def statistics_influence_people(uid, date, style): # output: different retweeted and comment, uids' domain distribution, topic distribution, registeration geo distribution results = {} # retwweted weibo people and comment weibo people date1 = str(date).replace('-', '') index_name = pre_index + date1 index_flow_text = pre_text_index + date try: bci_result = es_cluster.get(index=index_name, doc_type=influence_doctype, id=uid)["_source"] except: bci_result = [] return results origin_mid = [] # origin weibo mid retweeted_mid = [] # retweeted weibo mid query_body = { "query":{ "filtered":{ "filter":{ "bool":{ "must":[ ] } } } }, "size":1000 } body_1 = copy.deepcopy(query_body) body_2 = copy.deepcopy(query_body) body_1["query"]["filtered"]["filter"]["bool"]["must"].extend([{"term":{"message_type": 1}}, {"term":{"uid": uid}}]) result_1 = es.search(index=index_flow_text, doc_type=flow_text_index_type, body=body_1)["hits"]["hits"] if result_1: for item in result_1: origin_mid.append(item['_id']) body_1["query"]["filtered"]["filter"]["bool"]["must"].extend([{"term":{"message_type": 3}}, {"term":{"uid": uid}}]) result_2 = es.search(index=index_flow_text, doc_type=flow_text_index_type, body=body_2)["hits"]["hits"] if result_2: for item in result_2: if item['_source'].get('root_mid', ''): retweeted_mid.append(item['_source']['root_mid']) if int(style) == 0: # retweeted retweeted_results = influenced_user_detail(uid, date, origin_mid, retweeted_mid, 3) results = retweeted_results else: comment_results = influenced_user_detail(uid, date, origin_mid, retweeted_mid, 2) results = comment_results return results
def query_hot_mid(ts, keywords_list, text_type,size=100): query_body = { "query":{ "filtered":{ "filter":{ "bool":{ "must":[ {"range":{ "timestamp":{ "gte":ts - time_interval, "lt": ts } }}, {"terms": {"keywords_string": keywords_list}}, {"term": {"message_type": "0"}} ] } } } }, "aggs":{ "all_interests":{ "terms":{"field": "root_mid", "size": size} } } } datetime = ts2datetime(ts) datetime_1 = ts2datetime(ts-time_interval) index_name = flow_text_index_name_pre + datetime exist_es = es_text.indices.exists(index_name) index_name_1 = flow_text_index_name_pre + datetime_1 exist_bool_1 = es_text.indices.exists(index_name_1) print datetime, datetime_1 if datetime == datetime_1 and exist_es: search_results = es_text.search(index=index_name, doc_type=flow_text_index_type, body=query_body)["aggregations"]["all_interests"]["buckets"] elif datetime != datetime_1 and exist_bool_1: search_results = es_text.search(index=index_name_1, doc_type=flow_text_index_type, body=query_body)["aggregations"]["all_interests"]["buckets"] else: search_results = [] hot_mid_list = [] if search_results: for item in search_results: print item temp = [] temp.append(item['key']) temp.append(item['doc_count']) hot_mid_list.append(temp) #print hot_mid_list return hot_mid_list
def get_user_ip(uid): flow_text_index_list = [] now_timestamp = datetime2ts(ts2datetime(time.time())) if RUN_TYPE == 0: now_timestamp = datetime2ts(RUN_TEST_TIME) for i in range(7, 0, -1): iter_date = ts2datetime(now_timestamp - DAY * i) flow_text_index_list.append(flow_text_index_name_pre + iter_date) weibo_all = es_flow_text.search(index=flow_text_index_list, doc_type=flow_text_index_type, body={ 'query': { 'filtered': { 'filter': { 'term': { 'uid': uid } } } }, 'size': 10, })['hits']['hits'] ip = weibo_all[0]["_source"]["ip"] return ip
def get_repost_weibo(mid, weibo_timestamp): repost_result = [] index_date = ts2datetime(weibo_timestamp) index_name = flow_text_index_name_pre + index_date query_body = { 'query':{ 'bool':{ 'must':[ {'term':{'root_mid': mid}}, {'range':{'timestamp':{'gte': weibo_timestamp}}}, {'term':{'message_type': 2}} ] } } } try: flow_text_result = es_flow_text.search(index=index_name, doc_type=flow_text_index_type,\ body=query_body)['hits']['hits'] except: flow_text_result = [] repost_uid_list = [item['_source']['uid'] for item in flow_text_result] repost_user_info_dict = get_user_profile_weibo(repost_uid_list) statuses = [] for item in flow_text_result: item_source = item['_source'] item_source['user'] = repost_user_info_dict[item['uid']] statuses.append(item_source) return statuses
def get_sen_ratio(topic, start_ts, end_ts): query_body = { 'query': { 'bool': { 'must': [{ 'wildcard': { 'text': '*' + topic + '*' } }, { 'range': { 'timestamp': { 'lte': end_ts, 'gte': start_ts } } }] } }, 'aggs': { 'all_interests': { 'terms': { 'field': 'sentiment', } } } } if RUN_TYPE == 0: date = '2013-09-07' else: date = ts2datetime(time.time()) result = es_flow_text.search(index = flow_text_index_name_pre+date,doc_type=flow_text_index_type,body=query_body)\ ['aggregations']['all_interests']['buckets'] return result
def get_sen_ratio(topic,start_ts,end_ts): query_body = { 'query':{ 'bool':{ 'must':[ {'wildcard':{'text':'*'+topic+'*'}}, {'range':{'timestamp':{'lte':end_ts,'gte':start_ts}}} ] } }, 'aggs':{ 'all_interests':{ 'terms':{ 'field': 'sentiment', } } } } if RUN_TYPE == 0 : date = '2013-09-07' else: date = ts2datetime(time.time()) result = es_flow_text.search(index = flow_text_index_name_pre+date,doc_type=flow_text_index_type,body=query_body)\ ['aggregations']['all_interests']['buckets'] return result
def get_influence_content(uid, timestamp_from, timestamp_to): weibo_list = [] #split timestamp range to new_range_dict_list from_date_ts = datetime2ts(ts2datetime(timestamp_from)) to_date_ts = datetime2ts(ts2datetime(timestamp_to)) new_range_dict_list = [] if from_date_ts != to_date_ts: iter_date_ts = from_date_ts while iter_date_ts < to_date_ts: iter_next_date_ts = iter_date_ts + DAY new_range_dict_list.append({ 'range': { 'timestamp': { 'gte': iter_date_ts, 'lt': iter_next_date_ts } } }) iter_date_ts = iter_next_date_ts if new_range_dict_list[0]['range']['timestamp']['gte'] < timestamp_from: new_range_dict_list[0]['range']['timestamp'][ 'gte'] = timestamp_from if new_range_dict_list[-1]['range']['timestamp']['lt'] > timestamp_to: new_range_dict_list[-1]['range']['timestamp']['lt'] = timestamp_to else: new_range_dict_list = [{ 'range': { 'timestamp': { 'gte': timestamp_from, 'lt': timestamp_to } } }] #iter date to search flow_text iter_result = [] for range_item in new_range_dict_list: range_from_ts = range_item['range']['timestamp']['gte'] range_from_date = ts2datetime(range_from_ts) flow_text_index_name = flow_text_index_name_pre + range_from_date query = [] query.append({'term': {'uid': uid}}) query.append(range_item) try: flow_text_exist = es_flow_text.search(index=flow_text_index_name, doc_type=flow_text_index_type,\ body={'query':{'bool':{'must': query}}, 'sort':[{'timestamp':'asc'}]})['hits']['hits'] except: flow_text_exist = [] iter_result.extend(flow_text_exist) # get weibo list for item in flow_text_exist: source = item['_source'] weibo = {} weibo['timestamp'] = ts2date(source['timestamp']) weibo['ip'] = source['ip'] weibo['text'] = source['text'] weibo['geo'] = '\t'.join(source['geo'].split('&')) weibo_list.append(weibo) return weibo_list
def search_weibo(root_uid,uid,mtype): query_body = { #'query':{ 'filter':{ 'bool':{ 'must':[{'term':{'uid':uid}}, {'term':{'message_type':mtype}}], 'should':[{'term':{'root_uid':root_uid}}, {'term':{'directed_uid':root_uid}}], } } #} } index_list = [] for i in range(7, 0, -1): if RUN_TYPE == 1: iter_date = ts2datetime(datetime2ts(now_date) - i * DAY) else: iter_date = ts2datetime(datetime2ts(RUN_TEST_TIME) - i * DAY) index_list.append(flow_text_index_name_pre + iter_date) results = es_flow_text.search(index=index_list,doc_type=flow_text_index_type,body=query_body)['hits']['hits'] weibo = {} f_result = [] if len(results) > 0: for result in results: #print type(result),result weibo['last_text'] = [result['_source']['text'],result['_source']['text'],result['_source']['timestamp']] mid = result['_source']['root_mid'] # print mid len_pre = len(flow_text_index_name_pre) index = result['_index'][len_pre:] root_index = [] for j in range(0,7): #一周的,一个月的话就0,30 iter_date = ts2datetime(datetime2ts(index) - j * DAY) root_index.append(flow_text_index_name_pre + iter_date) results0 = es_flow_text.search(index=root_index,doc_type=flow_text_index_type,body={'query':{'term':{'mid':mid}}})['hits']['hits'] if len(results0)>0: for result0 in results0: weibo['ori_text'] = [result0['_source']['text'],result0['_source']['timestamp']] f_result.append(weibo) weibo={} return f_result
def get_social_inter_content(uid1, uid2, type_mark): weibo_list = [] #get two type relation about uid1 and uid2 #search weibo list now_ts = int(time.time()) #run_type if RUN_TYPE == 1: now_date_ts = datetime2ts(ts2datetime(now_ts)) else: now_date_ts = datetime2ts(RUN_TEST_TIME) #uid2uname uid2uname = {} try: portrait_result = es_user_portrait.mget(index=portrait_index_name, doc_type=portrait_index_type ,\ body={'ids': [uid1, uid2]}, _source=False, fields=['uid', 'uname'])['docs'] except: portrait_result = [] for item in portrait_result: uid = item['_id'] if item['found'] == True: uname = item['fields']['uname'][0] uid2uname[uid] = uname else: uid2uname[uid] = 'unknown' #iter date to search weibo list for i in range(7, 0, -1): iter_date_ts = now_date_ts - i*DAY iter_date = ts2datetime(iter_date_ts) flow_text_index_name = flow_text_index_name_pre + str(iter_date) query = [] query.append({'bool':{'must':[{'term':{'uid':uid1}}, {'term':{'directed_uid': int(uid2)}}]}}) if type_mark=='out': query.append({'bool':{'must':[{'term':{'uid':uid2}}, {'term':{'directed_uid': int(uid1)}}]}}) try: flow_text_result = es_flow_text.search(index=flow_text_index_name, doc_type=flow_text_index_type,\ body={'query': {'bool':{'should': query}}, 'sort':[{'timestamp':{'order': 'asc'}}], 'size':MAX_VALUE})['hits']['hits'] except: flow_text_result = [] for flow_text in flow_text_result: source = flow_text['_source'] weibo = {} weibo['timestamp'] = source['timestamp'] weibo['ip'] = source['ip'] weibo['geo'] = source['geo'] weibo['text'] = '\t'.join(source['text'].split('&')) weibo['uid'] = source['uid'] weibo['uname'] = uid2uname[weibo['uid']] weibo['directed_uid'] = str(source['directed_uid']) weibo['directed_uname'] = uid2uname[str(source['directed_uid'])] weibo_list.append(weibo) return weibo_list
