rov-offline/alg_growth_gh_reply_video_v1.py

# -*- coding: utf-8 -*-

import pandas as pd
import traceback
import odps
from odps import ODPS
from threading import Timer
from datetime import datetime, timedelta
from db_helper import MysqlHelper
from my_utils import check_table_partition_exits_v2, get_dataframe_from_odps, \
    get_odps_df_of_max_partition, get_odps_instance, get_odps_df_of_recent_partitions
from my_utils import request_post, send_msg_to_feishu
from my_config import set_config
import numpy as np
from log import Log
import os

CONFIG, _ = set_config()
LOGGER = Log()

BASE_GROUP_NAME = 'stg0909-base'
EXPLORE1_GROUP_NAME = 'stg0909-explore1'
EXPLORE2_GROUP_NAME = 'stg0909-explore2'
#TODO: fetch gh_id from external data source
GH_IDS = ('gh_ac43e43b253b', 'gh_93e00e187787', 'gh_77f36c109fb1', 'gh_68e7fdc09fe4')
CDN_IMG_OPERATOR = "?x-oss-process=image/resize,m_fill,w_600,h_480,limit_0/format,jpg/watermark,image_eXNoL3BpYy93YXRlcm1hcmtlci9pY29uX3BsYXlfd2hpdGUucG5nP3gtb3NzLXByb2Nlc3M9aW1hZ2UvcmVzaXplLHdfMTQ0,g_center"

ODS_PROJECT = "loghubods"
EXPLORE_POOL_TABLE = 'alg_growth_video_return_stats_history'
GH_REPLY_STATS_TABLE = 'alg_growth_gh_reply_video_stats'
ODPS_RANK_RESULT_TABLE = 'alg_gh_autoreply_video_rank_data'
RDS_RANK_RESULT_TABLE = 'alg_gh_autoreply_video_rank_data'
STATS_PERIOD_DAYS = 3

def check_data_partition(project, table, data_dt, data_hr=None):
    """检查数据是否准备好"""
    try:
        partition_spec = {'dt': data_dt}
        if data_hr:
            partition_spec['hour'] = data_hr
        part_exist, data_count = check_table_partition_exits_v2(
            project, table, partition_spec)
    except Exception as e:
        data_count = 0
    return data_count

def rank_for_layer1(run_dt, run_hour, project, table):
    # TODO: 加审核&退场
    df = get_odps_df_of_max_partition(project, table, {'dt': run_dt})
    df = df.to_pandas()
    # 确保重跑时可获得一致结果
    dt_version = f'{run_dt}{run_hour}'
    np.random.seed(int(dt_version)+1)

    # TODO: 修改权重计算策略
    sample_weights = df['rov']

    sampled_df = df.sample(n=2, weights=sample_weights)
    sampled_df['sort'] = range(1, len(sampled_df) + 1)
    sampled_df['strategy_key'] = EXPLORE1_GROUP_NAME
    sampled_df['dt_version'] = dt_version

    gh_name_df = pd.DataFrame({'gh_id': GH_IDS + ('default', )})
    sampled_df['_tmpkey'] = 1
    gh_name_df['_tmpkey'] = 1
    extend_df = sampled_df.merge(gh_name_df, on='_tmpkey').drop('_tmpkey', axis=1)

    result_df = extend_df[['strategy_key', 'dt_version', 'gh_id', 'sort', 'video_id']]
    return result_df

def rank_for_layer2(run_dt, run_hour, project, table):
    df = get_odps_df_of_recent_partitions(project, table, STATS_PERIOD_DAYS, {'dt': run_dt})
    df = df.to_pandas()
    df['video_id'] = df['video_id'].astype('int64')
    df = df[['gh_id', 'video_id', 'send_count', 'first_visit_uv', 'day0_return']]
    df = df.groupby(['video_id', 'gh_id']).agg({
        'send_count': 'sum',
        'first_visit_uv': 'sum',
        'day0_return': 'sum'
    }).reset_index()

