user_preference_predict/example_data.py

import pandas as pd
from common.log_utils import logFactory
from common import constant
from common import value_dict
from common.database_utils import database_util
from tqdm import tqdm
from sklearn.preprocessing import OneHotEncoder
import numpy as np
import math

click_client = database_util.get_client()

logger = logFactory("preprocess data").log

column_names = [
    "THEMONTH_V",
    "uuid",
    "TAG_AREA_C",
    "EVENT_CPNAME_C",
    "EVENT_SPNAME_C",
    "EVENT_CATEGORYNAME_C",
    "EVENT_CHANNEL_C",
    "EVENT_CHANNEL_BELONGTO_C",
    "EVENT_ORDER_MONTH_C",
    "EVENT_ORDER_MONTH_V",
    "EVENT_CANCEL_DIFF_C",
    "EVENT_CANCEL_DIFF_V",
    "EVENT_ORDER_PRICE_C",
    "EVENT_ORDER_PRICE_V",
    "EVENT_ORDER_FLOWP_C",
    "EVENT_ORDER_FLOWP_V",
    "EVENT_ORDER_STATE_C",
    "EVENT_ORDER_SUMMONTH_C",
    "EVENT_ORDER_SUMMONTH_V",
    "TAG_INTIME_C",
    "TAG_INTIME_V",
    "TAG_PACKAGE_C",
    "TAG_PACKAGE_PIC_C",
    "EVENT_FLUX_C",
    "EVENT_FLUX_V",
    "EVENT_CONSUM_C",
    "EVENT_CONSUM_V",
    "EVENT_PHONETYPE_C",
    "EVENT_VIDEO_FLUX_C",
    "EVENT_VIDEO_FLUX_V",
    "EVENT_IS_ACCT_C",
    "TAG_GENDER_C",
    "TAG_PROVINCE_C",
    "TAG_NETTYPE_C",
    "TAG_AGE_C",
    "EVENT_APP_USE.C",
    "EVENT_APP_USE.V",
    "EVENT_USER_OSTATE_C"
]

drop_columns = [
    "THEMONTH_V",
    "uuid",
    "EVENT_CPNAME_C",
    "EVENT_SPNAME_C",
    "EVENT_CATEGORYNAME_C",
    "EVENT_CHANNEL_C",
    "EVENT_CHANNEL_BELONGTO_C",
    "EVENT_ORDER_MONTH_C",
    "EVENT_ORDER_MONTH_V",
    "EVENT_CANCEL_DIFF_C",
    "EVENT_CANCEL_DIFF_V",
    "EVENT_ORDER_PRICE_C",
    "EVENT_ORDER_PRICE_V",
    "EVENT_ORDER_FLOWP_C",
    "EVENT_ORDER_FLOWP_V",
    "EVENT_ORDER_STATE_C",
    "EVENT_ORDER_SUMMONTH_C",
    "EVENT_ORDER_SUMMONTH_V",
    "TAG_INTIME_V",
    "TAG_PACKAGE_C",
    "TAG_PACKAGE_PIC_C",
    "EVENT_PHONETYPE_C",
    "TAG_AREA_C",
    "EVENT_APP_USE.V",
    "I_appuse"
]

fetch_size = 50000


def convert_product_type(value):
    if value == "无限畅视":
        value = 10
    elif value == "电商卡":
        value = 9
    elif value == "畅视后向":
        value = 8
    elif value == "畅视流量":
        value = 7
    elif value == "沃畅视":
        value = 6
    elif value == "畅视随心选":
        value = 5
    elif value == "沃视频":
        value = 4
    elif value == "小娱八斗":
        value = 3
    elif value == "畅视互娱":
        value = 2
    elif value == "其它":
        value = 1
    else:
        value = 0
    return value


