| 123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960616263646566676869707172737475767778798081828384858687888990919293949596979899100101102103104105106107108109110111112113114115116117118119120121122123124125126127128129130131132133134135136137138139140141142143144145146147148149150151152153154155156157158159160161162163164165166167168169170171172173174175176177178179180181182183184185186187188189190191192193194195196197198199200201202203204205206207208209210211212213214215216217218219220221222223224225226227228229230231232233234235236237238239240241242243244245246247248249250251252253254255256257258259260261262263264265266267268269270271272273274275276277278279280281282283284285286287288289290291292293294295296297298299300301302303304305306307308309310311312313314315316317318319320321322323324325326327328329330331332333334335336337338339340341342343344345346347348349350351352353354355356357358359360361362363364365366367368369370371372373374375376377378379380381382383384385386387388389390391392393394395396397398399400401402403404405406407408409410411412413414415416417418419420421422423424425426427428429 | from mysql_db import MysqlDBfrom excel_util import ExcelUtilclass Mvp:    """     ce mvp 答题数据统计     城市特例 北京市,上海市, 重庆市,天津市    """    age_dict = {        '00-04年生': '00后',        '05-09年生': '05后',        '50-59年生': '50后',        '60-69年生': '60后',        '70-74年生': '70后',        '75-79年生': '75后',        '80-84年生': '80后',        '85-89年生': '85后',        '90-94年生': '90后',        '95-99年生': '95后'    }    crowd = ['A', 'B', 'C', 'D', 'E', 'F']    # 获取答题记录中城市列表    sql_1 = 'select city from f_t_daren_score_2 group by city'    # 获取父选项和父题id    sql_2 = 'select a.id, a.content, b.id, b.name from bq_option a left join bq_question b on a.question_id = b.id ' \            'where a.serial_number = %s and b.serial_number = %s and a.status = b.status = 1 '    # 获取答题人的年龄段集合    sql_4 = 'select nld from f_t_daren_score_2 group by nld'    # 根据城市,年龄段,人群分类统计答题记录数    sql_5 = 'select group_type, COUNT(uuid) from f_t_daren_score_2 where (city = %s or province = %s) and nld ' \            '= %s and uuid in %s group by group_type '    # 根据父选项获取子选项id列表    sql_6 = 'SELECT c.id, c.sub_question_id, c.content FROM bq_sub_option c WHERE c.father_id in (SELECT a.id FROM ' \            'bq_option a ' \            'LEFT JOIN bq_question b ON a.question_id = b.id WHERE a.serial_number = %s AND b.serial_number = %s ' \            'and a.status = 1 and b.status = 1) and c.status = 1 '    # 根据子题id获取包含子题id的测试    sql_7 = 'select group_type from bq_testcase where status = 1 and FIND_IN_SET(%s, question_ids)'    # 根据子选项id统计答题数    sql_8 = 'SELECT count(1) FROM f_t_daren_score_2 a LEFT JOIN d_shangju_tiku_02 b ON a.sub_question_id = ' \            'b.sub_question_id AND (a.score  = b.score or a.score = b.sub_option_id) and a.testcase_id = ' \            'b.testcase_id WHERE b.sub_option_id in %s' \            'and (a.city = %s or a.province = %s) and a.nld = %s and a.uuid in %s'    # 获取一个uuid下答题的子选项id列表    sql_10 = 'select  DISTINCT uuid, GROUP_CONCAT(DISTINCT b.sub_option_id)  from f_t_daren_score_2 a left join ' \             'd_shangju_tiku_02 b on a.sub_question_id = b.sub_question_id and (a.score = b.score or a.score = ' \             'b.sub_option_id) where a.status = ' \             'b.status = 1 group by uuid '    # 向表mvp_crowd_info插入数据    sql_11 = 'insert into mvp_crowd_info(age_area, city_name, crowd_type, status) values(%s, %s, %s, 1)'    # 向表mvp_crowd_info_behavior中插入数据    sql_12 = 'insert into mvp_crowd_info_behavior(crowd_info_id, behavioral_interest, standard_value, status) values(' \             '%s, %s, ' \             '%s, 1) '    # 向表mvp_crowd_info_module中插入数据    sql_13 = 'insert into mvp_crowd_info_module(crowd_info_id, module_name, standard_value, status) values (%s, %s, ' \             '%s, 1) '    sql_14 = 'select a.id, a.age_area, a.city_name, a.crowd_type from mvp_crowd_info a where a.status = 1'    def __init__(self, path=None):        self.shangju_db = MysqlDB('shangju')        self.marketing_db = MysqlDB('bi_report')        # self.shangju_db.truncate('mvp_standard_score')        self.tag_data = ExcelUtil(file_name=path).init_mvp_data()        self.crowd_info = ExcelUtil(file_name=path, sheet_name='选项-人群分类对应表').init_crowd_info()        self.citys = self.init_city()        self.age = self.init_age()        self.people_sub_option_ids = self.marketing_db.select(self.sql_10)        self.crowd_contain_sub_option_ids = self.get_crowd_contain_sub_option_ids()        self.module_scores = ExcelUtil(file_name='set-behavior-tag.xlsx', sheet_name='算法关系表').init_module_info()        # self.scores_tag = ExcelUtil(file_name='行为与模块分值汇总.xlsx', sheet_name='行为').init_scores()        # self.score_module = ExcelUtil(file_name='行为与模块分值汇总.xlsx', sheet_name='模块').init_scores()        self.scores_tag = None        self.score_module = None    def close(self):        self.shangju_db.close()        self.marketing_db.close()    def init_city(self):        """            获取答题数据中的城市。        :return:        """        citys = ['宁波市', '上海市', '苏州市', '无锡市', '宁波市']        # citys_info = self.marketing_db.select(self.sql_1)        # citys.extend([x[0] for x in citys_info if x[0] is not None])        return citys    def query_behavioral_info(self, city=None, age=None, crowd=None):        """            查询行为兴趣信息        :return:        """        # datas = []        # for key in self.tag_data.keys():        #     values = self.tag_data[key]        #     for value in values:        #         question = value[0].split('-')[0]        #         option = value[0].split('-')[1]        #         corr = value[1]        #         data = self.shangju_db.select(self.sql_2, [option, question])        #         if len(data) > 0:        #             print([question, option, data[0][3], data[0][1], key, corr])        #             datas.append([question, option, data[0][3], data[0][1], key, corr])        # self.shangju_db.truncate('mvp_question_classification')        # self.shangju_db.add_some(self.sql_3, datas)        scores_behavioral = self.city_age_crowd(city, age, crowd)        # scores_module = self.module_score(crowd, city, age, scores_behavioral['score'])        # result = {'行为兴趣分值': scores_behavioral['score'], '模块分值': scores_module}        print('update finished!!!')        return scores_behavioral    def module_score(self, crowd, city, age, scores):        """            模块分数计算            城市 年龄 人群分类 模块名称 分数        :return:        """        import json        print(json.dumps(scores, ensure_ascii=False))        modules = self.module_scores[crowd]        result = []        for key in modules.keys():            values = modules[key]            module_name = key            score = 0            for value in values:                behavioral_name = value[0]                weight = float(value[2])                standard_score = [x[4] for x in scores if x[2] == behavioral_name]                if len(standard_score) > 0:                    score += standard_score[0] * weight            result.append([city, age, crowd, module_name, score])        return result    # def insert_data(self, scores_behavioral, scores_module):    def insert(self):        """            计算数据写入数据库中,供接口查看        :return:        """        infos = []        for city in ['上海市', '宁波市', '苏州市', '杭州市', ' 无锡市']:            for age in ['50-59年生', '60-69年生', '70-74年生', '75-79年生', '80-84年生', '85-89年生', '90-94年生', '95-99年生', '00'                                                                                                                '-04年生',                        '05-09年生']:                for c_type in ['A', 'B', 'C', 'D', 'E', 'F']:                    age_area = self.age_dict.get(age)                    if age_area:                        infos.append([age_area, city, c_type])        self.shangju_db.add_some(self.sql_11, infos)    def query_data(self):        ids = self.shangju_db.select(self.sql_14)        return ids    def shanghai_85_module_score_insert(self):        """            上海市,85后模块分数计算        :return:        """        result = []        for crowd in self.crowd:            modules = self.module_scores[crowd]            for key in modules.keys():                values = modules[key]                module_name = key                score = 0                for value in values:                    behavioral_name = value[0]                    weight = float(value[2])                    # standard_score = [x[4] for x in scores if x[2] == behavioral_name]                    standard_score = float(value[1])                    if standard_score is not None:                        score += standard_score * weight                result.append(['上海市', '85后', crowd, module_name, score])        return {'score': result, 'data': self.module_scores}    def tag_module_score_insert(self):        """            标签模块分数写入数据库        :return:        """        ids = self.query_data()        insert_data = []        insert_data_1 = []        for tag, module in zip(self.scores_tag, self.score_module):            city = tag[0]            