def get_top_user():
    results = dict()
    domain_results = dict()
    topic_results = dict()
    for item in domain_list:
        search_query_body = query_body("domain", item)
        search_results = es.search(index="sensitive_user_portrait", doc_type="user", body=search_query_body, _source=False, fields=['uname','photo_url'])['hits']['hits']
        uid_list = []
        for iter_item in search_results:
            uid_list.append([iter_item['_id'], iter_item['fields']['uname'][0], iter_item['fields']['photo_url'][0]])
        domain_results[item] = uid_list


    for item in topic_list:
        search_query_body = query_body("topic_string", item)
        search_results = es.search(index="sensitive_user_portrait", doc_type="user", body=search_query_body, _source=False, fields=['uname','photo_url'])['hits']['hits']
        uid_list = []
        for iter_item in search_results:
            uid_list.append([iter_item['_id'], iter_item['fields']['uname'][0], iter_item['fields']['photo_url'][0]])
        topic_results[item] = uid_list

    results['domain_rank'] = domain_results
    results['topic_rank'] = topic_results

    return results
예제 #2
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def search_important(category, detail):
    query_body = {
        "query": {
            "filtered": {
                "filter": {
                    "term": {
                        category: detail
                    }
                }
            }
        },
        "sort": {
            "sensitive": {
                "order": "desc"
            }
        },
        "size": 20
    }
    results = es.search(index="sensitive_user_portrait",
                        doc_type="user",
                        body=query_body,
                        _source=False,
                        fields=['uname'])['hits']['hits']
    uid_list = []
    for item in results:
        uid_list.append([item['_id'], item['fields']['uname'][0]])
    return uid_list
def get_evaluate_max():
    max_result = {}
    evaluate_index = ['activeness', 'importance', 'influence', 'sensitive']
    for evaluate in evaluate_index:
        query_body = {
            "query": {
                'match_all': {}
            },
            "size": 1,
            'sort': [{
                evaluate: {
                    'order': 'desc'
                }
            }]
        }
        result = es.search(index=portrait_index_name,
                           doc_type=portrait_index_type,
                           body=query_body)['hits']['hits']
        max_evaluate = result[0]['_source'][evaluate]
        if max_evaluate != 0:
            max_result[evaluate] = max_evaluate
        else:
            max_result[evaluate] = 99999

    return max_result
def get_user_portrait_byidname(uid, isuid=True, specify_field=[]):
    uid_list = [uid]
    results = []
    max_result = get_evaluate_max()
    fields_list = ['uname','domain','topic_string','politics','fansnum','statusnum','friendsnum','location', 'hashtag', 'activity_geo', 'keywords_string']
    if specify_field:
        fields_list = specify_field

    if isuid:
        search_results = es.mget(index=portrait_index_name,doc_type=portrait_index_type,body={"ids":uid_list}, _source=False, \
            fields=['uname','domain','topic_string','politics','fansnum','statusnum', 'hashtag_string', 'activity_geo', 'friendsnum','location','activeness','importance','influence','sensitive', 'keywords_dict'])["docs"]
        for item in search_results:
            iter_result = []
            iter_result.append(item['_id'])
            if item['found']:
                for iter_field in fields_list:
                    if iter_field == "topic_string":
                        iter_result.append(item['fields'][iter_field][0])
                        #iter_result.append(item['fields'][iter_field][0].split('&'))
                    elif iter_field == "keywords_dict":
                        iter_result.append(json.loads(item['fields'][iter_field][0]))
                    else:
                        iter_result.append(item['fields'][iter_field][0])

            else:
                iter_result = None
            results.append(iter_result)
    else:
        query_body = {
            "query":{
                "bool": {
                    "should": [
                        {"term": {"uname": uid}}
                    ]
                }
            },
            "size": 1
        }
        search_results = es.search(index=portrait_index_name,doc_type=portrait_index_type,body=query_body, \
            fields=['uname','domain','topic_string','politics','fansnum','statusnum', 'hashtag_string', 'activity_geo', 'friendsnum','location','activeness','importance','influence','sensitive', 'keywords_dict'])['hits']['hits']
        if len(search_results) == 0:
            results.append(None)
        for item in search_results:
            iter_result = []
            iter_result.append(item['_id'])
            for iter_field in fields_list:
                if iter_field == "topic_string":
                    iter_result.append(item['fields'][iter_field][0])
                    #iter_result.append(item['fields'][iter_field][0].split('&'))
                elif iter_field == "keywords_dict":
                    iter_result.append(json.loads(item['fields'][iter_field][0]))
                else:
                    iter_result.append(item['fields'][iter_field][0])