# 渠道大类
def convert_channel(value):
    if value == '互联网渠道':
        value = 6
    elif value == '集团渠道':
        value = 5
    elif value == '自有线上渠道':
        value = 4
    elif value == '省分线下渠道':
        value = 3
    elif value == '社会代理和其他渠道':
        value = 2
    elif value == '省分线上渠道':
        value = 1
    else:
        value = 0

    return value


# 订购月份
def convert_order_month(value):
    if value == '订购1月内':
        value = 1
    elif value == '1-3月':
        value = 2
    elif value == '3-6月':
        value = 3
    elif value == '6-12月':
        value = 4
    elif value == '12-24月':
        value = 5
    elif value == '24月以上':
        value = 6
    else:
        value = 0
    return value


# 退订距今时长
def convert_cancel_diff(value):
    if value == '退订1月内':
        value = 1
    elif value == '退订1-3月':
        value = 2
    elif value == '退订3-6月':
        value = 3
    elif value == '退订6-12月':
        value = 4
    elif value == '退订1年以上':
        value = 5
    elif value == '未退订':
        value = 6
    else:
        value = 0
    return value


# 订购价格
def convert_order_price(value):
    if value == '0元':
        value = 1
    elif value == '1-3元':
        value = 2
    elif value == '3-8元':
        value = 3
    elif value == '8-15元':
        value = 4
    elif value == '15-20元':
        value = 5
    elif value == '20以上':
        value = 6
    else:
        value = 0
    return value


# 流量使用比
def convert_order_flow(value):
    if str(value) in ['5%以下']:
        value = 1
    elif value == '5-20%':
        value = 2
    elif value == '20-50%':
        value = 3
    elif value == '50-80%':
        value = 4
    elif value == '80%以上':
        value = 5
    else:
        value = 0
    return value


# 产品订购总时长
def convert_order_sum_month(value):
    if value == '0-1月':
        value = 1
    elif value == '2-6月':
        value = 2
    elif value == '6-12月':
        value = 3
    elif value == '12-24月':
        value = 4
    elif value == '24月以上':
        value = 5
    else:
        value = 0
    return value


# 入网时长
def convert_intime(value):
    if value == '1年-2年':
        value = 1
    elif value == '2年-5年':
        value = 2
    elif value == '5年-10年':
        value = 3
    elif value == '10年-20年':
        value = 4
    elif value == '20年以上':
        value = 5
    else:
        value = 0
    return value


# 当月使用流量
def convert_flux(value):
    if value == '0-0.5G':
        value = 2
    elif value == '0.5G-2G':
        value = 3
    elif value == '2G-5G':
        value = 4
    elif value == '5G-10G':
        value = 5
    elif value == '10G-15G':
        value = 6
    elif value == '15-30G':
        value = 7
    elif value == '30G以上':
        value = 8
    elif value == '小于等于0':
        value = 1
    else:
        value = 0
    return value


# 是否出账
def convert_acct(value):
    if value == '已出账':
        value = 0
    elif value == '未出账':
        value = 1
    else:
        value = 2
    return value


# 月消费
def convert_consume(value):
    if value == '10元以下':
        value = 1
    elif value == '10-20元':
        value = 2
    elif value == '20-60元':
        value = 3
    elif value == '60-100元':
        value = 4
    elif value == '100-150元':
        value = 5
    elif value == '150-300元':
        value = 6
    elif value == '300元以上':
        value = 7
    else:
        value = 0
    return value


def convert_flux_value(value):
    if value is None or str(value) == "nan":
        value = 0
    elif not constant.is_number(value):
        value = 0
    return value


def convert_consume_value(value):
    if value is None or str(value) == "nan":
        value = 0
    elif not constant.is_number(value):
        value = 0
    return value


def convert_video_flux_value(value):
    if value is None or str(value) == "nan":
        value = 0
    elif not constant.is_number(value):
        value = 0
    return value


# 月视频使用流量
def convert_video_flux(value):
    if value == '0-0.5G':
        value = 1
    elif value == '0.5-2G':
        value = 2
    elif value == '2-5G':
        value = 3
    elif value == '5-10G':
        value = 4
    elif value == '10-15G':
        value = 5
    elif value == '15-30G':
        value = 6
    elif value == '30G以上':
        value = 7
    else:
        value = 0
    return value