age = tag[1]            crowd = tag[2]            tag_name = tag[3]            tag_score = tag[4]            city_2 = module[0]            age_2 = module[1]            crowd_2 = module[2]            module_name_2 = module[3]            module_score_2 = module[4]            for id in ids:                city_1 = id[2]                age_1 = id[1]                crowd_1 = id[3]                id_1 = id[0]                if city == city_1 and self.age_dict[age] == age_1 and crowd == crowd_1:                    insert_data.append([id_1, tag_name, tag_score])                if city_2 == city_1 and self.age_dict[age_2] == age_1 and crowd_2 == crowd_1:                    insert_data_1.append([id_1, module_name_2, module_score_2])        self.shangju_db.add_some(self.sql_12, insert_data)        self.shangju_db.add_some(self.sql_13, insert_data_1)    def init_age(self):        """           获取答题数据中的年龄        """        age_info = self.marketing_db.select(self.sql_4)        # print([x[0] for x in age_info])        return [x[0] for x in age_info if x[0] is not None]    def city_age_crowd(self, city=None, age=None, crowd=None):        data_start = []        result = []        module_scores = []        if city is not None and age is not None and crowd is not None:            print('获取指定城市,年龄段,人群类型的数据...')            people_uuids = self.get_people_uuid_by_type(crowd)            behavior_data = None            if len(people_uuids) > 0:                print('{}-{}-{}'.format(city, age, crowd))                datas = self.behavior_tag_init(city, age, people_uuids)                data_start.append(datas)                all_data, behavior_data_1 = self.calculation_standard_score(datas, city, age, crowd)                result.append(all_data)                behavior_data = behavior_data_1            # if behavior_data:            #     module_scores.extend(self.module_score(crowd, city, age, behavior_data))        else:            print('获取所有case的数据...')            # for city in self.citys:            # for city in [city]:            for age in self.age:                for crowd_type in self.crowd:                    if age == '85-89年生' and city == '上海市':                        print('上海市85后数据导入人工值,无需计算...')                        pass                    else:                        # print(' {}{}'.format(city, age))                        people_uuids = self.get_people_uuid_by_type(crowd_type)                        behavior_data = None                        if len(people_uuids) > 0:                            print('{}-{}-{}'.format(city, age, crowd_type))                            datas = self.behavior_tag_init(city, age, people_uuids)                            data_start.append(datas)                            all_data, behavior_data_1 = self.calculation_standard_score(datas, city, age, crowd)                            result.append(all_data)                            behavior_data = behavior_data_1                        if behavior_data:                           module_scores.extend(self.module_score(crowd_type, city, age, behavior_data))        # return result        # data_list = []        # for e in data_start:        #     for key in e.keys():        #         values = e[key]        #         for sub_e in values:        #             ele = [key]        #             ele.extend(sub_e)        #             data_list.append(ele)        #     pass        # return {'tag_score': result, 'module_score': module_scores}        return {'tag_score': result}        # return {'score': result, 'data': data_list}    def behavior_tag_init(self, city, age, people_uuids):        result = {}        self.group_type_count = self.marketing_db.select(self.sql_5, [city, city, age, people_uuids])        # 表名        for key in self.tag_data:            values = self.tag_data[key]            result_sub = {}            # 标签            for key_tag_name in values.keys():                questions = values[key_tag_name]                elements = []                for value in questions:                    question = value[0].split('-')[0]                    option = value[0].split('-')[1]                    corr = value[1]                    fz, fm = self.molecular_value(question, option, city, age, people_uuids)                    if fm == 0:                        c = 0                    else:                        c = fz / fm                    elements.append([question, option, corr, fz, fm, c])                result_sub[key_tag_name] = elements            result[key] = self.indicator_calculation_d_e(result_sub)        