def get_activity_weibo(task_name, start_ts, submit_user): results = [] task_id = submit_user + '-' + task_name #step1: get task_name uid try: group_result = es_group_result.get(index=group_index_name, doc_type=group_index_type ,\ id=task_id, _source=False, fields=['uid_list']) except: group_result = {} if group_result == {}: return 'task name invalid' try: uid_list = group_result['fields']['uid_list'] except: uid_list = [] if uid_list == []: return 'task uid list null' #step2: get uid2uname uid2uname = {} try: user_portrait_result = es_user_portrait.mget(index=portrait_index_name, doc_type=portrait_index_type, \ body = {'ids':uid_list}, _source=False, fields=['uname'])['docs'] except: user_portrait_result = [] for item in user_portrait_result: uid = item['_id'] if item['found']==True: uname = item['fields']['uname'][0] uid2uname[uid] = uname #step3: search time_segment weibo time_segment = FOUR_HOUR end_ts = start_ts + time_segment time_date = ts2datetime(start_ts) flow_text_index_name = flow_text_index_name_pre + time_date query = [] query.append({'terms':{'uid': uid_list}}) query.append({'range':{'timestamp':{'gte':start_ts, 'lt':end_ts}}}) try: flow_text_es_result = es_flow_text.search(index=flow_text_index_name, doc_type=flow_text_index_type, \ body={'query':{'bool':{'must':query}}, 'sort':'timestamp', 'size':MAX_VALUE})['hits']['hits'] except: flow_text_es_result = [] for item in flow_text_es_result: weibo = {} source = item['_source'] weibo['timestamp'] = ts2date(source['timestamp']) weibo['ip'] = source['ip'] weibo['text'] = source['text'] weibo['geo'] = '\t'.join(source['geo']) results.append(weibo) return results
def get_activity_weibo(task_name, start_ts, submit_user): results = [] task_id = submit_user + '-' + task_name #step1: get task_name uid try: group_result = es_group_result.get(index=group_index_name, doc_type=group_index_type ,\ id=task_id, _source=False, fields=['uid_list']) except: group_result = {} if group_result == {}: return 'task name invalid' try: uid_list = group_result['fields']['uid_list'] except: uid_list = [] if uid_list == []: return 'task uid list null' #step2: get uid2uname uid2uname = {} try: user_portrait_result = es_user_portrait.mget(index=portrait_index_name, doc_type=portrait_index_type, \ body = {'ids':uid_list}, _source=False, fields=['uname'])['docs'] except: user_portrait_result = [] for item in user_portrait_result: uid = item['_id'] if item['found'] == True: uname = item['fields']['uname'][0] uid2uname[uid] = uname #step3: search time_segment weibo time_segment = FOUR_HOUR end_ts = start_ts + time_segment time_date = ts2datetime(start_ts) flow_text_index_name = flow_text_index_name_pre + time_date query = [] query.append({'terms': {'uid': uid_list}}) query.append({'range': {'timestamp': {'gte': start_ts, 'lt': end_ts}}}) try: flow_text_es_result = es_flow_text.search(index=flow_text_index_name, doc_type=flow_text_index_type, \ body={'query':{'bool':{'must':query}}, 'sort':'timestamp', 'size':MAX_VALUE})['hits']['hits'] except: flow_text_es_result = [] for item in flow_text_es_result: weibo = {} source = item['_source'] weibo['timestamp'] = ts2date(source['timestamp']) weibo['ip'] = source['ip'] weibo['text'] = source['text'] weibo['geo'] = '\t'.join(source['geo']) results.append(weibo) return results
def get_user_ip(uid): flow_text_index_list = [] now_timestamp = datetime2ts(ts2datetime(time.time())) if RUN_TYPE == 0: now_timestamp = datetime2ts(RUN_TEST_TIME) for i in range(7, 0, -1): iter_date = ts2datetime(now_timestamp - DAY * i) flow_text_index_list.append(flow_text_index_name_pre + iter_date) weibo_all = es_flow_text.search(index=flow_text_index_list, doc_type=flow_text_index_type, body={'query': {'filtered': {'filter': {'term': {'uid': uid}}}}, 'size': 10, })['hits']['hits'] ip = weibo_all[0]["_source"]["ip"] return ip
def get_user_geo(uid, dropped_geos=u"中国&美国"): """ :param uid: 用户的id :param dropped_geos: &分割的地点,因为geo中都包含中国 :return: geo 位置的set """ dropped_geos = set(dropped_geos.split("&")) # 获取用户的偏好 try: user_portrait_result = es_user_portrait. \ get_source(index=portrait_index_name, doc_type=profile_index_type, id=uid) except NotFoundError: user_portrait_result = None # portrait表中存在geo信息 if user_portrait_result and len(user_portrait_result["activity_geo"]) > 0: geos = user_portrait_result["activity_geo"] - dropped_geos # 不存在geo信息,获取之前发去的微博提取 else: flow_text_index_list = [] now_timestamp = datetime2ts(ts2datetime(time.time())) if RUN_TYPE == 0: now_timestamp = datetime2ts(RUN_TEST_TIME) for i in range(7, 0, -1): iter_date = ts2datetime(now_timestamp - DAY * i) flow_text_index_list.append(flow_text_index_name_pre + iter_date) weibo_all = es_flow_text.search(index=flow_text_index_list, doc_type=flow_text_index_type, body={ 'query': { 'filtered': { 'filter': { 'term': { 'uid': uid } } } }, 'size': 2000, })['hits']['hits'] geos = set() for temp in weibo_all: geos |= set(temp["_source"]["geo"].split("&")) return geos
def cctv_video_rec(uid, k=10): flow_text_index_list = [] now_timestamp = datetime2ts(ts2datetime(time.time())) if RUN_TYPE == 0: now_timestamp = datetime2ts(RUN_TEST_TIME) for i in range(7, 0, -1): iter_date = ts2datetime(now_timestamp - DAY * i) flow_text_index_list.append(flow_text_index_name_pre + iter_date) weibo_all = es_flow_text.search(index=flow_text_index_list, doc_type=flow_text_index_type, body={ 'query': { 'filtered': { 'filter': { 'term': { 'uid': uid } } } }, 'size': 100, })['hits']['hits'] user_words = set() for weibo in weibo_all: weibo_text = weibo["_source"]["text"] user_words |= set(jieba.cut(weibo_text)) rio_dict = load_topic_video_dict(RIO_VIDEO_INFO_FILE) tiger_videos = load_videos(TIGER_VIDEO_INFO_FILE) ret_dict = dict() ret_dict["tiger"] = random.sample(tiger_videos, k) user_pref_topic = set(rio_dict.keys()) & user_words # 若找不到,随机分配topic if len(user_pref_topic) == 0: user_pref_topic = set(random.sample(rio_dict.keys(), k)) ret_dict["rio"] = list() for topic in user_pref_topic: ret_dict["rio"].extend(rio_dict[topic]) if len(ret_dict["rio"]) >= k: ret_dict["rio"] = ret_dict["rio"][:k] break return ret_dict
def get_influence_content(uid, timestamp_from, timestamp_to): weibo_list = [] #split timestamp range to new_range_dict_list from_date_ts = datetime2ts(ts2datetime(timestamp_from)) to_date_ts = datetime2ts(ts2datetime(timestamp_to)) new_range_dict_list = [] if from_date_ts != to_date_ts: iter_date_ts = from_date_ts while iter_date_ts < to_date_ts: iter_next_date_ts = iter_date_ts + DAY new_range_dict_list.append({'range':{'timestamp':{'gte':iter_date_ts, 'lt':iter_next_date_ts}}}) iter_date_ts = iter_next_date_ts if new_range_dict_list[0]['range']['timestamp']['gte'] < timestamp_from: new_range_dict_list[0]['range']['timestamp']['gte'] = timestamp_from if new_range_dict_list[-1]['range']['timestamp']['lt'] > timestamp_to: new_range_dict_list[-1]['range']['timestamp']['lt'] = timestamp_to else: new_range_dict_list = [{'range':{'timestamp':{'gte':timestamp_from, 'lt':timestamp_to}}}] #iter date to search flow_text iter_result = [] for range_item in new_range_dict_list: range_from_ts = range_item['range']['timestamp']['gte'] range_from_date = ts2datetime(range_from_ts) flow_text_index_name = flow_text_index_name_pre + range_from_date query = [] query.append({'term':{'uid':uid}}) query.append(range_item) try: flow_text_exist = es_flow_text.search(index=flow_text_index_name, doc_type=flow_text_index_type,\ body={'query':{'bool':{'must': query}}, 'sort':[{'timestamp':'asc'}]})['hits']['hits'] except: flow_text_exist = [] iter_result.extend(flow_text_exist) # get weibo list for item in flow_text_exist: source = item['_source'] weibo = {} weibo['timestamp'] = ts2date(source['timestamp']) weibo['ip'] = source['ip'] weibo['text'] = source['text'] weibo['geo'] = '\t'.join(source['geo'].split('&')) weibo_list.append(weibo) return weibo_list
def get_user_geo(uid, dropped_geos=u"中国&美国"): """ :param uid: 用户的id :param dropped_geos: &分割的地点,因为geo中都包含中国 :return: geo 位置的set """ dropped_geos = set(dropped_geos.split("&")) # 获取用户的偏好 try: user_portrait_result = es_user_portrait. \ get_source(index=portrait_index_name, doc_type=profile_index_type, id=uid) except NotFoundError: user_portrait_result = None # portrait表中存在geo信息 if user_portrait_result and len(user_portrait_result["activity_geo"]) > 0: geos = user_portrait_result["activity_geo"] - dropped_geos # 不存在geo信息,获取之前发去的微博提取 else: flow_text_index_list = [] now_timestamp = datetime2ts(ts2datetime(time.time())) if RUN_TYPE == 0: now_timestamp = datetime2ts(RUN_TEST_TIME) for i in range(7, 0, -1): iter_date = ts2datetime(now_timestamp - DAY * i) flow_text_index_list.append(flow_text_index_name_pre + iter_date) weibo_all = es_flow_text.search(index=flow_text_index_list, doc_type=flow_text_index_type, body={'query': {'filtered': {'filter': {'term': {'uid': uid}}}}, 'size': 2000, })['hits']['hits'] geos = set() for temp in weibo_all: geos |= set(temp["_source"]["geo"].split("&")) return geos
def cctv_video_rec(uid, k=10): flow_text_index_list = [] now_timestamp = datetime2ts(ts2datetime(time.time())) if RUN_TYPE == 0: now_timestamp = datetime2ts(RUN_TEST_TIME) for i in range(7, 0, -1): iter_date = ts2datetime(now_timestamp - DAY * i) flow_text_index_list.append(flow_text_index_name_pre + iter_date) weibo_all = es_flow_text.search(index=flow_text_index_list, doc_type=flow_text_index_type, body={'query': {'filtered': {'filter': {'term': {'uid': uid}}}}, 'size': 100, })['hits']['hits'] user_words = set() for weibo in weibo_all: weibo_text = weibo["_source"]["text"] user_words |= set(jieba.cut(weibo_text)) rio_dict = load_topic_video_dict(RIO_VIDEO_INFO_FILE) tiger_videos = load_videos(TIGER_VIDEO_INFO_FILE) ret_dict = dict() ret_dict["tiger"] = random.sample(tiger_videos, k) user_pref_topic = set(rio_dict.keys()) & user_words # 若找不到,随机分配topic if len(user_pref_topic) == 0: user_pref_topic = set(random.sample(rio_dict.keys(), k)) ret_dict["rio"] = list() for topic in user_pref_topic: ret_dict["rio"].extend(rio_dict[topic]) if len(ret_dict["rio"]) >= k: ret_dict["rio"] = ret_dict["rio"][:k] break return ret_dict
