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- from sqlalchemy import create_engine
- from urllib import parse
- import pandas as pd
- from utils.code_dict import age_dict
- import yaml
- def update_dict(x, y):
- # 用字典y,来更新x
- # key 存在则更新,不存在则新增
- x.update(y)
- return x
- def get_db_engine(db_info):
- db_con_str = 'mysql+pymysql://%s:%s@%s:%d/%s' % \
- (db_info['username'],
- parse.quote_plus(db_info['password']),
- db_info['host'],
- db_info['port'],
- db_info['database'])
- engine = create_engine(db_con_str, connect_args={'charset': 'utf8'})
- return engine
- def is_contains_region(val1, val2):
- """
- level1
- region long[] 可选 地域 传值为[]表示不限;传递上一级ID时,childrenID可以不传;不允许同时传parentID和childrenID;
- 仅计划的campaign_type为5时,支持设置三级地域(例:山西-大同-左云,左云是三级地域)
- :param val1: 推荐的城市等级组合,如 "一线城市|新一线城市|二线城市|三线城市" , "不限"
- :param val2: 从账户配置信息表里读取的地区信息, 如 '[]', None, '[14,15,21,22,23,31,32]'
- :return:
- """
- # 如果val1为空,表示该字段没有参与定向组合运算,就直接取客户投放策略里面的值
- if val1 is None:
- return True, {'region': val2}
- val_split = val1.split('|')
- val_tuple = tuple(val_split*2) if len(val_split) == 1 else tuple(val_split)
- sql = """
- select t1.city_name, t2.level, t2.region_id, t2.parent from ctop_kuaishou_city_level t1
- left join ctop_kuaishou_region_list_parent t2
- on t1.city_name = t2.name
- where t1.city_level in %s
- """ % (val_tuple,)
- with open('/data/pythonProject/ai_target/config/config.yaml', mode='r', encoding='utf-8') as f:
- config = yaml.load(f.read(), Loader=yaml.FullLoader)
- city_df = pd.read_sql(sql, get_db_engine(config['jeecg_product_db']))
- # 如果【吉林】在city_level里面的话,在ctop_kuaishou_region_list_parent会同时把 吉林省和吉林市过滤出来
- # 按level进行降序排列后,按city_name进行去重,保留低等级的值
- city_df = city_df.sort_values(by='level', ascending=False)
- city_df.drop_duplicates(subset=['city_name'], keep='first', inplace=True)
- if not val2 or val2 == '不限' or val2 == '[]':
- return True, {'region': list(city_df['region_id'].values)}
- else:
- if val1 == '不限':
- return False, -1
- else:
- val = []
- # level1
- if set(city_df[city_df.level == 1].region_id.values).issubset(set(eval(val2))):
- val.extend(city_df[city_df.level == 1].region_id.values)
- else:
- return False, -1
- # level2
- # 元素的父级是否存在于 val2,如果存在则为包含于的关系
- # 元素本身是否存在于 val2 ,如果存在则为包含于的关系
- # 以上都不满足,val1 不包含于 val2
- for item in city_df[city_df.level == 2].region_id.values:
- parent = city_df[city_df.region_id == item].parent.values[0]
- if parent in eval(val2):
- val.append(item)
- elif item in eval(val2):
- val.append(item)
- else:
- return False, -1
- # level3
- for item in city_df[city_df.level == 3].region_id.values:
- parent = city_df[city_df.region_id == item].parent.values[0]
- if parent in eval(val2):
- val.append(item)
- elif item in eval(val2):
- val.append(item)
- else:
- return False, -1
- return True, {'region': val}
- def is_contains_gender(val1, val2):
- """
- :param val1: ctop_ai_kuaishou_signature_recommended_target_combine 中的 gender 字段: '男'、'女'、'不限'、None
- :param val2: ctop_ai_kuaishou_advertiser_strategy 中的 gender 字段: 1:女性, 2:男性,0表示不限
- :return: val1 是否为 val2的子集,以及传递给快手后台的值
