def step4_cal():
""" 24 点时merge已有的簇
"""
if not initializing and now_hour == 0:
print '[%s] ' % ts2datetime(int(time.time())), 'event ', eventid, ' %s start step4' % ts2datetime(timestamp)
subevents = event.getSubEvents()
subevent_fwords = dict()
for subevent in subevents:
subeventid = subevent["_id"]
feature = Feature(subeventid)
# 获取每个子事件最新的特征词
fwords = feature.get_newest()
subevent_fwords[subeventid] = fwords
subeventids_sort_timestamp = event.get_sorted_subeventids()
cids, mids = merge_subevents(subevent_fwords, subeventids_sort_timestamp, top_tfidf_para=10, top_percent=0.3)
for res_id, mer_id in mids:
# 将mer_id下的文本扔入res_id下的簇，remove mer_id的簇
temp_infos = event.get_subevent_infos(mer_id)
for r in temp_infos:
news = News(r["_id"], event.id)
news.update_news_subeventid(res_id)
event.remove_subevents([mer_id])
# 重新计算各簇的特征词, 并计算文本权重, 并去重
if len(mids):
step3_cal()
print '[%s] ' % ts2datetime(int(time.time())), 'event ', eventid, ' %s end step3' % ts2datetime(timestamp)
def step1_cal():
"""第一步计算，获取子事件特征词，新文本与特征词匹配分类
"""
print '[%s] ' % ts2datetime(int(time.time(
))), 'event ', eventid, ' %s start step1' % ts2datetime(timestamp)
if initializing:
# 若话题需要做初始聚类，获取话题开始时间之前的文本
results = event.getInitialInfos()
else:
# 若话题已做完初始聚类，获取话题最新一小时的文本
results = event.getInfos(timestamp - 3600, timestamp)
if now_hour == 0:
# 如果不是在做初始化，24时的时候, 一定把当天（大于或等于0时小于24时）产生的簇（非其他簇）下的文本重新做一下匹配, 同时删除这些簇
temp_subeventids = event.getTodayCreatSubeventIds()
temp_infos = event.getTodayCreatSubeventInfos()
event.remove_subevents(temp_subeventids)
results.extend(temp_infos)
print eventid, ' before classify: ', len(results)
# 获取子事件
subevents = event.getSubEvents()
labels_list = []
feature_words_list = []
for subevent in subevents:
subeventid = subevent["_id"]
feature = Feature(subeventid)
# 获取每个子事件最新的特征词
fwords = feature.get_newest()
feature_words_list.append(fwords)
labels_list.append(subeventid)
for r in results:
text = (r['title'] + r['content168']).encode('utf-8')
feature_words_inputs = []
for fwords in feature_words_list:
wcdict = dict()
for w, c in fwords.iteritems():
if isinstance(w, unicode):
w = w.encode('utf-8')
wcdict[w] = c
feature_words_inputs.append(wcdict)
# 单条文本与各子事件的特征词进行匹配，得到每条文本的簇标签
label = subevent_classifier(text, labels_list,
feature_words_inputs)
if label == "other":
label = event.getOtherSubEventID()
news = News(r["_id"], event.id)
news.update_news_subeventid(label)
print '[%s] ' % ts2datetime(int(time.time(
))), 'event ', eventid, ' %s end step1' % ts2datetime(timestamp)
def step1_cal():
"""第一步计算，获取子事件特征词，新文本与特征词匹配分类
"""
print '[%s] ' % ts2datetime(int(time.time())), 'event ', eventid, ' %s start step1' % ts2datetime(timestamp)
if initializing:
# 若话题需要做初始聚类，获取话题开始时间之前的文本
results = event.getInitialInfos()
else:
# 若话题已做完初始聚类，获取话题最新一小时的文本
results = event.getInfos(timestamp - 3600, timestamp)
if now_hour == 0:
# 如果不是在做初始化，24时的时候, 一定把当天（大于或等于0时小于24时）产生的簇（非其他簇）下的文本重新做一下匹配, 同时删除这些簇
temp_subeventids = event.getTodayCreatSubeventIds()
temp_infos = event.getTodayCreatSubeventInfos()
event.remove_subevents(temp_subeventids)
results.extend(temp_infos)
print eventid, ' before classify: ', len(results)
# 获取子事件
subevents = event.getSubEvents()
labels_list = []
feature_words_list = []
for subevent in subevents:
subeventid = subevent["_id"]
feature = Feature(subeventid)
# 获取每个子事件最新的特征词
fwords = feature.get_newest()
feature_words_list.append(fwords)
labels_list.append(subeventid)
for r in results:
text = (r['title'] + r['content168']).encode('utf-8')
feature_words_inputs = []
for fwords in feature_words_list:
