def handle_error(eventid): """ """ event = Event(eventid) last_modify = event.getLastmodify() event.setLastmodify(last_modify - 3600) event.setModifysuccess(True)
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 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 one_topic_calculation(eventid_initializing): """多步计算 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))) 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 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 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) # 首先检测该事件最近一次修改是否成功 success = event.checkLastModify() if success: """ step1_cal() step2_cal() step3_cal() step4_cal() """ try: # 进行多步计算 step1_cal() step2_cal() step3_cal() step4_cal() event.setLastmodify(timestamp) # 更新事件的last_modify event.setModifysuccess(True) # 更新事件的modify_success为True except Exception, e: # 如果做计算时出错,更新last_modify, 并将modify_success设置为False print '[Error]: ', e event.setLastmodify(timestamp) event.setModifysuccess(False)
def one_topic_calculation(eventid_initializing): """多步计算 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))) 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 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 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) # 首先检测该事件最近一次修改是否成功 success = event.checkLastModify() if success: """ step1_cal() step2_cal() step3_cal() step4_cal() """ try: # 进行多步计算 step1_cal() step2_cal() step3_cal() step4_cal() event.setLastmodify(timestamp) # 更新事件的last_modify event.setModifysuccess(True) # 更新事件的modify_success为True except Exception, e: # 如果做计算时出错,更新last_modify, 并将modify_success设置为False print '[Error]: ', e event.setLastmodify(timestamp) event.setModifysuccess(False)