def get_social_inter_content(uid1, uid2, type_mark): weibo_list = [] #get two type relation about uid1 and uid2 #search weibo list now_ts = int(time.time()) #run_type if RUN_TYPE == 1: now_date_ts = datetime2ts(ts2datetime(now_ts)) else: now_date_ts = datetime2ts(RUN_TEST_TIME) #uid2uname uid2uname = {} try: portrait_result = es_user_portrait.mget(index=portrait_index_name, doc_type=portrait_index_type ,\ body={'ids': [uid1, uid2]}, _source=False, fields=['uid', 'uname'])['docs'] except: portrait_result = [] for item in portrait_result: uid = item['_id'] if item['found'] == True: uname = item['fields']['uname'][0] uid2uname[uid] = uname else: uid2uname[uid] = 'unknown' #iter date to search weibo list for i in range(7, 0, -1): iter_date_ts = now_date_ts - i * DAY iter_date = ts2datetime(iter_date_ts) flow_text_index_name = flow_text_index_name_pre + str(iter_date) query = [] query.append({ 'bool': { 'must': [{ 'term': { 'uid': uid1 } }, { 'term': { 'directed_uid': int(uid2) } }] } }) if type_mark == 'out': query.append({ 'bool': { 'must': [{ 'term': { 'uid': uid2 } }, { 'term': { 'directed_uid': int(uid1) } }] } }) try: flow_text_result = es_flow_text.search(index=flow_text_index_name, doc_type=flow_text_index_type,\ body={'query': {'bool':{'should': query}}, 'sort':[{'timestamp':{'order': 'asc'}}], 'size':MAX_VALUE})['hits']['hits'] except: flow_text_result = [] for flow_text in flow_text_result: source = flow_text['_source'] weibo = {} weibo['timestamp'] = source['timestamp'] weibo['ip'] = source['ip'] weibo['geo'] = source['geo'] weibo['text'] = '\t'.join(source['text'].split('&')) weibo['uid'] = source['uid'] weibo['uname'] = uid2uname[weibo['uid']] weibo['directed_uid'] = str(source['directed_uid']) weibo['directed_uname'] = uid2uname[str(source['directed_uid'])] weibo_list.append(weibo) return weibo_list
def influenced_detail(uid, date, style): date1 = str(date).replace('-', '') index_name = pre_index + date1 index_text = "flow_text_" + date style = int(style) query_body_origin = { "query":{ "filtered":{ "filter":{ "bool":{ "must":[ {"term":{"message_type": 1}}, {"term":{"uid": uid}} ] } } } }, "size": 10000 } result_1 = es.search(index=index_text, doc_type="text", body=query_body_origin)['hits']['hits'] origin_set = [] if result_1: for item in result_1: origin_set.append([item['_id'], item['_source'].get("retweeted", 0), item['_source'].get("comment", 0)]) query_body_retweeted = { "query":{ "filtered":{ "filter":{ "bool":{ "must":[ {"term":{"message_type": 3}}, {"term":{"uid": uid}} ] } } } }, "size": 10000 } result_2 = es.search(index=index_text, doc_type="text", body=query_body_retweeted)['hits']['hits'] retweeted_set = [] if result_2: for item in result_2: retweeted_set.append([item['_id'], item['_source'].get("retweeted", 0), item['_source'].get("comment", 0)]) if style == 0: sorted_list = sorted(origin_set, key=lambda x:x[1], reverse=True) detail_text = get_text(sorted_list[:20], date, style) elif style == 1: sorted_list = sorted(origin_set, key=lambda x:x[2], reverse=True) detail_text = get_text(sorted_list[:20], date, style) elif style == 2: sorted_list = sorted(retweeted_set, key=lambda x:x[1], reverse=True) detail_text = get_text(sorted_list[:20], date, style) else: sorted_list = sorted(retweeted_set, key=lambda x:x[2], reverse=True) detail_text = get_text(sorted_list[:20], date, style) return detail_text
def influenced_people(uid, mid, influence_style, date, default_number=20): # uid # which weibo----mid, retweeted weibo ---seek for root_mid # influence_style: retweeted(0) or comment(1) date1 = ts2datetime(datetime2ts(date)).replace('-', '') index_name = pre_index + date1 index_flow_text = pre_text_index + date text_result = es.get(index=index_flow_text, doc_type=flow_text_index_type, id=mid)["_source"] temp_mid = text_result.get("root_mid",'') #判断微博是否是原创微博 if temp_mid: mid_type = 1 # 非原创微博 else: mid_type = 0 # 原创微博 query_body = { "query":{ "filtered":{ "filter":{ "bool":{ "must":[ ] } } } }, "size": 30000 } if mid_type == 0: if int(influence_style) == 0: # origin weibo, all retweeted people query_body["query"]["filtered"]["filter"]["bool"]["must"].extend([{"term": {"root_uid": uid}}, {"term": {"message_type": 3}}, {"term": {"root_mid": mid}}]) else: # commented people query_body["query"]["filtered"]["filter"]["bool"]["must"].extend([{"term": {"directed_uid": uid}}, {"term": {"message_type": 2}}, {"term": {"root_mid": mid}}]) else: if int(influence_style) == 0: # origin weibo, all retweeted people query_body["query"]["filtered"]["filter"]["bool"]["must"].extend([{"term": {"directed_uid": uid}}, {"term": {"message_type": 3}}, {"term": {"root_mid": temp_mid}}]) else: # commented people query_body["query"]["filtered"]["filter"]["bool"]["must"].extend([{"term": {"directed_uid": uid}}, {"term": {"message_type": 2}}, {"term": {"root_mid": temp_mid}}]) search_results = es.search(index=index_flow_text, doc_type=flow_text_index_type, body=query_body, fields=["uid"], timeout=30)["hits"]["hits"] results = [] # uid_list if search_results: for item in search_results: if int(item["fields"]["uid"][0]) == int(uid): pass else: results.append(item["fields"]["uid"][0]) results = list(set(results)) else: results = [] bci_index = "bci_" + date.replace('-','') if results: portrait_results = es_user_portrait.mget(index=user_portrait, doc_type=portrait_index_type, body={"ids": results}, fields=["domain", "topic_string", "activity_geo_dict","importance", "influence"])["docs"] bci_results = es_user_portrait.mget(index=bci_index, doc_type='bci', body={"ids":results}, fields=['user_index'])['docs'] else: portrait_results = {} bci_results = {} in_portrait = [] out_portrait = [] in_portrait_info = [] retweeted_domain = {} retweeted_topic = {} retweeted_geo = {} average_influence = 0 total_influence = 0 count = 0 if bci_results: total_influence = 0 for item in bci_results: if item['found']: total_influence += item['fields']['user_index'][0] try: average_influence = total_influence/len(results) except: average_influence = 0 if portrait_results: for item in portrait_results: if item["found"]: temp = [] count += 1 temp.append(item['_id']) temp.append(item["fields"]["importance"][0]) in_portrait.append(temp) temp_domain = item["fields"]["domain"][0].split('&') temp_topic = item["fields"]["topic_string"][0].split('&') temp_geo = json.loads(item["fields"]["activity_geo_dict"][0])[-1].keys() #total_influence += item["fields"]["influence"][0] retweeted_domain = aggregation(temp_domain, retweeted_domain) retweeted_topic = aggregation(temp_topic, retweeted_topic) retweeted_geo = aggregation(temp_geo, retweeted_geo) else: out_portrait.append(item['_id']) retweeted_domain = proportion(retweeted_domain) retweeted_topic = proportion(retweeted_topic) retweeted_geo = proportion(retweeted_geo) #try: # average_influence = total_influence/count #except: # average_influence = 0 sorted_retweeted_domain = sorted(retweeted_domain.items(),key=lambda x:x[1], reverse=True) sorted_retweeted_topic = sorted(retweeted_topic.items(),key=lambda x:x[1], reverse=True) sorted_retweeted_geo = sorted(retweeted_geo.items(), key=lambda x:x[1], reverse=True) retweeted_results = dict() retweeted_results["domian"] = sorted_retweeted_domain[:5] retweeted_results["topic"] = sorted_retweeted_topic[:5] retweeted_results["geo"] = sorted_retweeted_geo[:5] retweeted_results["influence"] = average_influence in_portrait = sorted(in_portrait, key=lambda x:x[1], reverse=True) temp_list = [] for item in in_portrait: temp_list.append(item[0]) retweeted_results['in_portrait_number'] = len(temp_list) retweeted_results['out_portrait_number'] = len(out_portrait) in_portrait_url = get_user_url(temp_list[:default_number]) out_portrait_url = get_user_url(out_portrait[:default_number]) return_results = dict() return_results["influence_users"] = [in_portrait_url, out_portrait_url] return_results["influence_distribution"] = retweeted_results return return_results
def get_retweet_weibo_detail(ts, user, task_name, size, text_type, type_value): _id = user + '-' + task_name task_detail = es_user_portrait.get(index=index_sensing_task, doc_type=_id, id=ts)['_source'] origin_weibo_detail = json.loads(task_detail['origin_weibo_detail']) retweeted_weibo_detail = json.loads(task_detail['retweeted_weibo_detail']) mid_list = [] mid_list.extend(origin_weibo_detail.keys()) mid_list.extend(retweeted_weibo_detail.keys()) query_body = { "query":{ "filtered":{ "filter":{ "bool":{ "must":[ {"range":{ "timestamp":{ "gte": ts - time_interval, "lt": ts } }}, {"terms": {"root_mid": mid_list}} ] } } } }, "sort": {"timestamp": {"order": "desc"}}, "size": 100 } if text_type == "message_type": query_body['query']['filtered']['filter']['bool']['must'].append({"term":{text_type: type_value}}) if text_type == "sentiment": #if isinstance(type_value, str): if len(type_value) == 1: query_body['query']['filtered']['filter']['bool']['must'].append({"term":{text_type: type_value}}) else: query_body['query']['filtered']['filter']['bool']['must'].append({"terms":{text_type: type_value}}) datetime = ts2datetime(ts) datetime_1 = ts2datetime(ts-time_interval) index_name = flow_text_index_name_pre + datetime exist_es = es_text.indices.exists(index_name) index_name_1 = flow_text_index_name_pre + datetime_1 exist_es_1 = es_text.indices.exists(index_name_1) # 1. 查询微博 if datetime == datetime_1 and exist_es: search_results = es_text.search(index=index_name, doc_type=flow_text_index_type, body=query_body)["hits"]["hits"] elif datetime != datetime_1 and exist_es_1: search_results = es_text.search(index=index_name_1, doc_type=flow_text_index_type, body=query_body)["hits"]["hits"] else: search_results = [] #print search_results # 2. 获取微博相关信息 results = [] uid_list = [] if search_results: for item in search_results: uid_list.append(item["_source"]['uid']) if uid_list: portrait_result = es_profile.mget(index=profile_index_name, doc_type=profile_index_type, body={"ids":uid_list}, fields=['nick_name', 'photo_url'])["docs"] for i in range(len(uid_list)): item = search_results[i]['_source'] temp = [] # uid, nick_name, photo_url, text, sentiment, timestamp, geo, common_keywords, message_type temp.append(item['uid']) if portrait_result[i]['found']: temp.append(portrait_result[i]["fields"]["nick_name"][0]) temp.append(portrait_result[i]["fields"]["photo_url"][0]) else: temp.append(item['uid']) temp.append("") temp.append(item["text"]) #print item['text'] temp.append(item["sentiment"]) temp.append(ts2date(item['timestamp'])) temp.append(item['geo']) temp.append(item["message_type"]) results.append(temp) return results
