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 step2_cal():
"""第二步计算，判断其他类是否需要分裂，若需要，则对其他类进行文本聚类，并做聚类评价
"""
# 聚类评价时选取TOPK_FREQ_WORD的高频词
TOPK_FREQ_WORD = 50
# 聚类评价时最小簇的大小
LEAST_SIZE = 8
# 判断其他类是否需要分裂
ifsplit = event.check_ifsplit(initializing)
print '[%s] ' % ts2datetime(int(time.time())), 'event ', eventid, ' split ', ifsplit, ' %s start step2' % ts2datetime(timestamp)
if ifsplit:
inputs, kmeans_cluster_num, reserve_num = event.getOtherSubEventInfos(initializing)
print eventid, ' after classify before split: ', len(inputs), kmeans_cluster_num, reserve_num
if len(inputs) > 2:
items = []
for r in inputs:
r["title"] = r["title"].encode("utf-8")
r["content"] = r["content168"].encode("utf-8")
items.append(r)
# kmeans聚类
kmeans_results = kmeans(items, k=kmeans_cluster_num)
# 聚类评价
if initializing or now_hour == 0:
min_tfidf = event.get_min_tfidf()
final_cluster_results, tfidf_dict = cluster_evaluation(kmeans_results, top_num=reserve_num, topk_freq=TOPK_FREQ_WORD, least_size=LEAST_SIZE, min_tfidf=min_tfidf)
else:
# 每小时聚类时，不用和已有簇的最小tfidf作比
final_cluster_results, tfidf_dict = cluster_evaluation(kmeans_results, top_num=reserve_num, topk_freq=TOPK_FREQ_WORD, least_size=LEAST_SIZE)
# 更新新闻簇标签，更新子事件表
for label, items in final_cluster_results.iteritems():
if label == "other":
label = event.getOtherSubEventID()
event.save_subevent(label, timestamp)
if label != event.getOtherSubEventID():
# 更新每类的tfidf
event.update_subevent_tfidf(label, tfidf_dict[label])
for r in items:
news = News(r["_id"], event.id)
news.update_news_subeventid(label)
else:
print 'inputs less than 2, kmeans aborted'
print '[%s] ' % ts2datetime(int(time.time())), 'event ', eventid, ' %s end step2' % ts2datetime(timestamp)
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 step2_cal():
"""第二步计算，判断其他类是否需要分裂，若需要，则对其他类进行文本聚类，并做聚类评价
"""
# 聚类评价时选取TOPK_FREQ_WORD的高频词
TOPK_FREQ_WORD = 50
# 聚类评价时最小簇的大小
LEAST_SIZE = 8
# 判断其他类是否需要分裂
ifsplit = event.check_ifsplit(initializing)
print '[%s] ' % ts2datetime(
int(time.time())
), 'event ', eventid, ' split ', ifsplit, ' %s start step2' % ts2datetime(
timestamp)
if ifsplit:
inputs, kmeans_cluster_num, reserve_num = event.getOtherSubEventInfos(
initializing)
print eventid, ' after classify before split: ', len(
inputs), kmeans_cluster_num, reserve_num
if len(inputs) > 2:
items = []
for r in inputs:
r["title"] = r["title"].encode("utf-8")
r["content"] = r["content168"].encode("utf-8")
items.append(r)
# kmeans聚类
kmeans_results = kmeans(items, k=kmeans_cluster_num)
# 聚类评价
if initializing or now_hour == 0:
min_tfidf = event.get_min_tfidf()
final_cluster_results, tfidf_dict = cluster_evaluation(
kmeans_results,
top_num=reserve_num,
topk_freq=TOPK_FREQ_WORD,
least_size=LEAST_SIZE,
min_tfidf=min_tfidf)
else:
# 每小时聚类时，不用和已有簇的最小tfidf作比
final_cluster_results, tfidf_dict = cluster_evaluation(
kmeans_results,
top_num=reserve_num,
topk_freq=TOPK_FREQ_WORD,
least_size=LEAST_SIZE)
# 更新新闻簇标签，更新子事件表
for label, items in final_cluster_results.iteritems():
if label == "other":
label = event.getOtherSubEventID()
event.save_subevent(label, timestamp)
if label != event.getOtherSubEventID():
# 更新每类的tfidf
event.update_subevent_tfidf(label, tfidf_dict[label])
for r in items:
news = News(r["_id"], event.id)
news.update_news_subeventid(label)
else:
print 'inputs less than 2, kmeans aborted'
print '[%s] ' % ts2datetime(int(time.time(
))), 'event ', eventid, ' %s end step2' % ts2datetime(timestamp)