wcdict = dict()
for w, c in fwords.iteritems():
if isinstance(w, unicode):
w = w.encode('utf-8')
wcdict[w] = c
feature_words_inputs.append(wcdict)
# 单条文本与各子事件的特征词进行匹配，得到每条文本的簇标签
label = subevent_classifier(text, labels_list, feature_words_inputs)
if label == "other":
label = event.getOtherSubEventID()
news = News(r["_id"], event.id)
news.update_news_subeventid(label)
print '[%s] ' % ts2datetime(int(time.time())), 'event ', eventid, ' %s end step1' % ts2datetime(timestamp)
def step3_cal():
"""计算各簇的特征词、代表文本、去重, 更新簇的大小、增幅信息
"""
print '[%s] ' % ts2datetime(int(time.time(
))), 'event ', eventid, ' %s start step3' % ts2datetime(timestamp)
inputs = []
subevents = event.getSubEvents()
for subevent in subevents:
subeventid = subevent["_id"]
inputs.extend(event.getSubeventInfos(subeventid))
for r in inputs:
r["title"] = r["title"].encode("utf-8")
r["content"] = r["content168"].encode("utf-8")
r["label"] = r["subeventid"]
# 计算各簇的存量特征词
cluster_feature = extract_feature(inputs)
for label, fwords in cluster_feature.iteritems():
feature = Feature(label)
feature.upsert_newest(fwords)
# 计算文本权重
for r in inputs:
weight = text_weight_cal(r, cluster_feature[r['label']])
news = News(r["_id"], event.id)
news.update_news_weight(weight)
# 文本去重
items_dict = {}
for r in inputs:
try:
items_dict[r["label"]].append(r)
except KeyError:
items_dict[r["label"]] = [r]
for label, items in items_dict.iteritems():
results = duplicate(items)
for r in results:
news = News(r["_id"], event.id)
news.update_news_duplicate(r["duplicate"], r["same_from"])
# 更新簇的大小、增幅信息
before_size = event.get_subevent_size(label)
event.update_subevent_size(label, len(items))
event.update_subevent_addsize(label, len(items) - before_size)
if initializing:
# 更新事件状态由initializing变为active
event.activate()
print '[%s] ' % ts2datetime(int(time.time(
))), 'event ', eventid, ' %s end step3' % ts2datetime(timestamp)
def step3_cal():
"""计算各簇的特征词、代表文本、去重, 更新簇的大小、增幅信息
"""
print '[%s] ' % ts2datetime(int(time.time())), 'event ', eventid, ' %s start step3' % ts2datetime(timestamp)
inputs = []
subevents = event.getSubEvents()
for subevent in subevents:
subeventid = subevent["_id"]
inputs.extend(event.getSubeventInfos(subeventid))
for r in inputs:
r["title"] = r["title"].encode("utf-8")
r["content"] = r["content168"].encode("utf-8")
r["label"] = r["subeventid"]
# 计算各簇的存量特征词
cluster_feature = extract_feature(inputs)
for label, fwords in cluster_feature.iteritems():
feature = Feature(label)
feature.upsert_newest(fwords)
# 计算文本权重
for r in inputs:
weight = text_weight_cal(r, cluster_feature[r['label']])
news = News(r["_id"], event.id)
news.update_news_weight(weight)
# 文本去重
items_dict = {}
for r in inputs:
try:
items_dict[r["label"]].append(r)
except KeyError:
items_dict[r["label"]] = [r]
for label, items in items_dict.iteritems():
results = duplicate(items)
for r in results:
news = News(r["_id"], event.id)
news.update_news_duplicate(r["duplicate"], r["same_from"])
# 更新簇的大小、增幅信息
before_size = event.get_subevent_size(label)
event.update_subevent_size(label, len(items))
event.update_subevent_addsize(label, len(items) - before_size)
if initializing:
# 更新事件状态由initializing变为active
event.activate()
print '[%s] ' % ts2datetime(int(time.time())), 'event ', eventid, ' %s end step3' % ts2datetime(timestamp)
def one_topic_merge(eventid_initializing):
"""合并簇
input:
eventid_initializing: (eventid, initializing)
eventid: 话题ID
initializing： 是否做初始聚类
"""
eventid, initializing = eventid_initializing
# 根据话题ID初始化话题实例
event = Event(eventid)