            results.append(iter_result)

    return results
def search_in_portrait(category, max_result):
    query_body={
        "query":{
            "match_all": {}
        },
        "sort": {category: {"order": "desc"}}
    }
    results = es.search(index="sensitive_user_portrait", doc_type="user", body=query_body)['hits']['hits']
    uid_list = []
    for item in results:
        uid_list.append([item['_source']['uid'], item['_source']['uname'], normalize_index(item['_source'][category], max_result[category])])
    return uid_list
def get_top_user():
    results = dict()
    domain_results = dict()
    topic_results = dict()
    for item in domain_list:
        search_query_body = query_body("domain", item)
        search_results = es.search(index="sensitive_user_portrait",
                                   doc_type="user",
                                   body=search_query_body,
                                   _source=False,
                                   fields=['uname',
                                           'photo_url'])['hits']['hits']
        uid_list = []
        for iter_item in search_results:
            uid_list.append([
                iter_item['_id'], iter_item['fields']['uname'][0],
                iter_item['fields']['photo_url'][0]
            ])
        domain_results[item] = uid_list

    for item in topic_list:
        search_query_body = query_body("topic_string", item)
        search_results = es.search(index="sensitive_user_portrait",
                                   doc_type="user",
                                   body=search_query_body,
                                   _source=False,
                                   fields=['uname',
                                           'photo_url'])['hits']['hits']
        uid_list = []
        for iter_item in search_results:
            uid_list.append([
                iter_item['_id'], iter_item['fields']['uname'][0],
                iter_item['fields']['photo_url'][0]
            ])
        topic_results[item] = uid_list

    results['domain_rank'] = domain_results
    results['topic_rank'] = topic_results

    return results
def search_important(category, detail):
    query_body={
        "query":{
            "filtered":{
                "filter":{
                    "term": {category: detail}
                 }
            }
        },
        "sort": {"sensitive": {"order": "desc"}},
        "size": 20
    }
    results = es.search(index="sensitive_user_portrait", doc_type="user", body=query_body, _source=False, fields=['uname'])['hits']['hits']
    uid_list = []
    for item in results:
        uid_list.append([item['_id'], item['fields']['uname'][0]])
    return uid_list
def compute_mid_result_one(task_name, task_user, start_ts):
    result = []
    #step1: count the sensitive or not weibo count
    #step2: count the sensitive or not weibo geo count
    #step3: sentiment in sensitive / unsensitive
    #step4: compute hashtag
    #step5: compute sensitive_word
    #save mid_result
    query_body = []
    #query user
    query_body.append({'term':{'uid': task_user[0]}})
    #query time_segment
    query_body.append({'range':{'timestamp':{'from':start_ts, 'to':start_ts+900}}})
    try:
        task_user_weibo = es.search(index=text_index_name, doc_type=text_index_type,\
            body={'query':{'bool':{'must': query_body}}, 'size':100000})['hits']['hits']
    except Exception,e:
        raise e
def get_evaluate_max():
    max_result = {}
    evaluate_index = ['activeness', 'importance', 'influence', 'sensitive']
    for evaluate in evaluate_index:
        query_body = {
            "query":{
                'match_all':{}
                },
            "size":1,
            'sort':[{evaluate: {'order': 'desc'}}]
        }
        result = es.search(index=portrait_index_name, doc_type=portrait_index_type, body=query_body)['hits']['hits']
        max_evaluate = result[0]['_source'][evaluate]
        if max_evaluate != 0:
            max_result[evaluate] = max_evaluate
        else:
            max_result[evaluate] = 99999