def search_group_sentiment_weibo(task_name, start_ts, sentiment, submit_user): weibo_list = [] task_id = submit_user + '-' + task_name #print es_group_result,group_index_name,group_index_type #step1:get task_name uid try: group_result = es_group_result.get(index=group_index_name, doc_type=group_index_type,\ id=task_id, _source=False, fields=['uid_list']) except: group_result = {} if group_result == {}: return 'task name invalid' try: uid_list = group_result['fields']['uid_list'] except: uid_list = [] if uid_list == []: return 'task uid list null' #step3: get ui2uname uid2uname = {} try: user_portrait_result = es_user_portrait.mget(index=portrait_index_name, doc_type=portrait_index_type,\ body={'ids':uid_list}, _source=False, fields=['uname'])['docs'] except: user_portrait_result = [] for item in user_portrait_result: uid = item['_id'] if item['found']==True: uname = item['fields']['uname'][0] uid2uname[uid] = uname else: uid2uname[uid] = 'unknown' #step4:iter date to search weibo weibo_list = [] iter_date = ts2datetime(start_ts) flow_text_index_name = flow_text_index_name_pre + str(iter_date) #step4: get query_body if sentiment != '2': query_body = [{'terms': {'uid': uid_list}}, {'term':{'sentiment': sentiment}}, \ {'range':{'timestamp':{'gte':start_ts, 'lt': start_ts+DAY}}}] else: query_body = [{'terms':{'uid':uid_list}}, {'terms':{'sentiment': SENTIMENT_SECOND}},\ {'range':{'timestamp':{'gte':start_ts, 'lt':start_ts+DAY}}}] try: flow_text_result = es_flow_text.search(index=flow_text_index_name, doc_type=flow_text_index_type,\ body={'query':{'bool':{'must': query_body}}, 'sort': [{'timestamp':{'order':'asc'}}], 'size': MAX_VALUE})['hits']['hits'] except: flow_text_result = [] for flow_text_item in flow_text_result: source = flow_text_item['_source'] weibo = {} weibo['uid'] = source['uid'] weibo['uname'] = uid2uname[weibo['uid']] weibo['ip'] = source['ip'] try: weibo['geo'] = '\t'.join(source['geo'].split('&')) except: weibo['geo'] = '' weibo['text'] = source['text'] weibo['timestamp'] = source['timestamp'] weibo['sentiment'] = source['sentiment'] weibo_list.append(weibo) return weibo_list
def get_positive_weibo_detail(ts, social_sensors, keywords_list, size, sentiment_type=1): former_mid_list = query_mid_list(ts - time_interval, keywords_list, time_segment, social_sensors) # 前一段时间内的微博mid list current_mid_list = query_mid_list(ts, keywords_list, time_interval, social_sensors) mid_list = [] mid_list.extend(former_mid_list) mid_list.extend(current_mid_list) query_body = { "query": { "filtered": { "filter": { "bool": { "must": [ { "range": { "timestamp": { "gte": ts - time_interval, "lt": ts } } }, ], "should": [{ "terms": { "root_mid": mid_list } }, { "terms": { "mid": mid_list } }, { "terms": { "keywords_string": keywords_list } }] } } } }, "sort": { "timestamp": { "order": "desc" } }, "size": 100 } #if social_sensors and int(sentiment_type) == 1: # query_body["query"]["filtered"]["filter"]["bool"]["must"].append({"terms":{"uid": social_sensors}}) if int(sentiment_type) == 1 or int(sentiment_type) == 0: query_body["query"]["filtered"]["filter"]["bool"]["must"].append( {"term": { "sentiment": sentiment_type }}) else: query_body["query"]["filtered"]["filter"]["bool"]["must"] = [{ "terms": { "sentiment": ["2", "3"] } }] # 判断当前ts和ts-time_interval是否属于同一天,确定查询哪个es datetime = ts2datetime(ts) datetime_1 = ts2datetime(ts - time_interval) index_name = flow_text_index_name_pre + datetime exist_es = es_text.indices.exists(index_name) index_name_1 = flow_text_index_name_pre + datetime_1 exist_es_1 = es_text.indices.exists(index_name_1) # 1. 聚合原创微博mid list if datetime == datetime_1 and exist_es: search_results = es_text.search(index=index_name, doc_type=flow_text_index_type, body=query_body)["hits"]["hits"] elif datetime != datetime_1 and exist_es_1: search_results = es_text.search(index=index_name_1, doc_type=flow_text_index_type, body=query_body)["hits"]["hits"] else: search_results = [] uid_list = [] results = [] if search_results: for item in search_results: uid_list.append(item["_source"]['uid']) if uid_list: portrait_result = es_profile.mget( index=profile_index_name, doc_type=profile_index_type, body={"ids": uid_list}, fields=['nick_name', 'photo_url'])["docs"] for i in range(len(uid_list)): item = search_results[i]['_source'] temp = [] # uid, nick_name, photo_url, text, sentiment, timestamp, geo, common_keywords, message_type temp.append(item['uid']) if portrait_result[i]['found']: temp.append(portrait_result[i]["fields"]["nick_name"][0]) temp.append(portrait_result[i]["fields"]["photo_url"][0]) else: temp.append("unknown") temp.append("") temp.append(item["text"]) temp.append(item["sentiment"]) temp.append(ts2date(item['timestamp'])) temp.append(item['geo']) keywords_set = set(item['keywords_string'].split('&')) common_keywords = set(keywords_list) & keywords_set temp.append(list(common_keywords)) temp.append(item['message_type']) results.append(temp) return results
def get_sensitive_text_detail(task_name, ts, user, order): _id = user + '-' + task_name task_detail = es.get(index=index_sensing_task, doc_type=_id, id=ts)['_source'] weibo_detail = json.loads(task_detail['sensitive_weibo_detail']) weibo_detail_list = [] if weibo_detail: for iter_mid, item in weibo_detail.iteritems(): tmp = [] tmp.append(iter_mid) tmp.append(item[iter_mid]) tmp.append(item['retweeted']) tmp.append(item['comment']) weibo_detail_list.append(tmp) mid_list = weibo_detail.keys() results = [] query_body = { "query": { "filtered": { "filter": { "terms": { "mid": mid_list } } } } } index_list = [] datetime = ts2datetime(ts) datetime_1 = ts2datetime(ts - DAY) index_name = flow_text_index_name_pre + datetime exist_es = es_text.indices.exists(index_name) if exist_es: index_list.append(index_name) index_name_1 = flow_text_index_name_pre + datetime_1 exist_es_1 = es_text.indices.exists(index_name_1) if exist_es_1: index_list.append(index_name_1) if index_list and mid_list: search_results = es_text.search(index=index_list, doc_type=flow_text_index_type, body=query_body)["hits"]["hits"] else: search_results = [] uid_list = [] text_dict = dict() # 文本信息 portrait_dict = dict() # 背景信息 if search_results: for item in search_results: uid_list.append(item["_source"]['uid']) text_dict[item['_id']] = item['_source'] # _id是mid if uid_list: portrait_result = es_profile.mget( index=profile_index_name, doc_type=profile_index_type, body={"ids": uid_list}, fields=['nick_name', 'photo_url'])["docs"] for item in portrait_result: if item['found']: portrait_dict[item['_id']] = { "nick_name": item["fields"]["nick_name"][0], "photo_url": item["fields"]["photo_url"][0] } else: portrait_dict[item['_id']] = { "nick_name": item['_id'], "photo_url": "" } if order == "total": sorted_list = sorted(weibo_detail_list, key=lambda x: x[1], reverse=True) elif order == "retweeted": sorted_list = sorted(weibo_detail_list, key=lambda x: x[2], reverse=True) elif order == "comment": sorted_list = sorted(weibo_detail_list, key=lambda x: x[3], reverse=True) else: sorted_list = weibo_detail_list count_n = 0 for item in sorted_list: mid = item[0] iter_text = text_dict.get(mid, {}) temp = [] # uid, nick_name, photo_url, text, sentiment, timestamp, geo, common_keywords, message_type if iter_text: uid = iter_text['uid'] temp.append(uid) iter_portrait = portrait_dict.get(uid, {}) if iter_portrait: temp.append(iter_portrait['nick_name']) temp.append(iter_portrait['photo_url']) else: temp.extend([uid, '']) temp.append(iter_text["text"]) temp.append(iter_text["sentiment"]) temp.append(ts2date(iter_text['timestamp'])) temp.append(iter_text['geo']) temp.append(iter_text['message_type']) temp.append(item[2]) temp.append(item[3]) temp.append(iter_text.get('sensitive', 0)) count_n += 1 results.append(temp) if results and order == "ts": results = sorted(results, key=lambda x: x[5], reverse=True) if results and order == "sensitive": results = sorted(results, key=lambda x: x[-1], reverse=True) return results
def localRec(uid, queryInterval=HOUR * 25 * 7, k=200): # 运行状态, # 0 -> 当前为2016-11-28 00:00:00 # 1 -> 当前时间 now_timestamp = datetime2ts(ts2datetime(time.time())) if RUN_TYPE == 0: now_timestamp = datetime2ts(RUN_TEST_TIME) flow_text_index_list = [] for i in range(7, 0, -1): iter_date = ts2datetime(now_timestamp - DAY * i) flow_text_index_list.append(flow_text_index_name_pre + iter_date) # 获取用户地理位置 # user_geos = get_user_geo(uid) # # 根据位置查询weibo # weibo_all = es_flow_text.search(index=flow_text_index_list, doc_type=ads_weibo_index_type, # body={"query":{"bool":{"must": # [{"match":{"keywords_string":"新闻"}}, # {"match":{"geo":"合肥"}} # ]}}, # "size": 200 # })["hits"]["hits"] '''可以直接查询长度大于100的但是很慢 {"query":{"filtered":{"query":{"bool":{"must":[{"match":{"keywords_string":"新闻"}},{"match":{"geo":"合肥"}}]}},"filter":{"regexp":{"text":{"value":".{100,}"}}}}}} ''' ip = get_user_ip(uid) ip = ".".join(ip.split(".")[:-2]) print '326' weibo_all = es_flow_text.search(index=flow_text_index_list, doc_type=ads_weibo_index_type, body={ "query": { "bool": { "must": [{ "prefix": { "text.ip": ip } }] } }, "size": 2000 })["hits"]["hits"] local_weibo_rec = [] weibo_user_uids = [weibo["_source"]["uid"] for weibo in weibo_all] print '332', len(weibo_all) # user_profiles = search_user_profile_by_user_ids(weibo_user_uids) exists_ip = set() topic_word_weight_dic = construct_topic_word_weight_dic( ADS_TOPIC_TFIDF_DIR) for weibo in weibo_all: weibo = weibo["_source"] weibo_text = weibo["text"] if weibo["ip"] in exists_ip: continue # 一个ip只选一个 exists_ip.add(weibo["ip"]) if not is_suit(weibo_text): continue weibo["len"] = len(weibo_text) try: mid = weibo["mid"] uid = weibo["uid"] except: continue weibo["weibo_url"] = weiboinfo2url(uid, mid) weibo["weibo_topic"] = judge_ads_topic(list(jieba.cut(weibo_text)), topic_word_weight_dic) # 可能出现许多userprofile查不到的情况 # if uid in user_profiles: # weibo["photo_url"] = user_profiles[uid]["photo_url"] # weibo["nick_name"] = user_profiles[uid]["nick_name"] # else: # weibo["photo_url"] = "None" # weibo["nick_name"] = "None" # local_weibo_rec.append(weibo) local_weibo_rec.append(weibo) return local_weibo_rec
