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)
