alg/data_process/process_data_for_lightgbm.py

import datetime
import sys
import os
import argparse
import numpy as np

from tqdm import tqdm
import jieba.analyse
import pandas as pd

sys.path.append(os.getcwd())

from functions import MySQLClientSpider


class SpiderProcess(object):
    """
    Spider Data Process and Process data for lightgbm training
    """

    def __init__(self):
        self.client_spider = MySQLClientSpider()
        self.spider_features = [
            "channel",
            "out_user_id",
            "mode",
            "out_play_cnt",
            "out_like_cnt",
            "out_share_cnt"
        ]

    def spider_lop(self, video_id):
        """
        Spider lop = like / play
        :param video_id:
        :return:
        """
        sql = f"""SELECT like_cnt, play_cnt, duration from crawler_video where video_id = '{video_id}';"""
        try:
            like_cnt, play_cnt, duration = self.client_spider.select(sql)[0]
            lop = (like_cnt + 700) / (play_cnt + 18000)
            return lop, duration
        except Exception as e:
            print(video_id, "\t", e)
            return 0, 0

    def spider_data_produce(self, flag, dt_time):
        """
        从数据库中读取爬虫数据，并且转化为 dataframe， 存储到本地，用作训练和预测
        :return:
        """
        dt_time = datetime.datetime.strptime(dt_time, "%Y%m%d")
        three_date_before = dt_time + datetime.timedelta(days=4)
        temp_time = three_date_before.strftime("%Y%m%d")
        if flag == "train":
            select_sql = f"""SELECT video_id, video_title, label, channel, out_user_id, spider_mode, out_play_cnt, out_like_cnt, out_share_cnt FROM lightgbm_data WHERE type = 'spider' and daily_dt_str <= '{temp_time}' order by daily_dt_str;"""
            des_path = "/root/luojunhui/alg/data/train_data/spider_train_{}.json".format(
                datetime.datetime.today().strftime("%Y%m%d"))
        elif flag == "predict":
            select_sql = f"""SELECT video_id, video_title, label, channel, out_user_id, spider_mode, out_play_cnt, out_like_cnt, out_share_cnt FROM lightgbm_data WHERE type = 'spider' and daily_dt_str = '{temp_time}';"""
            des_path = "/root/luojunhui/alg/data/predict_data/predict_{}.json".format(dt_time.strftime("%Y%m%d"))
        else:
            return
        data_list = self.client_spider.select(select_sql)
        df = []
        for line in tqdm(data_list):
            try:
                temp = list(line)
                video_id = line[0]
                title = line[1]
                lop, duration = self.spider_lop(video_id)
                title_tags = list(jieba.analyse.textrank(title, topK=3))
                temp.append(lop)
                temp.append(duration)
                if title_tags:
                    for i in range(3):
                        try:
                            temp.append(title_tags[i])
                        except:
                            temp.append(None)
                else:
                    temp.append(None)
                    temp.append(None)
                    temp.append(None)
                df.append(temp)
            except:
                continue
        df = pd.DataFrame(df,
                          columns=['video_id', 'video_title', 'label', 'channel', 'out_user_id', 'mode', 'out_play_cnt',
                                   'out_like_cnt',
                                   'out_share_cnt', 'lop', 'duration', 'tag1', 'tag2', 'tag3'])

        df.to_json(des_path, orient='records')


class UserProcess(object):
    """
    User Data Process
    """

    def __init__(self):
        self.client_spider = MySQLClientSpider()
        self.user_features = [
            "label",
            "uid",
            "channel",
            "user_fans",
            "user_view_30",
            "user_share_30",
            "user_return_30",
            "user_rov",
            "user_str",
            "user_return_videos_30",
            "user_return_videos_3",
            "user_return_3",
            "user_view_3",
            "user_share_3",
            "address",
            "tag1",
            "tag2",
            "tag3"
        ]

    def generate_user_data(self, flag, dt_time):
        """
        生成user训练数据
        :return:
        """
        dt_time = datetime.datetime.strptime(dt_time, "%Y%m%d")
        three_date_before = dt_time + datetime.timedelta(days=4)
        temp_time = three_date_before.strftime("%Y%m%d")
        if flag == "train":
            sql = f"""select video_title, label, user_id, channel, user_fans, user_view_30, user_share_30, user_return_30, user_rov, user_str, user_return_videos_30, user_return_videos_3, user_return_3, user_view_3, user_share_3, address from lightgbm_data where type = 'userupload' and daily_dt_str <= '{temp_time}';"""
            des_path = "/root/luojunhui/alg/data/train_data/user_train_{}.json".format(
                datetime.datetime.today().strftime("%Y%m%d"))

        elif flag == "predict":
            sql = f"""select video_title, label, user_id, channel, user_fans, user_view_30, user_share_30, user_return_30, user_rov, user_str, user_return_videos_30, user_return_videos_3, user_return_3, user_view_3, user_share_3, address from lightgbm_data where type = 'userupload' and daily_dt_str = '{temp_time}';"""
            des_path = "/root/luojunhui/alg/data/predict_data/user_predict_{}.json".format(dt_time.strftime("%Y%m%d"))
        else:
            return
        dt_list = self.client_spider.select(sql)
        df = []
        for line in tqdm(dt_list):
            title = line[0]
            temp = list(line)
            title_tags = list(jieba.analyse.textrank(title, topK=3))
            if title_tags:
                for i in range(3):
                    try:
                        temp.append(title_tags[i])
                    except:
                        temp.append(None)
            else:
                temp.append(None)
                temp.append(None)
                temp.append(None)

            df.append(temp[1:])
        df = pd.DataFrame(df, columns=self.user_features)
        df['ros_30'] = np.where(df['user_view_30'] != 0, df['user_return_30'] / df['user_share_30'], np.nan)
        df['rov_30'] = np.where(df['user_view_30'] != 0, df['user_return_30'] / df['user_view_30'], np.nan)
        df['ros_3'] = np.where(df['user_view_3'] != 0, df['user_return_3'] / df['user_share_3'], np.nan)
        df['rov_3'] = np.where(df['user_view_3'] != 0, df['user_return_3'] / df['user_view_3'], np.nan)
        df.to_json(des_path, orient='records')


if __name__ == '__main__':
    parser = argparse.ArgumentParser()  # 新建参数解释器对象
    parser.add_argument("--cate")
    parser.add_argument("--flag")
    parser.add_argument("--dt")
    args = parser.parse_args()
    cate = args.cate
    flag = args.flag
    dt = args.dt
    match cate:
        case "spider":
            S = SpiderProcess()
            S.spider_data_produce(flag=flag, dt_time=dt)
        case "user_info":
            U = UserProcess()
            U.generate_user_data(flag=flag, dt_time=dt)