def get_retweet_weibo_detail(ts, user, task_name, size, text_type, type_value): _id = user + '-' + task_name task_detail = es_user_portrait.get(index=index_sensing_task, doc_type=_id, id=ts)['_source'] origin_weibo_detail = json.loads(task_detail['origin_weibo_detail']) retweeted_weibo_detail = json.loads(task_detail['retweeted_weibo_detail']) mid_list = [] mid_list.extend(origin_weibo_detail.keys()) mid_list.extend(retweeted_weibo_detail.keys()) query_body = { "query": { "filtered": { "filter": { "bool": { "must": [{ "range": { "timestamp": { "gte": ts - time_interval, "lt": ts } } }, { "terms": { "root_mid": mid_list } }] } } } }, "sort": { "timestamp": { "order": "desc" } }, "size": 100 } if text_type == "message_type": query_body['query']['filtered']['filter']['bool']['must'].append( {"term": { text_type: type_value }}) if text_type == "sentiment": #if isinstance(type_value, str): if len(type_value) == 1: query_body['query']['filtered']['filter']['bool']['must'].append( {"term": { text_type: type_value }}) else: query_body['query']['filtered']['filter']['bool']['must'].append( {"terms": { text_type: type_value }}) datetime = ts2datetime(ts) datetime_1 = ts2datetime(ts - time_interval) index_name = flow_text_index_name_pre + datetime exist_es = es_text.indices.exists(index_name) index_name_1 = flow_text_index_name_pre + datetime_1 exist_es_1 = es_text.indices.exists(index_name_1) # 1. 查询微博 if datetime == datetime_1 and exist_es: search_results = es_text.search(index=index_name, doc_type=flow_text_index_type, body=query_body)["hits"]["hits"] elif datetime != datetime_1 and exist_es_1: search_results = es_text.search(index=index_name_1, doc_type=flow_text_index_type, body=query_body)["hits"]["hits"] else: search_results = [] #print search_results # 2. 获取微博相关信息 results = [] uid_list = [] if search_results: for item in search_results: uid_list.append(item["_source"]['uid']) if uid_list: portrait_result = es_profile.mget( index=profile_index_name, doc_type=profile_index_type, body={"ids": uid_list}, fields=['nick_name', 'photo_url'])["docs"] for i in range(len(uid_list)): item = search_results[i]['_source'] temp = [] # uid, nick_name, photo_url, text, sentiment, timestamp, geo, common_keywords, message_type temp.append(item['uid']) if portrait_result[i]['found']: temp.append(portrait_result[i]["fields"]["nick_name"][0]) temp.append(portrait_result[i]["fields"]["photo_url"][0]) else: temp.append(item['uid']) temp.append("") temp.append(item["text"]) #print item['text'] temp.append(item["sentiment"]) temp.append(ts2date(item['timestamp'])) temp.append(item['geo']) temp.append(item["message_type"]) results.append(temp) return results
def get_origin_weibo_detail(ts, user, task_name, size, order, message_type=1): _id = user + '-' + task_name task_detail = es_user_portrait.get(index=index_sensing_task, doc_type=_id, id=ts)['_source'] print '37',index_sensing_task,_id mid_value = json.loads(task_detail['mid_topic_value']) duplicate_dict = json.loads(task_detail['duplicate_dict']) tmp_duplicate_dict = dict() for k,v in duplicate_dict.iteritems(): try: tmp_duplicate_dict[v].append(k) except: tmp_duplicate_dict[v] = [k, v] if message_type == 1: weibo_detail = json.loads(task_detail['origin_weibo_detail']) elif message_type == 2: weibo_detail = json.loads(task_detail['retweeted_weibo_detail']) else: weibo_detail = json.loads(task_detail['sensitive_weibo_detail']) weibo_detail_list = [] if weibo_detail: for iter_mid, item in weibo_detail.iteritems(): tmp = [] tmp.append(iter_mid) tmp.append(item[iter_mid]) tmp.append(item['retweeted']) tmp.append(item['comment']) weibo_detail_list.append(tmp) mid_list = weibo_detail.keys() print len(mid_list) results = [] query_body = { "query":{ "filtered":{ "filter":{ "terms":{"mid": mid_list} } } }, "size": 1000, "sort": {"timestamp": {"order": "desc"}} } index_list = [] datetime = ts2datetime(ts) datetime_1 = ts2datetime(ts-DAY) index_name = flow_text_index_name_pre + datetime print es_text exist_es = es_text.indices.exists(index_name) print exist_es if exist_es: index_list.append(index_name) index_name_1 = flow_text_index_name_pre + datetime_1 exist_es_1 = es_text.indices.exists(index_name_1) if exist_es_1: index_list.append(index_name_1) if index_list and mid_list: search_results = es_text.search(index=index_list, doc_type=flow_text_index_type, body=query_body)["hits"]["hits"] else: search_results = [] uid_list = [] text_dict = dict() # 文本信息 portrait_dict = dict() # 背景信息 sort_results = [] if search_results: for item in search_results: uid_list.append(item["_source"]['uid']) text_dict[item['_id']] = item['_source'] # _id是mid if uid_list: portrait_result = es_profile.mget(index=profile_index_name, doc_type=profile_index_type, body={"ids":uid_list}, fields=['nick_name', 'photo_url'])["docs"] for item in portrait_result: if item['found']: portrait_dict[item['_id']] = {"nick_name": item["fields"]["nick_name"][0], "photo_url": item["fields"]["photo_url"][0]} else: portrait_dict[item['_id']] = {"nick_name": item['_id'], "photo_url":""} if order == "total": sorted_list = sorted(weibo_detail_list, key=lambda x:x[1], reverse=True)[:10] elif order == "retweeted": sorted_list = sorted(weibo_detail_list, key=lambda x:x[2], reverse=True)[:10] elif order == "comment": sorted_list = sorted(weibo_detail_list, key=lambda x:x[3], reverse=True)[:10] else: sorted_list = weibo_detail_list count_n = 0 results_dict = dict() mid_index_dict = dict() for item in sorted_list: # size mid = item[0] iter_text = text_dict.get(mid, {}) temp = [] # uid, nick_name, photo_url, text, sentiment, timestamp, geo, keywords_string, message_type if iter_text: uid = iter_text['uid'] temp.append(uid) iter_portrait = portrait_dict.get(uid, {}) if iter_portrait: temp.append(iter_portrait['nick_name']) temp.append(iter_portrait['photo_url']) else: temp.extend([uid,'']) temp.append(iter_text["text"]) temp.append(iter_text["sentiment"]) temp.append(ts2date(iter_text['timestamp'])) temp.append(iter_text['geo']) if message_type == 1: temp.append(1) elif message_type == 2: temp.append(3) else: temp.append(iter_text['message_type']) #jln 提取关键词 f_key = get_weibo_single(iter_text['text']) temp.append(sorted(f_key.iteritems(),key=lambda x:x[1],reverse=True)) temp.append(item[2]) temp.append(item[3]) temp.append(iter_text.get('sensitive', 0)) temp.append(iter_text['timestamp']) temp.append(mid_value[mid]) temp.append(mid) results.append(temp) count_n += 1 results = sorted(results, key=operator.itemgetter(-4, -2, -6), reverse=True) # -4 -2 -3 sort_results = [] count = 0 for item in results: sort_results.append([item]) mid_index_dict[item[-1]] = count count += 1 if tmp_duplicate_dict: remove_list = [] value_list = tmp_duplicate_dict.values() # [[mid, mid], ] for item in value_list: tmp = [] for mid in item: if mid_index_dict.get(mid, 0): tmp.append(mid_index_dict[mid]) if len(tmp) > 1: tmp_min = min(tmp) else: continue tmp.remove(tmp_min) for iter_count in tmp: sort_results[tmp_min].extend(sort_results[iter_count]) remove_list.append(sort_results[iter_count]) if remove_list: for item in remove_list: sort_results.remove(item) return sort_results
def get_positive_weibo_detail(ts, social_sensors, keywords_list, size, sentiment_type=1): former_mid_list = query_mid_list(ts-time_interval, keywords_list, time_segment, social_sensors) # 前一段时间内的微博mid list current_mid_list = query_mid_list(ts, keywords_list, time_interval, social_sensors) mid_list = [] mid_list.extend(former_mid_list) mid_list.extend(current_mid_list) query_body = { "query":{ "filtered":{ "filter":{ "bool":{ "must":[ {"range":{ "timestamp":{ "gte": ts - time_interval, "lt": ts } }}, ], "should":[ {"terms": {"root_mid": mid_list}}, {"terms": {"mid": mid_list}}, {"terms":{"keywords_string": keywords_list}} ] } } } }, "sort": {"timestamp": {"order": "desc"}}, "size": 100 } #if social_sensors and int(sentiment_type) == 1: # query_body["query"]["filtered"]["filter"]["bool"]["must"].append({"terms":{"uid": social_sensors}}) if int(sentiment_type) == 1 or int(sentiment_type) == 0: query_body["query"]["filtered"]["filter"]["bool"]["must"].append({"term":{"sentiment":sentiment_type}}) else: query_body["query"]["filtered"]["filter"]["bool"]["must"] = [{"terms":{"sentiment": ["2", "3"]}}] # 判断当前ts和ts-time_interval是否属于同一天,确定查询哪个es datetime = ts2datetime(ts) datetime_1 = ts2datetime(ts-time_interval) index_name = flow_text_index_name_pre + datetime exist_es = es_text.indices.exists(index_name) index_name_1 = flow_text_index_name_pre + datetime_1 exist_es_1 = es_text.indices.exists(index_name_1) # 1. 聚合原创微博mid list if datetime == datetime_1 and exist_es: search_results = es_text.search(index=index_name, doc_type=flow_text_index_type, body=query_body)["hits"]["hits"] elif datetime != datetime_1 and exist_es_1: search_results = es_text.search(index=index_name_1, doc_type=flow_text_index_type, body=query_body)["hits"]["hits"] else: search_results = [] uid_list = [] results = [] if search_results: for item in search_results: uid_list.append(item["_source"]['uid']) if uid_list: portrait_result = es_profile.mget(index=profile_index_name, doc_type=profile_index_type, body={"ids":uid_list}, fields=['nick_name', 'photo_url'])["docs"] for i in range(len(uid_list)): item = search_results[i]['_source'] temp = [] # uid, nick_name, photo_url, text, sentiment, timestamp, geo, common_keywords, message_type temp.append(item['uid']) if portrait_result[i]['found']: temp.append(portrait_result[i]["fields"]["nick_name"][0]) temp.append(portrait_result[i]["fields"]["photo_url"][0]) else: temp.append("unknown") temp.append("") temp.append(item["text"]) temp.append(item["sentiment"]) temp.append(ts2date(item['timestamp'])) temp.append(item['geo']) keywords_set = set(item['keywords_string'].split('&')) common_keywords = set(keywords_list) & keywords_set temp.append(list(common_keywords)) temp.append(item['message_type']) results.append(temp) return results
