)]
# extDisMatrixNum[0]: the number of similarity value every topic
# extDisMatrixNum = [[0.0 for col in range(cols)] for row in range(rows)]
iRow = 0.0
# countt = 0
for topicId in range(topicNum):
for ti2 in range(topicId + 1, topicNum):
for i in range(len(simMatrixTopicList[topicId])):
for j in range(len(simMatrixTopicList[ti2])):
iRow += (simMatrix[simMatrixTopicList[topicId][i]][
simMatrixTopicList[ti2][j]][1])**2
# countt += 1
# print iRow
iRowNum = len(simMatrixTopicList[topicId]) * len(
simMatrixTopicList[ti2])
# print 'iRowNum:', iRowNum, 'countt:', countt
extDisMatrix[topicId][ti2] = iRow / iRowNum
iRow = 0.0
# countt = 0
print 'external distance:', extDisMatrix
totExtDis = 0
aveExtDis = 0
for i in extDisMatrix:
for j in i:
totExtDis += j
extNoneZeroNum = (1 + cols - 1) * (cols - 1) / 2
aveExtDis = 1 / (totExtDis / extNoneZeroNum)
print 'average external distance:', aveExtDis
print 'inner/external value:', aveInnDis / aveExtDis
# return aveInnDis, aveExtDis
# sys.exit()
# topic possibility distribution in user profile
topicPosi = []
for topicId in range(topicNum):
posiList = [i[1] for i in resList if i[0] == topicId]
# average accuracy rate
possi = sum(posiList) / len(posiList)
topicPosi.append(possi)
# sys.exit()
fullPath = os.getcwd()
# concatenate full path
userCodeIdListFilePath = fullPath + '/data/users/' + vCid + '/userIdList.txt'
userCodeIdList = util.getUserCodeIdList(userCodeIdListFilePath)
# for i in userCodeIdList:
# print i
favTagTlist = util.getFilesOfDir(vCid)
# concatenate full path
favTagTlist = [
fullPath + '/data/users/' + vCid + '/' + tagT for tagT in favTagTlist
]
for i in favTagTlist:
print i
tagMatrix, tagVNumMatrix, userList, catAll = clustering.scanAllTags(
favTagTlist)
catNum = len(catAll)
# eg: topicDist =
# [[125. 126. 83. 18. 121. 44. 72. 0. 108. 113. 46. 66. 114. 0. 109.],
# [ 799. 785. 558. 141. 737. 286. 425. 2. 659. 611. 376. 460. 765. 0. 657.],
# [ 308. 321. 238. 48. 272. 116. 162. 0. 259. 236. 135. 173. 284. 1. 267.],
# [ 557. 540. 378. 99. 490. 215. 315. 0. 457. 424. 232. 295. 514. 0. 449.],
# [ 537. 535. 361. 86. 477. 176. 293. 0. 463. 416. 234. 297. 509. 0. 444.]]
# 音乐 动画 番剧 广告 电影 时尚 舞蹈 公告 游戏 鬼畜 娱乐 电视剧 生活 活动 科技
topicDist = np.zeros((topicNum, catNum))
# every percentage of topic
topicPercDist = np.zeros((topicNum, catNum))
topicDistNoneNum = np.zeros(topicNum)
userIdNoneNum = 0
# topic index list: [0, 1, 2, 3, 4]
topicNumList = range(topicNum)
# a list of: all users' barrage data of a topic
aTopicNewUniBContentListString = []
topicUserNumList = []
for i in topicNumList:
aTopicNewUniBContentListString.append([])
topicUserNumList.append(0)
# used to calculate the number of topicPercDist added
topicPercDistAddNum = np.zeros(topicNum)
for i in range(len(resList)):
# print i
topicId = resList[i][0]
if topicId in topicNumList:
userId = util.getUserId(newUniBContentListString[i][0],
userCodeIdList)
# print newUniBContentListString[i][0], userId
aTopicNewUniBContentListString[topicId].append(
newUniBContentListString[i])
topicUserNumList[topicId] += 1
if userId is not None:
# print userId, favTagTlist, type(favTagTlist)
res = clustering.getTagLineOfUser(tagMatrix, tagVNumMatrix,
userList, userId)
if res is not None:
tagLineOfUI, tagVNumLineOfUI = res
# userId is not in the list
else:
continue
if tagLineOfUI is not None:
# print len(tagLineOfUI), userId, tagLineOfUI
topicDist[topicId] += tagLineOfUI
# the perc distribution of tagVideo number of a user
tagVPercLineOfUI = np.around(
tagVNumLineOfUI / float(sum(tagVNumLineOfUI)), 3)
topicPercDist[topicId] += tagVPercLineOfUI
topicPercDistAddNum[topicId] += 1
else:
topicDistNoneNum += 1
else:
userIdNoneNum += 1
topicWordBListL = []
# topicWordBListLSec = []
topicWordBListL2 = []
topicWordBListL3 = []
# print Top 10 frequent words in a topic & the barrageList of the topic, in one time
for i in topicNumList:
bContentList2 = xmlDL.divideSent(aTopicNewUniBContentListString[i], 0)
wordList2 = getMostWord(bContentList2, 20)
print '------------topic', i, ':',
for j in wordList2:
print j[0],
print
top10weightActor = []
top10weightNoActor = []
# print Top 10 frequent words in a topic & the barrageList of the topic
for i in topicNumList:
print '------------topic', i, '-------------users:', topicUserNumList
# divideSent(,0) no actors' name
bContentList = xmlDL.divideSent(aTopicNewUniBContentListString[i], 0)
wordList = getMostWord(bContentList, 20)
for j in wordList:
print j[0], j[1]
for j in aTopicNewUniBContentListString[i]:
# eg: abb5230a 417.671 灵尊：哟，火柴棍
if wordList[0][0].encode('utf-8') in j[2]:
util.printList(j)
print 'wordList[0][0]', wordList[0][0]
# the index list of all barrage in topic i which contains the 1st frequent word in topic i
topicIWord1BList = [
j[1] for j in aTopicNewUniBContentListString[i]
if wordList[0][0].encode('utf-8') in j[2]
]
topicWordBListL.append(topicIWord1BList)
# second word
# topicIWord1BListSec = [j[1] for j in aTopicNewUniBContentListString[i] if wordList[1][0].encode('utf-8') in j[2]]
# topicWordBListLSec.append(topicIWord1BListSec)
# 0.002*"合影" + 0.002*"钱" + 0.002*"撒花" + 0.002*"没" + 0.002*"完结" + 0.002*"看" + 0.002*"啊" + 0.002*"之" + 0.002*"湫" + 0.002*"一个"