return result    def molecular_value(self, queston, option, city, age, people_uuids):        # 获取当前父选项包含的子选项id和子题id列表        result = self.shangju_db.select(self.sql_6, [option, queston])        sub_option_ids = []        group_types = []        for rt in result:            sub_option_id, sub_question_id, content = rt[0], rt[1], rt[2]            grouptypes = self.shangju_db.select(self.sql_7, [sub_question_id])            for g_t in grouptypes:                if g_t[0] not in group_types:                    group_types.append(g_t[0])            sub_option_ids.append(sub_option_id)        # 计算子选项在答题记录中的点击数        sub_options_count = 0        if len(sub_option_ids) > 0:            result_1 = self.marketing_db.select(self.sql_8, [sub_option_ids, city, city, age, people_uuids])            sub_options_count = result_1[0][0]        # 计算父选项包含的子选项对应的子题所在的测试gt包含的点击数。        denominator_value = 0        for info in self.group_type_count:            if info[0] in group_types:                denominator_value += info[1]        return sub_options_count, denominator_value    def indicator_calculation_d_e(self, data):        result = {}        for key in data.keys():            values = data[key]            c_list = []            for x in values:                _x = x[5]                if _x is not None and x != 0:                    c_list.append(_x)            fm_list = [x[4] for x in values]            sum_c = sum(fm_list)            if len(c_list) == 0:                min_c = 0            else:                min_c = min(c_list)            elements = []            for value in values:                _value = []                c = value[5]                if sum_c == 0:                    d = 0                else:                    d = c / sum_c                e = c - min_c                _value.extend(value)                _value.append(d)                _value.append(e)                elements.append(_value)            result[key] = elements        return result    def calculation_standard_score(self, datas, city, age, crowd_type):        scores = {}        for key_tag_type in datas.keys():            print(key_tag_type)            tag_type_data = datas[key_tag_type]            scores_sub = []            for key_tag in tag_type_data.keys():                key_tag_data = tag_type_data[key_tag]                print(key_tag)                print('     父题序号 父选项序号 相关系系数 分子值 分母值 百分比 人数权重 偏离值')                values = [x[5] for x in key_tag_data]                min_c = min(values)                f = min_c                for value in key_tag_data:                    print('     {}'.format(value))                    if value[2] is not None and value[7] is not None:                        f += float(value[2] * value[7])                print('     标准分:{}'.format(f))                scores_sub.append([city, age, key_tag, crowd_type, f])            scores[key_tag_type] = scores_sub            # self.shangju_db.add_some(self.sql_9, scores)        return scores, scores['用户画像-行为兴趣']    def get_crowd_people(self):        result = {}        for type in self.crowd:            uuids = self.get_people_uuid_by_type(type)            result[type] = len(uuids)        return result    def get_people_uuid_by_type(self, type):        uuids = []        type_sub_option_ids = self.crowd_contain_sub_option_ids[type]        for people in self.people_sub_option_ids:            uuid = people[0]            sub_option_ids = list(map(int, str(people[1]).split(',')))            # list(set(a).intersection(set(b)))            if len(list(set(sub_option_ids).intersection(set(type_sub_option_ids)))) > 0 and uuid not in uuids:                uuids.append(uuid)        return uuids    def get_crowd_contain_sub_option_ids(self):        """            获取ABCDEF人群包含的子选项id        :return:        """        infos = {}        for key in self.crowd_info.keys():            values = self.crowd_info[key]            sub_option_ids = []            for value in values:                if value is not None:                    vals = str(value).split('-')                    option, question = vals[1], vals[0]                    query_result = self.shangju_db.select(self.sql_6, [option, question])                    for qr in query_result:                        sub_option_id, sub_question_id, content = qr[0], qr[1], qr[2]                        sub_option_ids.append(int(sub_option_id))            infos[key] = sub_option_ids        print(infos)        return infos
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