    return max_result
예제 #10
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def search_in_portrait(category, max_result):
    query_body = {
        "query": {
            "match_all": {}
        },
        "sort": {
            category: {
                "order": "desc"
            }
        }
    }
    results = es.search(index="sensitive_user_portrait",
                        doc_type="user",
                        body=query_body)['hits']['hits']
    uid_list = []
    for item in results:
        uid_list.append([
            item['_source']['uid'], item['_source']['uname'],
            normalize_index(item['_source'][category], max_result[category])
        ])
    return uid_list
예제 #11
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def compute_mid_result_one(task_name, task_user, start_ts):
    result = []
    #step1: count the sensitive or not weibo count
    #step2: count the sensitive or not weibo geo count
    #step3: sentiment in sensitive / unsensitive
    #step4: compute hashtag
    #step5: compute sensitive_word
    #save mid_result
    query_body = []
    #query user
    query_body.append({'term': {'uid': task_user[0]}})
    #query time_segment
    query_body.append(
        {'range': {
            'timestamp': {
                'from': start_ts,
                'to': start_ts + 900
            }
        }})
    try:
        task_user_weibo = es.search(index=text_index_name, doc_type=text_index_type,\
            body={'query':{'bool':{'must': query_body}}, 'size':100000})['hits']['hits']
    except Exception, e:
        raise e
def compute_mid_result_group(task_name, task_user, start_ts):
    result = []
    #step1: count the sensitive or not weibo count
    #step2: count the geo weibo count
    #step3: count the sentiment weibo count
    #step4: compute hashtag
    #step5: compute sensitive
    #step: compute the social
    #save mid result
    sensitive_weibo_dict = {}
    #sentiment_weibo_dict = {'0':{}, '1':{}}
    #geo_weibo_dict = {'0':{}, '1':{}}
    #hashtag_weibo_dict = {'0':{}, '1':{}}
    sentiment_weibo_dict = {}
    geo_weibo_dict = {}
    hashtag_weibo_dict = {}
    sensitive_word_dict = {}
    search_count = 0
    for uid in task_user:
        query_body = []
        query_body.append({'term':{'uid':str(uid)}})
        query_body.append({'range':{'timestamp':{'from': start_ts, 'to':start_ts+900}}})
        try:
            user_weibo = es.search(index=text_index_name, doc_type=text_index_type, \
                    body={'query':{'bool':{'must':query_body}}, 'size':100000})['hits']['hits']
        except Exception, e:
            raise e
        print 'user_weibo:', len(user_weibo)
        search_count += len(user_weibo)
        if user_weibo:
            for weibo_item in user_weibo:
                weibo_dict = weibo_item['_source']
                #compute sensitive_weibo_count and unsensitive_weibo_count in time-segment
                sensitive = weibo_dict['sensitive']
                try:
                    sensitive_weibo_dict[str(sensitive)] += 1
                except:
                    sensitive_weibo_dict[str(sensitive)] = 1
                #compute geo_weibo_count
                geo = weibo_dict['geo']
                if str(sensitive) in geo_weibo_dict:
                    try:
                        geo_weibo_dict[str(sensitive)][geo] += 1
                    except:
                        geo_weibo_dict[str(sensitive)][geo] = 1
                else:
                    geo_weibo_dict[str(sensitive)] = {geo: 1}
                #compute sentiment_weibo_count
                sentiment = weibo_dict['sentiment']

                if str(sensitive) in sentiment_weibo_dict:
                    try:
                        sentiment_weibo_dict[str(sensitive)][sentiment] += 1
                    except:
                        sentiment_weibo_dict[str(sensitive)][sentiment] = 1
                else:
                    sentiment_weibo_dict[str(sensitive)] = {sentiment: 1}
                #compute hashtag_weibo_dict
                try:
                    hashtag_list = weibo_dict['hashtag'].split('&')
                except:
                    hashtag_list = None
                if hashtag_list:
                    for hashtag in hashtag_list:
                        if str(sensitive) in hashtag_weibo_dict:
                            try:
                                hashtag_weibo_dict[str(sensitive)][hashtag] += 1
                            except:
                                hashtag_weibo_dict[str(sensitive)][hashtag] = 1
                        else:
                            hashtag_weibo_dict[str(sensitive)] = {hashtag: 1}
                #compute sensitive_word_dict
                try:
                    sensitive_word_list = weibo_dict['sensitive_word'].split('&')
                except:
                    sensitive_word_list = None
                if sensitive_word_list:
                    for sensitive_word in sensitive_word_list:
                        try:
                            sensitive_word_dict[sensitive_word] += 1
                        except:
                            sensitive_word_dict[sensitive_word] = 1
def get_attr(date):
    results = dict()
    total_number = es.count(index="sensitive_user_portrait", doc_type="user")['count']
    results['total_number'] = total_number