# 性别
def convert_gender(value):
    if value == '男':
        value = 0
    elif value == '女':
        value = 1
    else:
        value = 2
    return value


# 网络类型
def convert_nettype(value):
    if value == '2G':
        value = 1
    elif value == '3G':
        value = 2
    elif value == '4G':
        value = 3
    elif value == '5G':
        value = 4
    else:
        value = 0
    return value


# 年龄
def convert_age(value):
    if value == '70前':
        value = 1
    elif value == '70后':
        value = 2
    elif value == '80后':
        value = 3
    elif value == '90后':
        value = 4
    elif value == '00后':
        value = 5
    else:
        value = 0
    return value


def get_range_value(range_value):
    if range_value == '0.1G-0.5G':
        value = 1
    elif range_value == '0.5G-2G':
        value = 2
    elif range_value == '2G-5G':
        value = 3
    elif range_value == '5G-10G':
        value = 4
    elif range_value == '10G-20G':
        value = 5
    elif range_value == '20G以上':
        value = 6
    else:
        value = 0
    return value


def convert_app_use_info(value):
    value = value.tolist()[-2]
    tencent_app_use = 7
    kuaishou_app_use = 7
    mangguo_app_use = 7
    youku_app_use = 7
    iqiyi_app_use = 7
    bilibili_app_use = 7
    if str(value).lower() == 'nan':
        pass
    else:
        use_list = value
        for apptype_range in use_list:
            if len(apptype_range.split("_")) > 1:
                apptype = apptype_range.split("_")[0]
                apprange = apptype_range.split("_")[1]
                if "腾讯视频" in apptype:
                    tencent_app_use = get_range_value(apprange)
                if "哔哩哔哩" in apptype:
                    bilibili_app_use = get_range_value(apprange)
                if "爱奇艺" in apptype:
                    iqiyi_app_use = get_range_value(apprange)
                if "快手" in apptype:
                    kuaishou_app_use = get_range_value(apprange)
                if "优酷" in apptype:
                    youku_app_use = get_range_value(apprange)
                if "芒果" in apptype:
                    mangguo_app_use = get_range_value(apprange)
    return int(tencent_app_use), int(mangguo_app_use), int(youku_app_use), int(iqiyi_app_use), int(
        bilibili_app_use), int(kuaishou_app_use)


# APP使用情况
def convert_appuse(df):
    app_use_columns = ['app_use_tencent', 'app_use_mangguo', 'app_use_youku', 'app_use_iqiyi', 'app_use_bilibili',
                       'app_use_kuaishou']
    df[app_use_columns] = df.apply(convert_app_use_info, axis=1, result_type="expand")


def convert_province(value):
    return value_dict.area_dict[value]