- """
- if val1 is None:
- # 表示该字段没有参与贝叶斯的计算, 直接返回客户投放策略里的值
- return True, {'gender': val2}
- if val1 == '男' and val2 in (2, 0):
- return True, {'gender': 2}
- if val1 == '女' and val2 in (1, 0):
- return True, {'gender': 1}
- if val1 == '不限' and val2 == 0:
- return True, {'gender': 0}
- def is_contains_age(val1, val2_min, val2_max, val2_range):
- """
- :param val1: 推荐的年龄等级组合,如 "31-40岁|41-49岁|50+岁" , "不限"
- age struct 可选 自定义年龄段 不传值表示不限,传值具体见下方表格;与ages_range不能同时传
- min int 必填 年龄最小限制 年龄区间最小为18岁
- max int 必填 年龄最大限制 年龄区间最大为55岁,且年龄最大限制须大于等于年龄最小限制
- :param val2_min: age_min
- :param val2_max: age_max
- :param val2_range: ctop_ai_kuaishou_advertiser_strategy 中的 ages_range [] varchar
- 与age不能同时传;【18:表示18-23岁】;【24:表示24-30岁】;【31:表示31-40岁】;【41:表示41-49岁】;【50:表示50-100岁】
- :return:
- """
- # val2_range 为字符串类型,修改为 list 类型
- if val2_range == '[]':
- val2_range = None
- if val2_range:
- val2_range = eval(val2_range)
- # 如果val1为空,表示该字段没有参与定向组合运算,就直接取客户投放策略里面的值
- if val1 is None:
- return True, {'age_min': val2_min, 'age_max': val2_max, 'ages_range': val2_range}
- if val1 == '不限':
- if (not val2_min) and (not val2_max) and (not val2_range or val2_range == '不限'):
- return True, {'age_min': val2_min, 'age_max': val2_max, 'ages_range': val2_range}
- else:
- return False, -1
- else:
- val1 = [age_dict[ele] for ele in val1.split('|')]
- if (not val2_min) and (not val2_max) and (not val2_range or val2_range == '不限'):
- return True, {'ages_range': val1}
- if (val2_min is None and val2_max is None) and val2_range:
- if set(val1).issubset(set(val2_range)):
- return True, {'ages_range': val1}
- else:
- return False, -1
- if (val2_min and val2_max) and (not val2_range):
- if val1[0] >= val2_min and val1[-1] <= val2_max:
- return True, {'age_min': val1[0], 'age_max': val1[-1]}
- else:
- return False, -1
- def age_code_transform(val):
- if (val is None) or (val == '不限'):
- ages_range = None
- else:
- ages_range = [age_dict[item] for item in val.split('|')]
- return ages_range
- def gender_code_transform(val):
- if (val is None) or (val == '不限'):
- gender = 0
- elif val == '女':
- gender = 1
- else:
- gender = 2
- return gender
- def city_code_transform(val):
- if (val is None) or (val == '不限'):
- region = None
- else:
- sql = """select t1.city_name, t2.level, t2.region_id, t2.parent from ctop_kuaishou_city_level t1
- left join ctop_kuaishou_region_list_parent t2
- on t1.city_name = t2.name
- where t1.city_level in %s
- """ % (tuple(val.split('|')),)
- with open('/data/pythonProject/ai_target/config/config.yaml', mode='r', encoding='utf-8') as f:
- config = yaml.load(f.read(), Loader=yaml.FullLoader)
- city_df = pd.read_sql(sql, get_db_engine(config['jeecg_product_db']))
- # 如果【吉林】在city_level里面的话,在ctop_kuaishou_region_list_parent会同时把 吉林省和吉林市查询出来
- # 按level进行降序排列后,按city_name进行去重,保留高等级的值
- city_df = city_df.sort_values(by='level', ascending=False)
- city_df.drop_duplicates(subset=['city_name'], keep='first', inplace=True)
- region = city_df.region_id.values
- return region
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