    max_result = get_evaluate_max()
    query_body={
        "query":{
            "filtered":{
                "filter":{
                    "term":{
                        "sensitive": 0
                    }
                }
            }
        }
    }
    influence_number = es.count(index="sensitive_user_portrait", doc_type="user", body=query_body)['count']
    results['sensitive_number'] = total_number - influence_number
    results['influence_number'] = influence_number

    # 政治倾向性统计
    query_body = query_body_module('politics')
    politic_array =  es.search(index='sensitive_user_portrait', doc_type='user', body=query_body)['aggregations']['all_interests']['buckets']
    politic_dict = dict()
    for item in politic_array:
        politic_dict[item['key']] = item['doc_count']
    results['politics'] = politic_dict

    # 入库推荐人数
    recommend_in_sensitive = 0
    recommend_in_sensitive = r.hlen("recomment_" + date +'sensitive')

    recommend_in_influence = 0
    recommend_in_influence = r.hlen("recomment_" + date + "_influence")
    results['recommend_in'] = recommend_in_influence + recommend_in_sensitive

    # 群体分析任务
    results['monitor_number'] = [4, 83] # test
    query_body = {
        "query":{
            "bool":{
                "must":[
                    {"term":{'task_type':"detect"}},
                    {"term":{"state":0}}
                ]
            }
        }
    }
    group_detect_number = es.count(index=group_index_name, doc_type=group_index_type, body=query_body)["count"]
    query_body = {
        "query":{
            "bool":{
                "must":[
                    {"term":{'task_type':"analysis"}},
                    {"term":{"state":0}}
                ]
            }
        }
    }
    group_analysis_number = es.count(index=group_index_name, doc_type=group_index_type, body=query_body)["count"]
    results["group_detect_number"] = group_detect_number
    results["group_analysis_number"] = group_analysis_number


    # 敏感词
    query_body = query_body_module('sensitive_words_string')
    sw_list =  es.search(index='sensitive_user_portrait', doc_type='user', body=query_body)['aggregations']['all_interests']['buckets']
    sensitive_words = []
    for item in sw_list:
        temp = []
        temp.append(item['key'])
        temp.append(item['doc_count'])
        sensitive_words.append(temp)
    results['sensitive_words'] = sensitive_words

    query_body = query_body_module('keywords_string')
    sg_list =  es.search(index='sensitive_user_portrait', doc_type='user', body=query_body)['aggregations']['all_interests']['buckets']
    sensitive_geo = []
    for item in sg_list:
        temp = []
        temp.append(item['key'])
        temp.append(item['doc_count'])
        sensitive_geo.append(temp)
    results['keywords_string'] = sensitive_geo

    query_body = query_body_module('sensitive_hashtag_string')
    sh_list =  es.search(index='sensitive_user_portrait', doc_type='user', body=query_body)['aggregations']['all_interests']['buckets']
    sensitive_hashtag = []
    for item in sh_list:
        temp = []
        temp.append(item['key'])
        temp.append(item['doc_count'])
        sensitive_hashtag.append(temp)
    results['sensitive_hashtag'] = sensitive_hashtag

    query_body = query_body_module('sensitive_activity_geo_aggs')
    sg_list =  es.search(index='sensitive_user_portrait', doc_type='user', body=query_body)['aggregations']['all_interests']['buckets']
    sensitive_geo = []
    for item in sg_list:
        temp = []
        temp.append(item['key'])
        temp.append(item['doc_count'])
        sensitive_geo.append(temp)
    results['sensitive_geo'] = sensitive_geo