def convert_label(data):
    logger.info("开始预处理")
    # data['EVENT_CATEGORYNAME_C'] = data['EVENT_CATEGORYNAME_C'].map(convert_product_type)
    # data['EVENT_CATEGORYNAME_C'] = data['EVENT_CATEGORYNAME_C'].astype('int')
    # data['EVENT_CHANNEL_BELONGTO_C'] = data['EVENT_CHANNEL_BELONGTO_C'].map(convert_channel)
    # data['EVENT_CHANNEL_BELONGTO_C'] = data['EVENT_CHANNEL_BELONGTO_C'].astype('int')
    # data['EVENT_ORDER_MONTH_C'] = data['EVENT_ORDER_MONTH_C'].map(convert_order_month)
    # data['EVENT_CANCEL_DIFF_C'] = data['EVENT_CANCEL_DIFF_C'].map(convert_cancel_diff)
    # data['EVENT_CANCEL_DIFF_C'] = data['EVENT_CANCEL_DIFF_C'].astype('int')
    # data['EVENT_ORDER_PRICE_C'] = data['EVENT_ORDER_PRICE_C'].map(convert_order_price)
    # data['EVENT_ORDER_FLOWP_C'] = data['EVENT_ORDER_FLOWP_C'].map(convert_order_flow)
    # data['EVENT_ORDER_SUMMONTH_C'] = data['EVENT_ORDER_SUMMONTH_C'].map(convert_order_sum_month)
    data['TAG_PROVINCE_C'] = data['TAG_PROVINCE_C'].map(convert_province)
    data['TAG_INTIME_C'] = data['TAG_INTIME_C'].map(convert_intime)
    data['TAG_INTIME_C'] = data['TAG_INTIME_C'].astype('int')
    data['EVENT_FLUX_C'] = data['EVENT_FLUX_C'].map(convert_flux)
    data['EVENT_FLUX_V'] = data['EVENT_FLUX_V'].map(convert_flux_value)
    data['EVENT_FLUX_V'] = data['EVENT_FLUX_V'].astype('float')
    data['EVENT_FLUX_C'] = data['EVENT_FLUX_C'].astype('int')
    data['EVENT_CONSUM_C'] = data['EVENT_CONSUM_C'].map(convert_consume)
    data['EVENT_CONSUM_V'] = data['EVENT_CONSUM_V'].map(convert_consume_value)
    data['EVENT_CONSUM_C'] = data['EVENT_CONSUM_C'].astype('int')
    data['EVENT_CONSUM_V'] = data['EVENT_CONSUM_V'].astype('float')
    data['EVENT_VIDEO_FLUX_C'] = data['EVENT_VIDEO_FLUX_C'].map(convert_video_flux)
    data['EVENT_VIDEO_FLUX_V'] = data['EVENT_VIDEO_FLUX_V'].map(convert_video_flux_value)
    data['EVENT_VIDEO_FLUX_V'] = data['EVENT_VIDEO_FLUX_V'].astype('float')
    data['EVENT_VIDEO_FLUX_C'] = data['EVENT_VIDEO_FLUX_C'].astype('int')
    data['TAG_GENDER_C'] = data['TAG_GENDER_C'].map(convert_gender)
    data['TAG_GENDER_C'] = data['TAG_GENDER_C'].astype('int')
    data['TAG_NETTYPE_C'] = data['TAG_NETTYPE_C'].map(convert_nettype)
    data['TAG_NETTYPE_C'] = data['TAG_NETTYPE_C'].astype('int')
    data['TAG_AGE_C'] = data['TAG_AGE_C'].map(convert_age)
    data['TAG_AGE_C'] = data['TAG_AGE_C'].astype('int')
    data['EVENT_IS_ACCT_C'] = data['EVENT_IS_ACCT_C'].map(convert_acct)
    data['EVENT_IS_ACCT_C'] = data['EVENT_IS_ACCT_C'].astype('int')
    # app使用
    convert_appuse(data)
    data.drop(columns=['EVENT_APP_USE.C'], inplace=True)
    data.drop(columns=['EVENT_USER_OSTATE_C'], inplace=True)


# 性别：0，1，2
# 网络类型: 1,2,3,4,5
np_cates = list(np.array([
    [x for x in range(0, 3)],
    [x for x in range(0, 5)],
    [x for x in range(0, 6)],
    [x for x in range(0, 6)],
    [x for x in range(0, 3)],
    [x for x in range(0, 9)],
    [x for x in range(0, 8)],
    [x for x in range(0, 8)],
    [x for x in range(0, 32)],
    [x for x in range(0, 8)],
    [x for x in range(0, 8)],
    [x for x in range(0, 8)],
    [x for x in range(0, 8)],
    [x for x in range(0, 8)],
    [x for x in range(0, 8)]
]))
enc = OneHotEncoder(categories=np_cates)
one_hot_feature_columns = [
    "TAG_GENDER_C",
    "TAG_NETTYPE_C",
    "TAG_AGE_C",
    "TAG_INTIME_C",
    "EVENT_IS_ACCT_C",
    "EVENT_FLUX_C",
    "EVENT_CONSUM_C",
    "EVENT_VIDEO_FLUX_C",
    "TAG_PROVINCE_C",
    'app_use_tencent', 'app_use_mangguo', 'app_use_youku', 'app_use_iqiyi', 'app_use_bilibili',
    'app_use_kuaishou'
]