def new_get_user_weibo(uid, sort_type): results = [] weibo_list = [] now_date = ts2datetime(time.time()) #run_type if RUN_TYPE == 0: now_date = RUN_TEST_TIME sort_type = 'timestamp' #step1:get user name print '708' try: user_profile_result = es_user_profile.get(index=profile_index_name, doc_type=profile_index_type,\ id=uid, _source=False, fields=['nick_name']) except: user_profile_result = {} print '714',len(user_profile_result) if user_profile_result: uname = user_profile_result['fields']['nick_name'][0] else: uname = '' #step2:get user weibo for i in range(7, 0, -1): if RUN_TYPE == 1: iter_date = ts2datetime(datetime2ts(now_date) - i * DAY) else: iter_date = '2013-09-01' index_name = flow_text_index_name_pre + iter_date print '726' try: weibo_result = es_flow_text.search(index=index_name, doc_type=flow_text_index_type,\ body={'query':{'filtered':{'filter':{'term': {'uid': uid}}}}, 'size':MAX_VALUE})['hits']['hits'] #print weibo_result except: weibo_result = [] print '732',len(weibo_result) if weibo_result: weibo_list.extend(weibo_result) #sort_weibo_list = sorted(weibo_list, key=lambda x:x['_source'][sort_type], reverse=True)[:100] mid_set = set() for weibo_item in weibo_list: source = weibo_item['_source'] mid = source['mid'] uid = source['uid'] text = source['text'] ip = source['ip'] timestamp = source['timestamp'] date = ts2date(timestamp) sentiment = source['sentiment'] weibo_url = weiboinfo2url(uid, mid) #run_type if RUN_TYPE == 1: try: retweet_count = source['retweeted'] except: retweet_count = 0 try: comment_count = source['comment'] except: comment_count = 0 try: sensitive_score = source['sensitive'] except: sensitive_score = 0 else: retweet_count = 0 comment_count = 0 sensitive_score = 0 city = ip2city(ip) if mid not in mid_set: results.append([mid, uid, text, ip, city,timestamp, date, retweet_count, comment_count, sensitive_score, weibo_url]) mid_set.add(mid) if sort_type == 'timestamp': sort_results = sorted(results, key=lambda x:x[5], reverse=True) elif sort_type == 'retweet_count': sort_results = sorted(results, key=lambda x:x[7], reverse=True) elif sort_type == 'comment_count': sort_results = sorted(results, key=lambda x:x[8], reverse=True) elif sort_type == 'sensitive': sort_results = sorted(results, key=lambda x:x[9], reverse=True) print '778' return sort_results
def localRec(uid, queryInterval=HOUR*25*7, k=200): # 运行状态, # 0 -> 当前为2016-11-28 00:00:00 # 1 -> 当前时间 now_timestamp = datetime2ts(ts2datetime(time.time())) if RUN_TYPE == 0: now_timestamp = datetime2ts(RUN_TEST_TIME) flow_text_index_list = [] for i in range(7, 0, -1): iter_date = ts2datetime(now_timestamp - DAY * i) flow_text_index_list.append(flow_text_index_name_pre + iter_date) # 获取用户地理位置 # user_geos = get_user_geo(uid) # # 根据位置查询weibo # weibo_all = es_flow_text.search(index=flow_text_index_list, doc_type=ads_weibo_index_type, # body={"query":{"bool":{"must": # [{"match":{"keywords_string":"新闻"}}, # {"match":{"geo":"合肥"}} # ]}}, # "size": 200 # })["hits"]["hits"] '''可以直接查询长度大于100的但是很慢 {"query":{"filtered":{"query":{"bool":{"must":[{"match":{"keywords_string":"新闻"}},{"match":{"geo":"合肥"}}]}},"filter":{"regexp":{"text":{"value":".{100,}"}}}}}} ''' ip = get_user_ip(uid) ip = ".".join(ip.split(".")[:-2]) print '326' weibo_all = es_flow_text.search(index=flow_text_index_list, doc_type=ads_weibo_index_type, body={"query": {"bool": {"must": [{"prefix": {"text.ip": ip}}]}}, "size": 2000})["hits"]["hits"] local_weibo_rec = [] weibo_user_uids = [weibo["_source"]["uid"] for weibo in weibo_all] print '332',len(weibo_all) # user_profiles = search_user_profile_by_user_ids(weibo_user_uids) exists_ip = set() topic_word_weight_dic = construct_topic_word_weight_dic(ADS_TOPIC_TFIDF_DIR) for weibo in weibo_all: weibo = weibo["_source"] weibo_text = weibo["text"] if weibo["ip"] in exists_ip: continue # 一个ip只选一个 exists_ip.add(weibo["ip"]) if not is_suit(weibo_text): continue weibo["len"] = len(weibo_text) try: mid = weibo["mid"] uid = weibo["uid"] except: continue weibo["weibo_url"] = weiboinfo2url(uid, mid) weibo["weibo_topic"] = judge_ads_topic(list(jieba.cut(weibo_text)), topic_word_weight_dic) # 可能出现许多userprofile查不到的情况 # if uid in user_profiles: # weibo["photo_url"] = user_profiles[uid]["photo_url"] # weibo["nick_name"] = user_profiles[uid]["nick_name"] # else: # weibo["photo_url"] = "None" # weibo["nick_name"] = "None" # local_weibo_rec.append(weibo) local_weibo_rec.append(weibo) return local_weibo_rec
def aggregation_hot_keywords(start_time, stop_time, keywords_list): start_time = int(start_time) stop_time = int(stop_time) query_body = { "query": { "filtered": { "filter": { "bool": { "must": [{ "terms": { "keywords_string": keywords_list } }, { "range": { "timestamp": { "gte": start_time, "lt": stop_time } } }] } } } }, "aggs": { "all_keywords": { "terms": { "field": "keywords_string", "size": PRE_AGGREGATION_NUMBER } } } } keywords_dict = dict() datetime = ts2datetime(float(stop_time)) index_name = flow_text_index_name_pre + datetime exist_es = es_text.indices.exists(index_name) if exist_es: search_results = es_text.search( index=index_name, doc_type=flow_text_index_type, body=query_body)["aggregations"]['all_keywords']['buckets'] if search_results: for item in search_results: keywords_dict[item['key']] = item['doc_count'] datetime_1 = ts2datetime(float(start_time)) if datetime_1 == datetime: pass else: ts = float(stop_time) while 1: keywords_dict_1 = dict() ts = ts - day_time datetime = ts2datetime(ts) index_name = flow_text_index_name_pre + datetime exist_es = es_text.indices.exists(index_name) if exist_es: search_results_1 = es_text.search( index=index_name, doc_type=flow_text_index_type, body=query_body)["aggregations"]['all_keywords']['buckets'] if search_results_1: print search_results_1 for item in search_results_1: keywords_dict_1[item['key']] = item['doc_count'] for iter_key in keywords_dict_1.keys(): if keywords_dict.has_key(iter_key): keywords_dict[iter_key] += keywords_dict_1[iter_key] else: keywords_dict[iter_key] = keywords_dict_1[iter_key] if datetime_1 == datetime: break print keywords_dict return_dict = sorted(keywords_dict.items(), key=lambda x: x[1], reverse=True)[:AGGRAGATION_KEYWORDS_NUMBER] return return_dict
def influenced_user_detail(uid, date, origin_retweeted_mid, retweeted_retweeted_mid, message_type, default_number=20): query_body = { "query":{ "filtered":{ "filter":{ "bool":{ "must": [ ] } } } }, "size":100000, "sort":{"user_fansnum":{"order":"desc"}} } #详细影响到的人 date1 = str(date).replace('-', '') index_name = pre_index + date1 index_flow_text = pre_text_index + date origin_retweeted_uid = [] # influenced user uid_list retweeted_retweeted_uid = [] origin_comment_uid = [] retweeted_comment_uid = [] query_origin = copy.deepcopy(query_body) query_retweeted = copy.deepcopy(query_body) if origin_retweeted_mid: # 所有转发该条原创微博的用户 query_origin["query"]["filtered"]["filter"]["bool"]["must"].append({"terms": {"root_mid": origin_retweeted_mid}}) query_origin["query"]["filtered"]["filter"]["bool"]["must"].extend([{"term":{"message_type": message_type}}, {"term":{"root_uid": uid}}]) origin_retweeted_result = es.search(index=index_flow_text, doc_type=flow_text_index_type, body=query_origin, fields=["uid"])["hits"]["hits"] if origin_retweeted_result: for item in origin_retweeted_result: origin_retweeted_uid.append(item["fields"]["uid"][0]) if retweeted_retweeted_mid: # 所有评论该条原创微博的用户 query_retweeted["query"]["filtered"]["filter"]["bool"]["must"].append({"terms": {"root_mid": retweeted_retweeted_mid}}) query_retweeted["query"]["filtered"]["filter"]["bool"]["must"].extend([{"term":{"message_type": message_type}},{"term": {"directed_uid": uid}}]) retweeted_retweeted_result = es.search(index=index_flow_text, doc_type=flow_text_index_type, body=query_retweeted, fields=["uid"])["hits"]["hits"] if retweeted_retweeted_result: for item in retweeted_retweeted_result: retweeted_retweeted_uid.append(item["fields"]["uid"][0]) retweeted_uid_list = [] # all retweeted user list retweeted_results = {} # statistics of all retweeted uid information retweeted_domain = {} retweeted_topic = {} retweeted_geo = {} bci_results = {} in_portrait = [] out_portrait = [] average_influence = 0 total_influence = 0 count = 0 all_uid_set = set(origin_retweeted_uid) | set(retweeted_retweeted_uid) retweeted_uid_list.extend(origin_retweeted_uid) retweeted_uid_list.extend(retweeted_retweeted_uid) retweeted_uid_list = list(set(retweeted_uid_list) - set([uid])) # filter uids if retweeted_uid_list: user_portrait_result = es_user_portrait.mget(index=user_portrait, doc_type=portrait_index_type, body={"ids": retweeted_uid_list}, fields=["domain", "topic_string", "activity_geo_dict","importance", "influence"])["docs"] bci_index = "bci_" + date.replace('-', '') bci_results = es_cluster.mget(index=bci_index, doc_type="bci", body={"ids":retweeted_uid_list}, fields=['user_index'])["docs"] for item in user_portrait_result: if item["found"]: temp = [] count += 1 temp.append(item['_id']) temp.append(item["fields"]["importance"][0]) in_portrait.append(temp) temp_domain = item["fields"]["domain"][0].split('&') temp_topic = item["fields"]["topic_string"][0].split('&') temp_geo = json.loads(item["fields"]["activity_geo_dict"][0])[-1].keys() #total_influence += item["fields"]["influence"][0] retweeted_domain = aggregation(temp_domain, retweeted_domain) retweeted_topic = aggregation(temp_topic, retweeted_topic) retweeted_geo = aggregation(temp_geo, retweeted_geo) else: out_portrait.append(item['_id']) retweeted_domain = proportion(retweeted_domain) retweeted_topic = proportion(retweeted_topic) retweeted_geo = proportion(retweeted_geo) if bci_results: total_influence = 0 for item in bci_results: if item['found']: total_influence += item['fields']['user_index'][0] try: average_influence = total_influence/len(retweeted_uid_list) except: average_influence = 0 sorted_retweeted_domain = sorted(retweeted_domain.items(),key=lambda x:x[1], reverse=True) sorted_retweeted_topic = sorted(retweeted_topic.items(),key=lambda x:x[1], reverse=True) sorted_retweeted_geo = sorted(retweeted_geo.items(), key=lambda x:x[1], reverse=True) retweeted_results["domian"] = sorted_retweeted_domain[:5] retweeted_results["topic"] = sorted_retweeted_topic[:5] retweeted_results["geo"] = sorted_retweeted_geo[:5] retweeted_results["influence"] = average_influence in_portrait = sorted(in_portrait, key=lambda x:x[1], reverse=True) temp_list = [] for item in in_portrait: temp_list.append(item[0]) retweeted_results['in_portrait_number'] = len(temp_list) retweeted_results['out_portrait_number'] = len(out_portrait) in_portrait_url = get_user_url(temp_list[:default_number]) out_portrait_url = get_user_url(out_portrait[:default_number]) retweeted_results["in_portrait"] = in_portrait_url retweeted_results["out_portrait"] = out_portrait_url retweeted_results["total_number"] = len(temp_list) + len(out_portrait) return retweeted_results