    '''
    query_body = query_body_module('domain_string')
    sd_list =  es.search(index='sensitive_user_portrait', doc_type='user', body=query_body)['aggregations']['all_interests']['buckets']
    domain = []
    for item in sd_list:
        temp = []
        temp.append(item['key'])
        temp.append(item['doc_count'])
        domain.append(temp)
    results['domain'] = domain
    '''

    # tendency distribution


    # domain and topic
    rank_results = get_top_user()
    results.update(rank_results)



    # rank
    results['importance'] = search_in_portrait('importance', max_result)
    results['sensitive'] = search_in_portrait('sensitive', max_result)
    results['influence'] = search_in_portrait('influence', max_result)
    results['activeness'] = search_in_portrait('activeness', max_result)

    # 敏感微博转发量和评论量
    mid_list = get_top_mid()
    sensitive_hot_retweet = sort_retweet_sensitive_weibo(mid_list)
    sensitive_hot_comment = sort_comment_sensitive_weibo(mid_list)
    sensitive_weibo_text = get_weibo_detail(mid_list)

    results['sensitive_hot_retweet'] = sensitive_hot_retweet
    results['sensitive_hot_comment'] = sensitive_hot_comment
    results['sensitive_weibo_text'] = sensitive_weibo_text

    r.set('overview', json.dumps(results))
예제 #14
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def get_attr(date):
    results = dict()
    total_number = es.count(index="sensitive_user_portrait",
                            doc_type="user")['count']
    results['total_number'] = total_number

    max_result = get_evaluate_max()
    query_body = {
        "query": {
            "filtered": {
                "filter": {
                    "term": {
                        "sensitive": 0
                    }
                }
            }
        }
    }
    influence_number = es.count(index="sensitive_user_portrait",
                                doc_type="user",
                                body=query_body)['count']
    results['sensitive_number'] = total_number - influence_number
    results['influence_number'] = influence_number

    # 政治倾向性统计
    query_body = query_body_module('politics')
    politic_array = es.search(
        index='sensitive_user_portrait', doc_type='user',
        body=query_body)['aggregations']['all_interests']['buckets']
    politic_dict = dict()
    for item in politic_array:
        politic_dict[item['key']] = item['doc_count']
    results['politics'] = politic_dict

    # 入库推荐人数
    recommend_in_sensitive = 0
    recommend_in_sensitive = r.hlen("recomment_" + date + 'sensitive')

    recommend_in_influence = 0
    recommend_in_influence = r.hlen("recomment_" + date + "_influence")
    results['recommend_in'] = recommend_in_influence + recommend_in_sensitive

    # 群体分析任务
    results['monitor_number'] = [4, 83]  # test
    query_body = {
        "query": {
            "bool": {
                "must": [{
                    "term": {
                        'task_type': "detect"
                    }
                }, {
                    "term": {
                        "state": 0
                    }
                }]
            }
        }
    }
    group_detect_number = es.count(index=group_index_name,
                                   doc_type=group_index_type,
                                   body=query_body)["count"]
    query_body = {
        "query": {
            "bool": {
                "must": [{
                    "term": {
                        'task_type': "analysis"
                    }
                }, {
                    "term": {
                        "state": 0
                    }
                }]
            }
        }
    }
    group_analysis_number = es.count(index=group_index_name,
                                     doc_type=group_index_type,
                                     body=query_body)["count"]
    results["group_detect_number"] = group_detect_number
    results["group_analysis_number"] = group_analysis_number

    # 敏感词
    query_body = query_body_module('sensitive_words_string')
    sw_list = es.search(
        index='sensitive_user_portrait', doc_type='user',
        body=query_body)['aggregations']['all_interests']['buckets']
    sensitive_words = []
    for item in sw_list:
        temp = []
        temp.append(item['key'])
        temp.append(item['doc_count'])
        sensitive_words.append(temp)
    results['sensitive_words'] = sensitive_words

