add region h ab test

config.py

@@ -104,6 +104,15 @@ class BaseConfig(object):
         'rule2': {'cal_score_func': 2, 'return_count': 200},
     }
 
+    # 地域分组小时级规则更新使用数据
+    PROJECT_REGION = ''
+    TABLE_REGION = ''
+
+    # 地域分组小时级规则参数
+    RULE_PARAMS_REGION = {
+        'rule1': {'view_type': 'pre-view', 'return_count': 20, 'score_rule': 0.005},
+    }
+
     # 老视频更新使用数据
     OLD_VIDEOS_PROJECT = 'loghubods'
     OLD_VIDEOS_TABLE = 'xcx_test_video'
@@ -127,6 +136,14 @@ class BaseConfig(object):
     # 完整格式：com.weiqu.video.recall.hot.item.score.dup.day.pre.{rule_key}.{date}
     RECALL_KEY_NAME_PREFIX_DUP_DAY_PRE = 'com.weiqu.video.recall.hot.item.score.dup.day.pre.'
 
+    # 小程序地域分组小时级更新结果存放 redis key前缀，完整格式：com.weiqu.video.recall.item.score.region.h.{region}.{rule_key}.{date}.{h}
+    RECALL_KEY_NAME_PREFIX_REGION_BY_H = 'com.weiqu.video.recall.item.score.region.h.'
+    # 小程序离线ROV模型结果与小程序地域分组小时级更新结果去重后 存放 redis key前缀，
+    # 完整格式：com.weiqu.video.recall.hot.item.score.dup.region.h.{region}.{rule_key}.{date}.{h}
+    RECALL_KEY_NAME_PREFIX_DUP_REGION_H = 'com.weiqu.video.recall.hot.item.score.dup.h.'
+    # 地域分组小时级视频状态不符合推荐要求的列表 redis key，完整格式：com.weiqu.video.filter.region.h.item.{region}.{rule_key}
+    REGION_H_VIDEO_FILER = 'com.weiqu.video.filter.region.h.item.'
+
     # 小程序老视频更新结果存放 redis key 前缀，完整格式：'com.weiqu.video.recall.old.item.{date}'
     RECALL_KEY_NAME_PREFIX_OLD_VIDEOS = 'com.weiqu.video.recall.old.item.'
 
@@ -434,8 +451,8 @@ class ProductionConfig(BaseConfig):
 
 def set_config():
     # 获取环境变量 ROV_OFFLINE_ENV
-    env = os.environ.get('ROV_OFFLINE_ENV')
-    # env = 'dev'
+    # env = os.environ.get('ROV_OFFLINE_ENV')
+    env = 'dev'
     if env is None:
         log_.error('ENV ERROR: is None!')
         return