def query_hot_mid(ts, keywords_list, text_type, size=100): query_body = { "query": { "filtered": { "filter": { "bool": { "must": [{ "range": { "timestamp": { "gte": ts - time_interval, "lt": ts } } }, { "terms": { "keywords_string": keywords_list } }, { "term": { "message_type": "0" } }] } } } }, "aggs": { "all_interests": { "terms": { "field": "root_mid", "size": size } } } } datetime = ts2datetime(ts) datetime_1 = ts2datetime(ts - time_interval) index_name = flow_text_index_name_pre + datetime exist_es = es_text.indices.exists(index_name) index_name_1 = flow_text_index_name_pre + datetime_1 exist_bool_1 = es_text.indices.exists(index_name_1) print datetime, datetime_1 if datetime == datetime_1 and exist_es: search_results = es_text.search( index=index_name, doc_type=flow_text_index_type, body=query_body)["aggregations"]["all_interests"]["buckets"] elif datetime != datetime_1 and exist_bool_1: search_results = es_text.search( index=index_name_1, doc_type=flow_text_index_type, body=query_body)["aggregations"]["all_interests"]["buckets"] else: search_results = [] hot_mid_list = [] if search_results: for item in search_results: print item temp = [] temp.append(item['key']) temp.append(item['doc_count']) hot_mid_list.append(temp) #print hot_mid_list return hot_mid_list
def search_group_sentiment_weibo(task_name, start_ts, sentiment, submit_user): weibo_list = [] task_id = submit_user + '-' + task_name #step1:get task_name uid try: group_result = es_group_result.get(index=group_index_name, doc_type=group_index_type,\ id=task_id, _source=False, fields=['uid_list']) except: group_result = {} if group_result == {}: return 'task name invalid' try: uid_list = group_result['fields']['uid_list'] except: uid_list = [] if uid_list == []: return 'task uid list null' #step3: get ui2uname uid2uname = {} try: user_portrait_result = es_user_portrait.mget(index=portrait_index_name, doc_type=portrait_index_type,\ body={'ids':uid_list}, _source=False, fields=['uname'])['docs'] except: user_portrait_result = [] for item in user_portrait_result: uid = item['_id'] if item['found'] == True: uname = item['fields']['uname'][0] uid2uname[uid] = uname #step4:iter date to search weibo weibo_list = [] iter_date = ts2datetime(start_ts) flow_text_index_name = flow_text_index_name_pre + str(iter_date) #step4: get query_body if sentiment != '2': query_body = [{'terms': {'uid': uid_list}}, {'term':{'sentiment': sentiment}}, \ {'range':{'timestamp':{'gte':start_ts, 'lt': start_ts+DAY}}}] else: query_body = [{'terms':{'uid':uid_list}}, {'terms':{'sentiment': SENTIMENT_SECOND}},\ {'range':{'timestamp':{'gte':start_ts, 'lt':start_ts+DAY}}}] try: flow_text_result = es_flow_text.search(index=flow_text_index_name, doc_type=flow_text_index_type,\ body={'query':{'bool':{'must': query_body}}, 'sort': [{'timestamp':{'order':'asc'}}], 'size': MAX_VALUE})['hits']['hits'] except: flow_text_result = [] for flow_text_item in flow_text_result: source = flow_text_item['_source'] weibo = {} weibo['uid'] = source['uid'] weibo['uname'] = uid2uname[weibo['uid']] weibo['ip'] = source['ip'] try: weibo['geo'] = '\t'.join(source['geo'].split('&')) except: weibo['geo'] = '' weibo['text'] = source['text'] weibo['timestamp'] = source['timestamp'] weibo['sentiment'] = source['sentiment'] weibo_list.append(weibo) return weibo_list
def get_origin_weibo_detail(ts, user, task_name, size, order, message_type=1): _id = user + '-' + task_name task_detail = es_user_portrait.get(index=index_sensing_task, doc_type=_id, id=ts)['_source'] print '37', index_sensing_task, _id mid_value = json.loads(task_detail['mid_topic_value']) duplicate_dict = json.loads(task_detail['duplicate_dict']) tmp_duplicate_dict = dict() for k, v in duplicate_dict.iteritems(): try: tmp_duplicate_dict[v].append(k) except: tmp_duplicate_dict[v] = [k, v] if message_type == 1: weibo_detail = json.loads(task_detail['origin_weibo_detail']) elif message_type == 2: weibo_detail = json.loads(task_detail['retweeted_weibo_detail']) else: weibo_detail = json.loads(task_detail['sensitive_weibo_detail']) weibo_detail_list = [] if weibo_detail: for iter_mid, item in weibo_detail.iteritems(): tmp = [] tmp.append(iter_mid) tmp.append(item[iter_mid]) tmp.append(item['retweeted']) tmp.append(item['comment']) weibo_detail_list.append(tmp) mid_list = weibo_detail.keys() print len(mid_list) results = [] query_body = { "query": { "filtered": { "filter": { "terms": { "mid": mid_list } } } }, "size": 1000, "sort": { "timestamp": { "order": "desc" } } } index_list = [] datetime = ts2datetime(ts) datetime_1 = ts2datetime(ts - DAY) index_name = flow_text_index_name_pre + datetime print es_text exist_es = es_text.indices.exists(index_name) print exist_es if exist_es: index_list.append(index_name) index_name_1 = flow_text_index_name_pre + datetime_1 exist_es_1 = es_text.indices.exists(index_name_1) if exist_es_1: index_list.append(index_name_1) if index_list and mid_list: search_results = es_text.search(index=index_list, doc_type=flow_text_index_type, body=query_body)["hits"]["hits"] else: search_results = [] uid_list = [] text_dict = dict() # 文本信息 portrait_dict = dict() # 背景信息 sort_results = [] if search_results: for item in search_results: uid_list.append(item["_source"]['uid']) text_dict[item['_id']] = item['_source'] # _id是mid if uid_list: portrait_result = es_profile.mget( index=profile_index_name, doc_type=profile_index_type, body={"ids": uid_list}, fields=['nick_name', 'photo_url'])["docs"] for item in portrait_result: if item['found']: portrait_dict[item['_id']] = { "nick_name": item["fields"]["nick_name"][0], "photo_url": item["fields"]["photo_url"][0] } else: portrait_dict[item['_id']] = { "nick_name": item['_id'], "photo_url": "" } if order == "total": sorted_list = sorted(weibo_detail_list, key=lambda x: x[1], reverse=True)[:10] elif order == "retweeted": sorted_list = sorted(weibo_detail_list, key=lambda x: x[2], reverse=True)[:10] elif order == "comment": sorted_list = sorted(weibo_detail_list, key=lambda x: x[3], reverse=True)[:10] else: sorted_list = weibo_detail_list count_n = 0 results_dict = dict() mid_index_dict = dict() for item in sorted_list: # size mid = item[0] iter_text = text_dict.get(mid, {}) temp = [] # uid, nick_name, photo_url, text, sentiment, timestamp, geo, keywords_string, message_type if iter_text: uid = iter_text['uid'] temp.append(uid) iter_portrait = portrait_dict.get(uid, {}) if iter_portrait: temp.append(iter_portrait['nick_name']) temp.append(iter_portrait['photo_url']) else: temp.extend([uid, '']) temp.append(iter_text["text"]) temp.append(iter_text["sentiment"]) temp.append(ts2date(iter_text['timestamp'])) temp.append(iter_text['geo']) if message_type == 1: temp.append(1) elif message_type == 2: temp.append(3) else: temp.append(iter_text['message_type']) #jln 提取关键词 f_key = get_weibo_single(iter_text['text']) temp.append( sorted(f_key.iteritems(), key=lambda x: x[1], reverse=True)) temp.append(item[2]) temp.append(item[3]) temp.append(iter_text.get('sensitive', 0)) temp.append(iter_text['timestamp']) temp.append(mid_value[mid]) temp.append(mid) results.append(temp) count_n += 1 results = sorted(results, key=operator.itemgetter(-4, -2, -6), reverse=True) # -4 -2 -3 sort_results = [] count = 0 for item in results: sort_results.append([item]) mid_index_dict[item[-1]] = count count += 1 if tmp_duplicate_dict: remove_list = [] value_list = tmp_duplicate_dict.values() # [[mid, mid], ] for item in value_list: tmp = [] for mid in item: if mid_index_dict.get(mid, 0): tmp.append(mid_index_dict[mid]) if len(tmp) > 1: tmp_min = min(tmp) else: continue tmp.remove(tmp_min) for iter_count in tmp: sort_results[tmp_min].extend(sort_results[iter_count]) remove_list.append(sort_results[iter_count]) if remove_list: for item in remove_list: sort_results.remove(item) return sort_results
def group_user_weibo(task_name, submit_user, sort_type): weibo_list = [] now_date = ts2datetime(time.time()) if sort_type == 'retweet': sort_type = 'retweeted' #run_type if RUN_TYPE == 0: now_date = RUN_TEST_TIME sort_type = 'timestamp' #step1: get group user task_id = submit_user + '-' + task_name try: group_exist_result = es_group_result.get(index=group_index_name, doc_type=group_index_type,\ id=task_id)['_source'] except: group_exist_result = {} if not group_exist_result: return 'group no exist' #step2: get user weibo list uid_list = group_exist_result['uid_list'] for i in range(6, -1, -1): iter_date = ts2datetime(datetime2ts(now_date) - i * DAY) index_name = flow_text_index_name_pre + iter_date try: weibo_result = es_flow_text.search(index=index_name, doc_type=flow_text_index_type,\ body={'query':{'filtered':{'filter':{'terms':{'uid': uid_list}}}}, 'sort':[{sort_type: {'order': 'desc'}}], 'size':100})['hits']['hits'] except: weibo_result = [] if weibo_result: weibo_list.extend(weibo_result) #sort_weibo_list = sorted(weibo_list, key=lambda x:x['_source'][sort_type], reverse=True)[:100] sort_weibo_list = weibo_list #step3: get user name try: portrait_exist_result = es_user_portrait.mget(index=portrait_index_name, doc_type=portrait_index_type, \ body={'ids':uid_list})['docs'] except: portrait_exist_result = [] uid2uname_dict = {} for portrait_item in portrait_exist_result: uid = portrait_item['_id'] if portrait_item['found'] == True: source = portrait_item['_source'] uname = source['uname'] else: uname = 'unknown' uid2uname_dict[uid] = uname weibo_list = [] for weibo_item in sort_weibo_list: source = weibo_item['_source'] mid = source['mid'] uid = source['uid'] uname = uid2uname_dict[uid] text = source['text'] ip = source['geo'] timestamp = source['timestamp'] date = ts2date(timestamp) sentiment = source['sentiment'] weibo_url = weiboinfo2url(uid, mid) #run_type: if RUN_TYPE == 1: try: retweet_count = source['retweeted'] except: retweet_count = 0 try: comment_count = source['comment'] except: comment_count = 0 try: sensitive_score = source['sensitive'] except: sensitive_score = 0 else: retweet_count = 0 comment_count = 0 sensitive_score = 0 city = ip2city(ip) weibo_list.append([ mid, uid, uname, text, ip, city, timestamp, date, retweet_count, comment_count, sensitive_score, weibo_url ]) if sort_type == 'timestamp': new_weibo_list = sorted(weibo_list, key=lambda x: x[6], reverse=True) elif sort_type == 'retweeted': new_weibo_list = sorted(weibo_list, key=lambda x: x[8], reverse=True) elif sort_type == 'comment': new_weibo_list = sorted(weibo_list, key=lambda x: x[9], reverse=True) elif sort_type == 'sensitive': new_weibo_list = sorted(weibo_list, key=lambda x: x[10], reverse=True) return new_weibo_list