    # 确保重跑时可获得一致结果
    dt_version = f'{run_dt}{run_hour}'
    np.random.seed(int(dt_version)+1)
    # TODO: 计算账号间相关性
    ## 账号两两组合，取有RoVn数值视频的交集，单个账号内的RoVn（平滑后）组成向量
    ## 求向量相关系数或cosine相似度
    ## 单个视频的RoVn加权求和
    # 当前实现基础版本：只在账号内求二级探索排序分

    sampled_dfs = []
    # 处理default逻辑（default-explore2）
    default_stats_df = df \
        .groupby('video_id') \
        .agg({'send_count': 'sum',
              'first_visit_uv': 'sum',
              'day0_return': 'sum'}) \
        .reset_index()
    default_stats_df['gh_id'] = 'default'
    default_stats_df['score'] = default_stats_df['day0_return'] / (default_stats_df['first_visit_uv'] + 1000)
    sampled_df = default_stats_df.sample(n=2, weights=default_stats_df['score'])
    sampled_df['sort'] = range(1, len(sampled_df) + 1)
    sampled_dfs.append(sampled_df)

    # 基础过滤for账号
    df = df.query('day0_return > 100')
    # TODO: fetch send_count
    # TODO: 个数不足时的兜底逻辑
    df['score'] = df['day0_return'] / (df['first_visit_uv'] + 1000)
    for gh_id in GH_IDS:
        sub_df = df.query(f'gh_id == "{gh_id}"')
        sampled_df = sub_df.sample(n=2, weights=sub_df['score'])
        sampled_df['sort'] = range(1, len(sampled_df) + 1)
        sampled_dfs.append(sampled_df)
        if len(sampled_df) != 2:
            raise

    extend_df = pd.concat(sampled_dfs)
    extend_df['strategy_key'] = EXPLORE2_GROUP_NAME
    extend_df['dt_version'] = dt_version
    result_df = extend_df[['strategy_key', 'dt_version', 'gh_id', 'sort', 'video_id']]
    return result_df

def rank_for_base(run_dt, run_hour, project, stats_table, rank_table):
    #TODO: support to set base manually
    dt_version = f'{run_dt}{run_hour}'

    # 获取当前base信息, 策略表dt_version(ctime partition)采用当前时间
    strategy_df = get_odps_df_of_max_partition(
        project, rank_table, { 'ctime': dt_version }
    ).to_pandas()
    base_strategy_df = strategy_df.query('strategy_key.str.contains("base")')
    base_strategy_df = base_strategy_df[['gh_id', 'video_id', 'strategy_key']].drop_duplicates()

    # 获取多天即转统计数据，聚合
    stats_df = get_odps_df_of_recent_partitions(
        project, stats_table, STATS_PERIOD_DAYS, {'dt': run_dt}
    ).to_pandas()
    stats_df['video_id'] = stats_df['video_id'].astype('int64')
    stats_df = stats_df[['gh_id', 'video_id', 'send_count', 'first_visit_uv', 'day0_return']]
    stats_df = stats_df.groupby(['video_id', 'gh_id']).agg({
        'send_count': 'sum',
        'first_visit_uv': 'sum',
        'day0_return': 'sum'
    }).reset_index()

    # 聚合所有数据作为新号base利用数据(default-base)
    default_stats_df = stats_df \
        .groupby('video_id') \
        .agg({'send_count': 'sum',
              'first_visit_uv': 'sum',
              'day0_return': 'sum'}) \
        .reset_index()
    default_stats_df['gh_id'] = 'default'

    # 在账号内排序，决定该账号（包括default）的base利用内容
    # 排序过程中，确保当前base策略参与排序，因此先关联再过滤
    gh_ids_str = ','.join(f'"{x}"' for x in GH_IDS)
    stats_df = stats_df.query(f'gh_id in ({gh_ids_str})')

    stats_with_strategy_df = stats_df \
        .merge(
            base_strategy_df,
            on=['gh_id', 'video_id'],
            how='left') \
        .query('strategy_key.notna() or day0_return > 100')

    # 合并default和分账号数据
    grouped_stats_df = pd.concat([default_stats_df, stats_with_strategy_df]).reset_index()

    grouped_stats_df['score'] = grouped_stats_df['day0_return'] / (grouped_stats_df['first_visit_uv'] + 1000)
    def set_top_n(group, n=2):
        group_sorted = group.sort_values(by='score', ascending=False)
        top_n = group_sorted.head(n)
        top_n['sort'] = range(1, n + 1)
        return top_n
    ranked_df = grouped_stats_df.groupby('gh_id').apply(set_top_n)
    ranked_df = ranked_df.reset_index(drop=True)
    #ranked_df['sort'] = grouped_stats_df.groupby('gh_id')['score'].rank(ascending=False)
    ranked_df['strategy_key'] = BASE_GROUP_NAME
    ranked_df['dt_version'] = dt_version
    ranked_df = ranked_df[['strategy_key', 'dt_version', 'gh_id', 'sort', 'video_id']]
    return ranked_df