timestamp = event.getLastmodify() + 3600 # 当前的时间戳，int, 默认为最后修改日期+3600
now_hour = int(time.strftime('%H', time.localtime(timestamp)))
subevents = event.getSubEvents()
subevent_fwords = dict()
for subevent in subevents:
subeventid = subevent["_id"]
feature = Feature(subeventid)
# 获取每个子事件最新的特征词
fwords = feature.get_newest()
subevent_fwords[subeventid] = fwords
subeventids_sort_timestamp = event.get_sorted_subeventids()
cids, mids = merge_subevents(subevent_fwords,
subeventids_sort_timestamp,
top_tfidf_para=10,
top_percent=0.3)
for res_id, mer_id in mids:
# 将mer_id下的文本扔入res_id下的簇，remove mer_id的簇，同时重新计算各簇的特征词, 并计算文本权重, 并去重
temp_infos = event.get_subevent_infos(mer_id)
for r in temp_infos:
news = News(r["_id"], event.id)
news.update_news_subeventid(res_id)
event.remove_subevents([mer_id])
def step4_cal():
""" 24 点时merge已有的簇
"""
if not initializing and now_hour == 0:
print '[%s] ' % ts2datetime(int(time.time(
))), 'event ', eventid, ' %s start step4' % ts2datetime(timestamp)
subevents = event.getSubEvents()
subevent_fwords = dict()
for subevent in subevents:
subeventid = subevent["_id"]
feature = Feature(subeventid)
# 获取每个子事件最新的特征词
fwords = feature.get_newest()
subevent_fwords[subeventid] = fwords
subeventids_sort_timestamp = event.get_sorted_subeventids()
cids, mids = merge_subevents(subevent_fwords,
subeventids_sort_timestamp,
top_tfidf_para=10,
top_percent=0.3)
for res_id, mer_id in mids:
# 将mer_id下的文本扔入res_id下的簇，remove mer_id的簇
temp_infos = event.get_subevent_infos(mer_id)
for r in temp_infos:
news = News(r["_id"], event.id)
news.update_news_subeventid(res_id)
event.remove_subevents([mer_id])
# 重新计算各簇的特征词, 并计算文本权重, 并去重
if len(mids):
step3_cal()
print '[%s] ' % ts2datetime(int(time.time(
))), 'event ', eventid, ' %s end step3' % ts2datetime(timestamp)
def test_subevent_classifier():
labels_list = []
feature_words_inputs = []
subevents = event.getSubEvents()
for subevent in subevents:
subeventid = subevent["_id"]
if subeventid != "575612b6-a26f-4df9-a2de-01c85cae56a2":
labels_list.append(subeventid)
feature = Feature(subeventid)
feature_words = feature.get_newest()
new_feature_words = dict()
for k, v in feature_words.iteritems():
new_feature_words[k.encode('utf-8')] = v
feature_words_inputs.append(new_feature_words)
news_id = "http://news.xinhuanet.com/comments/2014-11/03/c_1113084515.htm"
news = News(news_id, event.id)
ns = news.get_news_info()
text = ns['title'].encode('utf-8') + ns['content168'].encode('utf-8')
label = subevent_classifier(text, labels_list, feature_words_inputs)
print label
def one_topic_merge(eventid_initializing):
"""合并簇
input:
eventid_initializing: (eventid, initializing)
eventid: 话题ID
initializing： 是否做初始聚类
"""
eventid, initializing = eventid_initializing
# 根据话题ID初始化话题实例
event = Event(eventid)
timestamp = event.getLastmodify() + 3600 # 当前的时间戳，int, 默认为最后修改日期+3600
now_hour = int(time.strftime('%H', time.localtime(timestamp)))
subevents = event.getSubEvents()
subevent_fwords = dict()
for subevent in subevents:
subeventid = subevent["_id"]
feature = Feature(subeventid)
# 获取每个子事件最新的特征词
fwords = feature.get_newest()
subevent_fwords[subeventid] = fwords
subeventids_sort_timestamp = event.get_sorted_subeventids()
cids, mids = merge_subevents(subevent_fwords, subeventids_sort_timestamp, top_tfidf_para=10, top_percent=0.3)
for res_id, mer_id in mids:
# 将mer_id下的文本扔入res_id下的簇，remove mer_id的簇，同时重新计算各簇的特征词, 并计算文本权重, 并去重
temp_infos = event.get_subevent_infos(mer_id)
for r in temp_infos:
news = News(r["_id"], event.id)
news.update_news_subeventid(res_id)
event.remove_subevents([mer_id])