region_rule_rank_h.py

@@ -0,0 +1,277 @@
+# -*- coding: utf-8 -*-
+# @ModuleName: region_rule_rank_h
+# @Author: Liqian
+# @Time: 2022/5/5 15:54
+# @Software: PyCharm
+
+import datetime
+import pandas as pd
+import math
+from odps import ODPS
+from threading import Timer
+from utils import MysqlHelper, RedisHelper, get_data_from_odps
+from config import set_config
+from log import Log
+
+config_, _ = set_config()
+log_ = Log()
+
+region_code = {
+    '河北省': '130000',
+    '山西省': '140000',
+    '辽宁省': '210000',
+    '吉林省': '220000',
+    '黑龙江省': '230000',
+    '江苏省': '320000',
+    '浙江省': '330000',
+    '安徽省': '340000',
+    '福建省': '350000',
+    '江西省': '360000',
+    '山东省': '370000',
+    '河南省': '410000',
+    '湖北省': '420000',
+    '湖南省': '430000',
+    '广东省': '440000',
+    '海南省': '460000',
+    '四川省': '510000',
+    '贵州省': '520000',
+    '云南省': '530000',
+    '陕西省': '610000',
+    '甘肃省': '620000',
+    '青海省': '630000',
+    '台湾省': '710000',
+    '北京': '110000',
+    '天津': '120000',
+    '内蒙古': '150000',
+    '上海': '310000',
+    '广西': '450000',
+    '重庆': '500000',
+    '西藏': '540000',
+    '宁夏': '640000',
+    '新疆': '650000',
+    '香港': '810000',
+    '澳门': '820000',
+}
+
+features = [
+    'videoid',
+    'lastonehour_preview',  # 过去1小时预曝光人数
+    'lastonehour_view',  # 过去1小时曝光人数
+    'lastonehour_play',  # 过去1小时播放人数
+    'lastonehour_share',  # 过去1小时分享人数
+    'lastonehour_return',  # 过去1小时分享，过去1小时回流人数
+    'lastonehour_preview_total_final',  # 过去1小时预曝光次数
+    'lastonehour_view_total_final',  # 过去1小时曝光次数
+    'lastonehour_play_total_final',  # 过去1小时播放次数
+    'lastonehour_share_total_final',  # 过去1小时分享次数
+]
+
+
+def get_region_code(region):
+    """获取省份对应的code"""
+    mysql_helper = MysqlHelper(mysql_info=config_.MYSQL_INFO)
+    sql = f"SELECT ad_code FROM region_adcode WHERE parent_id = 0 AND region LIKE '{region}%';"
+    ad_code = mysql_helper.get_data(sql=sql)
+    return ad_code[0][0]
+
+
+def h_data_check(project, table, now_date):
+    """检查数据是否准备好"""
+    odps = ODPS(
+        access_id=config_.ODPS_CONFIG['ACCESSID'],
+        secret_access_key=config_.ODPS_CONFIG['ACCESSKEY'],
+        project=project,
+        endpoint=config_.ODPS_CONFIG['ENDPOINT'],
+        connect_timeout=3000,
+        read_timeout=500000,
+        pool_maxsize=1000,
+        pool_connections=1000
+    )
+
+    try:
+        dt = datetime.datetime.strftime(now_date, '%Y%m%d%H')
+        sql = f'select * from {project}.{table} where dt = {dt}'
+        with odps.execute_sql(sql=sql).open_reader() as reader:
+            data_count = reader.count
+    except Exception as e:
+        data_count = 0
+    return data_count
+
+
+def get_rov_redis_key(now_date):
+    """获取rov模型结果存放key"""
+    redis_helper = RedisHelper()
+    now_dt = datetime.datetime.strftime(now_date, '%Y%m%d')
+    key_name = f'{config_.RECALL_KEY_NAME_PREFIX}{now_dt}'
+    if not redis_helper.key_exists(key_name=key_name):
+        pre_dt = datetime.datetime.strftime(now_date - datetime.timedelta(days=1), '%Y%m%d')
+        key_name = f'{config_.RECALL_KEY_NAME_PREFIX}{pre_dt}'
+    return key_name
+
+
+def get_feature_data(project, table, now_date):
+    """获取特征数据"""
+    dt = datetime.datetime.strftime(now_date, '%Y%m%d%H')
+    # dt = '2022041310'
+    records = get_data_from_odps(date=dt, project=project, table=table)
+    feature_data = []
+    for record in records:
+        item = {}
+        for feature_name in features:
+            item[feature_name] = record[feature_name]
+        feature_data.append(item)
+    feature_df = pd.DataFrame(feature_data)
+    return feature_df
+
+
+def cal_score(df):
+    """
+    计算score
+    :param df: 特征数据
+    :return:
+    """
+    # score计算公式: sharerate*backrate*logback*ctr
+    # sharerate = lastonehour_share/(lastonehour_play+1000)
+    # backrate = lastonehour_return/(lastonehour_share+10)
+    # ctr = lastonehour_play/(lastonehour_preview+1000), 对ctr限最大值：K2 = 0.6 if ctr > 0.6 else ctr
+    # score = sharerate * backrate * LOG(lastonehour_return+1) * K2
+
+    df = df.fillna(0)
+    df['share_rate'] = df['lastonehour_share'] / (df['lastonehour_play'] + 1000)
+    df['back_rate'] = df['lastonehour_return'] / (df['lastonehour_share'] + 10)
+    df['log_back'] = (df['lastonehour_return'] + 1).apply(math.log)
+    df['ctr'] = df['lastonehour_play'] / (df['lastonehour_preview'] + 1000)
+    df['K2'] = df['ctr'].apply(lambda x: 0.6 if x > 0.6 else x)
+    df['score'] = df['share_rate'] * df['back_rate'] * df['log_back'] * df['K2']
+    df = df.sort_values(by=['score'], ascending=False)
+    return df
+
+
+def video_rank(df, now_date, now_h, rule_key, param, region):
+    """
+    获取符合进入召回源条件的视频，与每日更新的rov模型结果视频列表进行合并
+    :param df:
+    :param now_date:
+    :param now_h:
+    :param rule_key: 小时级数据进入条件
+    :param param: 小时级数据进入条件参数
+    :param region: 所属地域
+    :return:
+    """
+    # 获取rov模型结果
+    redis_helper = RedisHelper()