    query_body = query_body_module('keywords_string')
    sg_list = es.search(
        index='sensitive_user_portrait', doc_type='user',
        body=query_body)['aggregations']['all_interests']['buckets']
    sensitive_geo = []
    for item in sg_list:
        temp = []
        temp.append(item['key'])
        temp.append(item['doc_count'])
        sensitive_geo.append(temp)
    results['keywords_string'] = sensitive_geo

    query_body = query_body_module('sensitive_hashtag_string')
    sh_list = es.search(
        index='sensitive_user_portrait', doc_type='user',
        body=query_body)['aggregations']['all_interests']['buckets']
    sensitive_hashtag = []
    for item in sh_list:
        temp = []
        temp.append(item['key'])
        temp.append(item['doc_count'])
        sensitive_hashtag.append(temp)
    results['sensitive_hashtag'] = sensitive_hashtag

    query_body = query_body_module('sensitive_activity_geo_aggs')
    sg_list = es.search(
        index='sensitive_user_portrait', doc_type='user',
        body=query_body)['aggregations']['all_interests']['buckets']
    sensitive_geo = []
    for item in sg_list:
        temp = []
        temp.append(item['key'])
        temp.append(item['doc_count'])
        sensitive_geo.append(temp)
    results['sensitive_geo'] = sensitive_geo
    '''
    query_body = query_body_module('domain_string')
    sd_list =  es.search(index='sensitive_user_portrait', doc_type='user', body=query_body)['aggregations']['all_interests']['buckets']
    domain = []
    for item in sd_list:
        temp = []
        temp.append(item['key'])
        temp.append(item['doc_count'])
        domain.append(temp)
    results['domain'] = domain
    '''

    # tendency distribution

    # domain and topic
    rank_results = get_top_user()
    results.update(rank_results)

    # rank
    results['importance'] = search_in_portrait('importance', max_result)
    results['sensitive'] = search_in_portrait('sensitive', max_result)
    results['influence'] = search_in_portrait('influence', max_result)
    results['activeness'] = search_in_portrait('activeness', max_result)

    # 敏感微博转发量和评论量
    mid_list = get_top_mid()
    sensitive_hot_retweet = sort_retweet_sensitive_weibo(mid_list)
    sensitive_hot_comment = sort_comment_sensitive_weibo(mid_list)
    sensitive_weibo_text = get_weibo_detail(mid_list)

    results['sensitive_hot_retweet'] = sensitive_hot_retweet
    results['sensitive_hot_comment'] = sensitive_hot_comment
    results['sensitive_weibo_text'] = sensitive_weibo_text

    r.set('overview', json.dumps(results))
def get_user_portrait_byidname(uid, isuid=True, specify_field=[]):
    uid_list = [uid]
    results = []
    max_result = get_evaluate_max()
    fields_list = [
        'uname', 'domain', 'topic_string', 'politics', 'fansnum', 'statusnum',
        'friendsnum', 'location', 'hashtag', 'activity_geo', 'keywords_string'
    ]
    if specify_field:
        fields_list = specify_field

    if isuid:
        search_results = es.mget(index=portrait_index_name,doc_type=portrait_index_type,body={"ids":uid_list}, _source=False, \
            fields=['uname','domain','topic_string','politics','fansnum','statusnum', 'hashtag_string', 'activity_geo', 'friendsnum','location','activeness','importance','influence','sensitive', 'keywords_dict'])["docs"]
        for item in search_results:
            iter_result = []
            iter_result.append(item['_id'])
            if item['found']:
                for iter_field in fields_list:
                    if iter_field == "topic_string":
                        iter_result.append(item['fields'][iter_field][0])
                        #iter_result.append(item['fields'][iter_field][0].split('&'))
                    elif iter_field == "keywords_dict":
                        iter_result.append(
                            json.loads(item['fields'][iter_field][0]))
                    else:
                        iter_result.append(item['fields'][iter_field][0])