# "合影" + "钱" + "撒花" + "没" + .....
wordList2 = rmNum(ldaOut[i][1].encode('utf-8'))
# get the most weight word in wordList2(after deleting actors' name)
# eg: wordList2 = '''0.440*"小凡" + 0.030*"鲸鱼" + 0.018*"上线" + 0.014*"灰" + 0.013*"套路" + 0.012*"官方" + 0.010*"小痴" + 0.009*"滴血" + 0.009*"姐姐" + 0.009*"嘴"'''
# firstWord = '上线', (default firstWord is '小凡')
firstWord = wordList2[0]
for word in wordList2:
# if '小葵' in wordList2:
# firstWord = '小葵'
# top10weightNoActor.append('小葵')
# break
if word in util.getTxtList(
'data/stopWord/jiebaNewWord_Qingyunzhi.txt'):
continue
else:
firstWord = word
top10weightNoActor.append(word)
break
top10weightActor.append(wordList2[0])
# the index list of all barrage in topic i which contains the 1st frequent word(with weight) in topic i
topicIWord1BList2 = [
j[1] for j in aTopicNewUniBContentListString[i]
if firstWord in j[2]
]
topicWordBListL2.append(topicIWord1BList2)
# weight, with actors' name
topicIWord1BList3 = [
j[1] for j in aTopicNewUniBContentListString[i]
if wordList2[0] in j[2]
]
topicWordBListL3.append(topicIWord1BList3)
print 'top 10 weight word with actor:',
for i in top10weightActor:
print i,
print
print 'top 10 weight word without actor:',
for i in top10weightNoActor:
print i,
print
plt.figure(1)
plt.subplot(211)
topicWordBListL = topicWordBListL3
for i in topicWordBListL:
y = [topicWordBListL.index(i) for indexx in range(len(i))]
plt.scatter(i, y, marker='.', color='b')
# print 'len(i), len(y):', len(i), len(y), i, y
plt.plot([], marker='.', color='b', label='Most frequent words')
plt.xlim(0, )
plt.legend()
plt.xlabel('Barrage Time(s)')
plt.ylabel('Topic ID')
plt.subplot(212)
for i in topicWordBListL2:
y = [topicWordBListL2.index(i) for indexx in range(len(i))]
plt.scatter(i, y, marker='x', color='r')
plt.plot([], marker='x', color='r', label='Most weight words')
plt.xlim(0, )
plt.legend()
plt.xlabel('Barrage Time(s)')
plt.ylabel('Topic ID')
# plt.show()
print 'the num of users of different topics:', topicUserNumList
print 'the num of users who is not in userCodeIdList:', userIdNoneNum
print '-------------------------'
# print topicDist
topicDist2 = np.sum(topicDist, axis=1)
# the percentage of tag of a user
topicDist3 = np.transpose(np.transpose(topicDist) / np.float16(topicDist2))
print topicDist3, '\n'
for i in range(len(topicPercDist)):
for j in range(len(topicPercDist[i])):
topicPercDist[i][j] = topicPercDist[i][j] / topicPercDistAddNum[i]
# topicPercDist = topicPercDist/topicPercDistAddNum
print 'topicPercDist, topicPercDist[1][1]:', topicPercDist, topicPercDist[
1][1]
np.savetxt('topicDist.txt', topicDist)
np.savetxt('topicPercDist.txt', topicPercDist)
colorList = ['b', 'c', 'g', 'k', 'm', 'r', 'y']
plt.figure(5)
for i in range(len(topicPercDist)):
plt.plot(topicPercDist[i], colorList[i])
plt.xlabel = u'用户收藏视频主题类型'
plt.ylabel = u'用户收藏视频主题占比（各主题视频数量/所有主题视频数量）'
plt.show()
print topicNum, "topics possibility average value:", topicPosi, sum(
topicPosi) / len(topicPosi)
resIndexList = [i[0] for i in resList]
resTypeList = list(set(resIndexList))
# number of type index
resTCountList = []
# barrage sequence number of type index
indexList = []
for index in resTypeList:
resTCountList.append(resIndexList.count(index))
indexList.append([i for i, v in enumerate(resIndexList) if v == index])
# print resTCountList
# print indexList[0]
# type 0
print 'all barrage comments of type 000000000000000------------'
# for i in indexList[0]:
# print uniBContentList[i][-1]
plt.xlabel("Barrage Type")
plt.ylabel("Barrage Number")
plt.plot(resTCountList, 'r-')
# plt.show()
# return 1/aveInnDis, 1/aveExtDis
return aveInnDis, aveExtDis