def group_user_weibo(task_name, submit_user, sort_type): weibo_list = [] now_date = ts2datetime(time.time()) if sort_type == 'retweet': sort_type = 'retweeted' #run_type if RUN_TYPE == 0: now_date = RUN_TEST_TIME sort_type = 'timestamp' #step1: get group user task_id = submit_user + '-' + task_name try: group_exist_result = es_group_result.get(index=group_index_name, doc_type=group_index_type,\ id=task_id)['_source'] except: group_exist_result = {} if not group_exist_result: return 'group no exist' #step2: get user weibo list uid_list = group_exist_result['uid_list'] for i in range(6,-1,-1): iter_date = ts2datetime(datetime2ts(now_date) - i * DAY) index_name = flow_text_index_name_pre + iter_date try: weibo_result = es_flow_text.search(index=index_name, doc_type=flow_text_index_type,\ body={'query':{'filtered':{'filter':{'terms':{'uid': uid_list}}}}, 'sort':[{sort_type: {'order': 'desc'}}], 'size':100})['hits']['hits'] except: weibo_result = [] if weibo_result: weibo_list.extend(weibo_result) #sort_weibo_list = sorted(weibo_list, key=lambda x:x['_source'][sort_type], reverse=True)[:100] sort_weibo_list = weibo_list #step3: get user name try: portrait_exist_result = es_user_portrait.mget(index=portrait_index_name, doc_type=portrait_index_type, \ body={'ids':uid_list})['docs'] except: portrait_exist_result = [] uid2uname_dict = {} for portrait_item in portrait_exist_result: uid = portrait_item['_id'] if portrait_item['found'] == True: source = portrait_item['_source'] uname = source['uname'] else: uname = 'unknown' uid2uname_dict[uid] = uname weibo_list = [] for weibo_item in sort_weibo_list: source = weibo_item['_source'] mid = source['mid'] uid = source['uid'] uname = uid2uname_dict[uid] text = source['text'] ip = source['geo'] timestamp = source['timestamp'] date = ts2date(timestamp) sentiment = source['sentiment'] weibo_url = weiboinfo2url(uid, mid) #run_type: if RUN_TYPE == 1: try: retweet_count = source['retweeted'] except: retweet_count = 0 try: comment_count = source['comment'] except: comment_count = 0 try: sensitive_score = source['sensitive'] except: sensitive_score = 0 else: retweet_count = 0 comment_count = 0 sensitive_score = 0 city = ip2city(ip) weibo_list.append([mid, uid, uname, text, ip, city, timestamp, date, retweet_count, comment_count, sensitive_score, weibo_url]) if sort_type == 'timestamp': new_weibo_list = sorted(weibo_list, key=lambda x:x[6], reverse=True) elif sort_type == 'retweeted': new_weibo_list = sorted(weibo_list, key=lambda x:x[8], reverse=True) elif sort_type == 'comment': new_weibo_list = sorted(weibo_list, key=lambda x:x[9], reverse=True) elif sort_type == 'sensitive': new_weibo_list = sorted(weibo_list, key=lambda x:x[10], reverse=True) return new_weibo_list
def search_weibo(root_uid, uid, mtype): query_body = { #'query':{ 'filter': { 'bool': { 'must': [{ 'term': { 'uid': uid } }, { 'term': { 'message_type': mtype } }], 'should': [{ 'term': { 'root_uid': root_uid } }, { 'term': { 'directed_uid': root_uid } }], } } #} } index_list = [] for i in range(7, 0, -1): if RUN_TYPE == 1: iter_date = ts2datetime(datetime2ts(now_date) - i * DAY) else: iter_date = ts2datetime(datetime2ts(RUN_TEST_TIME) - i * DAY) index_list.append(flow_text_index_name_pre + iter_date) results = es_flow_text.search(index=index_list, doc_type=flow_text_index_type, body=query_body)['hits']['hits'] weibo = {} f_result = [] if len(results) > 0: for result in results: ## print type(result),result weibo['last_text'] = [ result['_source']['text'], result['_source']['text'], result['_source']['timestamp'] ] mid = result['_source']['root_mid'] # # print mid len_pre = len(flow_text_index_name_pre) index = result['_index'][len_pre:] root_index = [] for j in range(0, 7): #一周的,一个月的话就0,30 iter_date = ts2datetime(datetime2ts(index) - j * DAY) root_index.append(flow_text_index_name_pre + iter_date) results0 = es_flow_text.search( index=root_index, doc_type=flow_text_index_type, body={'query': { 'term': { 'mid': mid } }})['hits']['hits'] if len(results0) > 0: for result0 in results0: weibo['ori_text'] = [ result0['_source']['text'], result0['_source']['timestamp'] ] f_result.append(weibo) weibo = {} return f_result
def aggregation_hot_keywords(start_time, stop_time, keywords_list): start_time = int(start_time) stop_time = int(stop_time) query_body = { "query":{ "filtered":{ "filter":{ "bool":{ "must":[ {"terms": {"keywords_string": keywords_list}}, {"range":{ "timestamp":{ "gte":start_time, "lt": stop_time } }} ] } } } }, "aggs":{ "all_keywords":{ "terms": {"field": "keywords_string", "size": PRE_AGGREGATION_NUMBER} } } } keywords_dict = dict() datetime = ts2datetime(float(stop_time)) index_name = flow_text_index_name_pre + datetime exist_es = es_text.indices.exists(index_name) if exist_es: search_results = es_text.search(index=index_name, doc_type=flow_text_index_type, body=query_body)["aggregations"]['all_keywords']['buckets'] if search_results: for item in search_results: keywords_dict[item['key']] = item['doc_count'] datetime_1 = ts2datetime(float(start_time)) if datetime_1 == datetime: pass else: ts = float(stop_time) while 1: keywords_dict_1 = dict() ts = ts-day_time datetime = ts2datetime(ts) index_name = flow_text_index_name_pre + datetime exist_es = es_text.indices.exists(index_name) if exist_es: search_results_1 = es_text.search(index=index_name, doc_type=flow_text_index_type, body=query_body)["aggregations"]['all_keywords']['buckets'] if search_results_1: print search_results_1 for item in search_results_1: keywords_dict_1[item['key']] = item['doc_count'] for iter_key in keywords_dict_1.keys(): if keywords_dict.has_key(iter_key): keywords_dict[iter_key] += keywords_dict_1[iter_key] else: keywords_dict[iter_key] = keywords_dict_1[iter_key] if datetime_1 == datetime: break print keywords_dict return_dict = sorted(keywords_dict.items(), key=lambda x:x[1], reverse=True)[:AGGRAGATION_KEYWORDS_NUMBER] return return_dict
def get_sensitive_text_detail(task_name, ts, user, order): _id = user + '-' + task_name task_detail = es.get(index=index_sensing_task, doc_type=_id, id=ts)['_source'] weibo_detail = json.loads(task_detail['sensitive_weibo_detail']) weibo_detail_list = [] if weibo_detail: for iter_mid, item in weibo_detail.iteritems(): tmp = [] tmp.append(iter_mid) tmp.append(item[iter_mid]) tmp.append(item['retweeted']) tmp.append(item['comment']) weibo_detail_list.append(tmp) mid_list = weibo_detail.keys() results = [] query_body = { "query":{ "filtered":{ "filter":{ "terms":{"mid": mid_list} } } } } index_list = [] datetime = ts2datetime(ts) datetime_1 = ts2datetime(ts-DAY) index_name = flow_text_index_name_pre + datetime exist_es = es_text.indices.exists(index_name) if exist_es: index_list.append(index_name) index_name_1 = flow_text_index_name_pre + datetime_1 exist_es_1 = es_text.indices.exists(index_name_1) if exist_es_1: index_list.append(index_name_1) if index_list and mid_list: search_results = es_text.search(index=index_list, doc_type=flow_text_index_type, body=query_body)["hits"]["hits"] else: search_results = [] uid_list = [] text_dict = dict() # 文本信息 portrait_dict = dict() # 背景信息 if search_results: for item in search_results: uid_list.append(item["_source"]['uid']) text_dict[item['_id']] = item['_source'] # _id是mid if uid_list: portrait_result = es_profile.mget(index=profile_index_name, doc_type=profile_index_type, body={"ids":uid_list}, fields=['nick_name', 'photo_url'])["docs"] for item in portrait_result: if item['found']: portrait_dict[item['_id']] = {"nick_name": item["fields"]["nick_name"][0], "photo_url": item["fields"]["photo_url"][0]} else: portrait_dict[item['_id']] = {"nick_name": item['_id'], "photo_url":""} if order == "total": sorted_list = sorted(weibo_detail_list, key=lambda x:x[1], reverse=True) elif order == "retweeted": sorted_list = sorted(weibo_detail_list, key=lambda x:x[2], reverse=True) elif order == "comment": sorted_list = sorted(weibo_detail_list, key=lambda x:x[3], reverse=True) else: sorted_list = weibo_detail_list count_n = 0 for item in sorted_list: mid = item[0] iter_text = text_dict.get(mid, {}) temp = [] # uid, nick_name, photo_url, text, sentiment, timestamp, geo, common_keywords, message_type if iter_text: uid = iter_text['uid'] temp.append(uid) iter_portrait = portrait_dict.get(uid, {}) if iter_portrait: temp.append(iter_portrait['nick_name']) temp.append(iter_portrait['photo_url']) else: temp.extend([uid,'']) temp.append(iter_text["text"]) temp.append(iter_text["sentiment"]) temp.append(ts2date(iter_text['timestamp'])) temp.append(iter_text['geo']) temp.append(iter_text['message_type']) temp.append(item[2]) temp.append(item[3]) temp.append(iter_text.get('sensitive', 0)) count_n += 1 results.append(temp) if results and order == "ts": results = sorted(results, key=lambda x:x[5], reverse=True) if results and order == "sensitive": results = sorted(results, key=lambda x:x[-1], reverse=True) return results