            else:
                iter_result = None
            results.append(iter_result)
    else:
        query_body = {
            "query": {
                "bool": {
                    "should": [{
                        "term": {
                            "uname": uid
                        }
                    }]
                }
            },
            "size": 1
        }
        search_results = es.search(index=portrait_index_name,doc_type=portrait_index_type,body=query_body, \
            fields=['uname','domain','topic_string','politics','fansnum','statusnum', 'hashtag_string', 'activity_geo', 'friendsnum','location','activeness','importance','influence','sensitive', 'keywords_dict'])['hits']['hits']
        if len(search_results) == 0:
            results.append(None)
        for item in search_results:
            iter_result = []
            iter_result.append(item['_id'])
            for iter_field in fields_list:
                if iter_field == "topic_string":
                    iter_result.append(item['fields'][iter_field][0])
                    #iter_result.append(item['fields'][iter_field][0].split('&'))
                elif iter_field == "keywords_dict":
                    iter_result.append(
                        json.loads(item['fields'][iter_field][0]))
                else:
                    iter_result.append(item['fields'][iter_field][0])

            results.append(iter_result)

    return results
예제 #16
0
def compute_mid_result_group(task_name, task_user, start_ts):
    result = []
    #step1: count the sensitive or not weibo count
    #step2: count the geo weibo count
    #step3: count the sentiment weibo count
    #step4: compute hashtag
    #step5: compute sensitive
    #step: compute the social
    #save mid result
    sensitive_weibo_dict = {}
    #sentiment_weibo_dict = {'0':{}, '1':{}}
    #geo_weibo_dict = {'0':{}, '1':{}}
    #hashtag_weibo_dict = {'0':{}, '1':{}}
    sentiment_weibo_dict = {}
    geo_weibo_dict = {}
    hashtag_weibo_dict = {}
    sensitive_word_dict = {}
    search_count = 0
    for uid in task_user:
        query_body = []
        query_body.append({'term': {'uid': str(uid)}})
        query_body.append(
            {'range': {
                'timestamp': {
                    'from': start_ts,
                    'to': start_ts + 900
                }
            }})
        try:
            user_weibo = es.search(index=text_index_name, doc_type=text_index_type, \
                    body={'query':{'bool':{'must':query_body}}, 'size':100000})['hits']['hits']
        except Exception, e:
            raise e
        print 'user_weibo:', len(user_weibo)
        search_count += len(user_weibo)
        if user_weibo:
            for weibo_item in user_weibo:
                weibo_dict = weibo_item['_source']
                #compute sensitive_weibo_count and unsensitive_weibo_count in time-segment
                sensitive = weibo_dict['sensitive']
                try:
                    sensitive_weibo_dict[str(sensitive)] += 1
                except:
                    sensitive_weibo_dict[str(sensitive)] = 1
                #compute geo_weibo_count
                geo = weibo_dict['geo']
                if str(sensitive) in geo_weibo_dict:
                    try:
                        geo_weibo_dict[str(sensitive)][geo] += 1
                    except:
                        geo_weibo_dict[str(sensitive)][geo] = 1
                else:
                    geo_weibo_dict[str(sensitive)] = {geo: 1}
                #compute sentiment_weibo_count
                sentiment = weibo_dict['sentiment']

                if str(sensitive) in sentiment_weibo_dict:
                    try:
                        sentiment_weibo_dict[str(sensitive)][sentiment] += 1
                    except:
                        sentiment_weibo_dict[str(sensitive)][sentiment] = 1
                else:
                    sentiment_weibo_dict[str(sensitive)] = {sentiment: 1}
                #compute hashtag_weibo_dict
                try:
                    hashtag_list = weibo_dict['hashtag'].split('&')
                except:
                    hashtag_list = None
                if hashtag_list:
                    for hashtag in hashtag_list:
                        if str(sensitive) in hashtag_weibo_dict:
                            try:
                                hashtag_weibo_dict[str(
                                    sensitive)][hashtag] += 1
                            except:
                                hashtag_weibo_dict[str(sensitive)][hashtag] = 1
                        else:
                            hashtag_weibo_dict[str(sensitive)] = {hashtag: 1}
                #compute sensitive_word_dict
                try:
                    sensitive_word_list = weibo_dict['sensitive_word'].split(
                        '&')
                except:
                    sensitive_word_list = None
                if sensitive_word_list:
                    for sensitive_word in sensitive_word_list:
                        try:
                            sensitive_word_dict[sensitive_word] += 1
                        except:
                            sensitive_word_dict[sensitive_word] = 1