def build_and_transfer_data(run_dt, run_hour, project):
    dt_version = f'{run_dt}{run_hour}'

    layer1_rank = rank_for_layer1(run_dt, run_hour, ODS_PROJECT, EXPLORE_POOL_TABLE)
    layer2_rank = rank_for_layer2(run_dt, run_hour, ODS_PROJECT, GH_REPLY_STATS_TABLE)
    base_rank = rank_for_base(run_dt, run_hour, ODS_PROJECT,
                              GH_REPLY_STATS_TABLE, ODPS_RANK_RESULT_TABLE)
    final_rank_df = pd.concat([layer1_rank, layer2_rank, base_rank]).reset_index(drop=True)

    odps_instance = get_odps_instance(project)
    odps_ranked_df = odps.DataFrame(final_rank_df)

    video_df = get_dataframe_from_odps('videoods', 'wx_video')
    video_df['cover_url'] = video_df['cover_img_path'] + CDN_IMG_OPERATOR
    video_df = video_df['id', 'title', 'cover_url']
    final_df = odps_ranked_df.join(video_df, on=('video_id', 'id'))

    final_df = final_df.to_pandas()
    final_df = final_df[['strategy_key', 'dt_version', 'gh_id', 'sort', 'video_id', 'title', 'cover_url']]

    # save to ODPS
    t = odps_instance.get_table(ODPS_RANK_RESULT_TABLE)
    part_spec_dict = {'dt': run_dt, 'hour': run_hour, 'ctime': dt_version}
    part_spec =','.join(['{}={}'.format(k, part_spec_dict[k]) for k in part_spec_dict.keys()])
    with t.open_writer(partition=part_spec, create_partition=True, overwrite=True) as writer:
        writer.write(list(final_df.itertuples(index=False)))

    # sync to MySQL
    data_to_insert = [tuple(row) for row in final_df.itertuples(index=False)]
    data_columns = list(final_df.columns)
    mysql = MysqlHelper(CONFIG.MYSQL_CRAWLER_INFO)
    mysql.batch_insert(RDS_RANK_RESULT_TABLE, data_to_insert, data_columns)


def main_loop():
    try:
        now_date = datetime.today()
        LOGGER.info(f"开始执行: {datetime.strftime(now_date, '%Y-%m-%d %H:%M')}")
        now_hour = now_date.strftime("%H")

        last_date = now_date - timedelta(1)
        last_dt = last_date.strftime("%Y%m%d")
        # 查看当前天级更新的数据是否已准备好
        # 当前上游统计表为天级更新，但字段设计为兼容小时级
        h_data_count = check_data_partition(ODS_PROJECT, GH_REPLY_STATS_TABLE, last_dt, '00')
        if h_data_count > 0:
            LOGGER.info('上游数据表查询数据条数={}，开始计算'.format(h_data_count))
            run_dt = now_date.strftime("%Y%m%d")
            LOGGER.info(f'run_dt: {run_dt}, run_hour: {now_hour}')
            build_and_transfer_data(run_dt, now_hour, ODS_PROJECT)
            LOGGER.info('数据更新完成')
        else:
            LOGGER.info("上游数据未就绪，等待60s")
            Timer(60, main_loop).start()
        return
    except Exception as e:
        LOGGER.error(f"数据更新失败, exception: {e}, traceback: {traceback.format_exc()}")
        if CONFIG.ENV_TEXT == '开发环境':
            return
        return  # 暂时不发送报警
        send_msg_to_feishu(
            webhook=CONFIG.FEISHU_ROBOT['server_robot'].get('webhook'),
            key_word=CONFIG.FEISHU_ROBOT['server_robot'].get('key_word'),
            msg_text=f"rov-offline{CONFIG.ENV_TEXT} - 数据更新失败\n"
                     f"exception: {e}\n"
                     f"traceback: {traceback.format_exc()}"
        )


if __name__ == '__main__':
    LOGGER.info("%s 开始执行" % os.path.basename(__file__))
    LOGGER.info(f"environment: {CONFIG.ENV_TEXT}")
    main_loop()