def convert_onehot(df):
    logger.info("开始onehot编码")
    enc.fit(df[one_hot_feature_columns])
    after_onehot_features = enc.get_feature_names(one_hot_feature_columns)
    one_hot_dataframe = pd.DataFrame(enc.fit_transform(df[one_hot_feature_columns]).toarray().astype("int"),
                                     columns=after_onehot_features)
    df.drop(columns=one_hot_feature_columns, inplace=True)
    df = pd.concat([df, one_hot_dataframe], axis=1)
    return df


def insert_to_database(dataf, row_index):
    nums = str(len(dataf))
    logger.info(f"开始写入数据库，共{nums}条")
    # 增加自增列
    next_index = row_index + len(dataf)
    dataf['row_id'] = range(row_index, next_index)
    dataf['month'] = '202107'
    # tb_name = 'lu_target_data'
    tb_name = constant.insert_neg_tb_name
    cols = ",".join(dataf.columns.tolist())
    data = dataf.to_dict("records")
    click_client.execute(f"INSERT INTO {tb_name} ({cols}) VALUES ", data, types_check=True)

    return next_index


# 760
# range(0,190) row_index = 0
# range(190,380) row_index = 9500000
# range(380,570) row_index = 19000000
# range(570,760) row_index = 28500000
def get_data(row_index, range_start, range_end, part):
    logger.info("开始处理数据")
    # for_nums = int(2000000 / fetch_size)
    # for_nums = math.ceil(36700000 / fetch_size)
    # for_nums = math.ceil(38000000 / fetch_size)
    row_index = row_index
    data_all = []
    try:
        for i in tqdm(range(range_start, range_end)):
            step = str(i)
            logger.info(f"第{step}次循环开始处理")
            start = i * fetch_size
            # sql = f"select t.* from Z_USER_TAG_FLAT_out t where EVENT_CPNAME_C!='快手' and EVENT_USER_OSTATE_C!='在订' and EVENT_CONSUM_C!='10元以下' and EVENT_VIDEO_FLUX_C!='未知' and TAG_NETTYPE_C!='2G' and position(arrayStringConcat(EVENT_APP_USE.C,','),'快手')!=0 limit {start},{fetch_size} "
            sql = f"select t.* from l_neg_origin_07 t order by t.uuid limit {fetch_size} offset {start}"
            all_data = click_client.execute(sql)
            dataf = pd.DataFrame(all_data, columns=constant.origin_column_names)
            dataf.drop(columns=drop_columns, inplace=True)
            # 预处理
            convert_label(dataf)

            # onehot编码
            dataf = convert_onehot(dataf)

            # 写入数据库
            row_index = insert_to_database(dataf, row_index)
            logger.info(f"第{step}次循环处理完毕")
            data_all.append(dataf)
    except Exception as e:
        logger.error(str(e))
        data_last = pd.concat(data_all, axis=0)
        data_last.to_pickle(f"data_neg_part_{part}_0218_07.pkl")

    data_last = pd.concat(data_all, axis=0)
    data_last.to_pickle(f"data_neg_part_{part}_0218_07.pkl")


if __name__ == "__main__":
    get_data(0, 0, 27, "1")
    # get_data(9500000, 190, 285, "2")
    # get_data(14250000, 285, 380, "3")
    # get_data(9500000, 190, 380, "4")
    # get_data(19000000, 380, 475, "5")
    # get_data(19000000, 475, 570, "6")
    # get_data(28500000, 570, 760)