+    key_name = get_rov_redis_key(now_date=now_date)
+    initial_data = redis_helper.get_data_zset_with_index(key_name=key_name, start=0, end=-1, with_scores=True)
+    log_.info(f'initial data count = {len(initial_data)}')
+
+    # 获取符合进入召回源条件的视频，进入条件：小时级回流>=20 && score>=0.005
+    return_count = param.get('return_count')
+    score_value = param.get('score_rule')
+    h_recall_df = df[(df['lastonehour_return'] >= return_count) & (df['score'] >= score_value)]
+    h_recall_videos = h_recall_df['videoid'].to_list()
+    log_.info(f'h_recall videos count = {len(h_recall_videos)}')
+    # 写入对应的redis
+    h_video_ids =[]
+    h_recall_result = {}
+    for video_id in h_recall_videos:
+        score = h_recall_df[h_recall_df['videoid'] == video_id]['score']
+        h_recall_result[int(video_id)] = float(score)
+        h_video_ids.append(int(video_id))
+    h_recall_key_name = \
+        f"{config_.RECALL_KEY_NAME_PREFIX_REGION_BY_H}{region}.{rule_key}.{datetime.datetime.strftime(now_date, '%Y%m%d')}.{now_h}"
+    if len(h_recall_result) > 0:
+        redis_helper.add_data_with_zset(key_name=h_recall_key_name, data=h_recall_result, expire_time=23 * 3600)
+        # 清空线上过滤应用列表
+        redis_helper.del_keys(key_name=f"{config_.REGION_H_VIDEO_FILER}{region}.{rule_key}")
+
+    # 去重更新rov模型结果，并另存为redis中
+    initial_data_dup = {}
+    for video_id, score in initial_data:
+        if int(video_id) not in h_video_ids:
+            initial_data_dup[int(video_id)] = score
+    log_.info(f"initial data dup count = {len(initial_data_dup)}")
+    initial_key_name = \
+        f"{config_.RECALL_KEY_NAME_PREFIX_DUP_REGION_H}{region}.{rule_key}.{datetime.datetime.strftime(now_date, '%Y%m%d')}.{now_h}"
+    if len(initial_data_dup) > 0:
+        redis_helper.add_data_with_zset(key_name=initial_key_name, data=initial_data_dup, expire_time=23 * 3600)
+
+
+def rank_by_h(project, table, now_date, now_h, rule_params):
+    # 获取特征数据
+    feature_df = get_feature_data(project=project, table=table, now_date=now_date)
+    # 获取所有的region
+    region_list = list(set(feature_df[''].to_list()))
+    # rank
+    for key, value in rule_params.items():
+        log_.info(f"rule = {key}, param = {value}")
+        for region in region_list:
+            log_.info(f"region = {region}")
+            # 计算score
+            score_df = cal_score(df=feature_df)
+            video_rank(df=score_df, now_date=now_date, now_h=now_h, rule_key=key, param=value, region=region)
+            # to-csv
+            score_filename = f"score_{region}_{key}_{datetime.datetime.strftime(now_date, '%Y%m%d%H')}.csv"
+            score_df.to_csv(f'./data/{score_filename}')
+
+
+def h_rank_bottom(now_date, now_h, rule_key, project, table):
+    """未按时更新数据，用上一小时结果作为当前小时的数据"""
+    log_.info(f"rule_key = {rule_key}")
+    # 获取rov模型结果
+    redis_helper = RedisHelper()
+    if now_h == 0:
+        redis_dt = datetime.datetime.strftime(now_date - datetime.timedelta(days=1), '%Y%m%d')
+        redis_h = 23
+    else:
+        redis_dt = datetime.datetime.strftime(now_date, '%Y%m%d')
+        redis_h = now_h - 1
+
+    key_prefix_list = [config_.RECALL_KEY_NAME_PREFIX_REGION_BY_H, config_.RECALL_KEY_NAME_PREFIX_DUP_REGION_H]
+    fea_df = get_feature_data(project=project, table=table, now_date=now_date - datetime.timedelta(hours=1))
+    region_list = list(set(fea_df[''].to_list()))
+    for region in region_list:
+        log_.info(f"region = {region}")
+        for key_prefix in key_prefix_list:
+            key_name = f"{key_prefix}{region}.{rule_key}.{redis_dt}.{redis_h}"
+            initial_data = redis_helper.get_data_zset_with_index(key_name=key_name, start=0, end=-1, with_scores=True)
+            final_data = dict()
+            for video_id, score in initial_data:
+                final_data[video_id] = score
+            # 存入对应的redis
+            final_key_name = \
+                f"{key_prefix}{region}.{rule_key}.{datetime.datetime.strftime(now_date, '%Y%m%d')}.{now_h}"
+            if len(final_data) > 0:
+                redis_helper.add_data_with_zset(key_name=final_key_name, data=final_data, expire_time=23 * 3600)
+        # 清空线上过滤应用列表
+        redis_helper.del_keys(key_name=f"{config_.REGION_H_VIDEO_FILER}{region}.{rule_key}")
+
+
+def h_timer_check():
+    rule_params = config_.RULE_PARAMS_REGION
+    project = config_.PROJECT_REGION
+    table = config_.TABLE_REGION
+    now_date = datetime.datetime.today()
+    log_.info(f"now_date: {datetime.datetime.strftime(now_date, '%Y%m%d%H')}")
+    now_h = datetime.datetime.now().hour
+    now_min = datetime.datetime.now().minute
+    if now_h == 0:
+        for key, _ in rule_params.items():
+            h_rank_bottom(now_date=now_date, now_h=now_h, rule_key=key, project=project, table=table)
+        return
+    # 查看当前小时更新的数据是否已准备好
+    h_data_count = h_data_check(project=project, table=table, now_date=now_date)
+    if h_data_count > 0:
+        log_.info(f'h_data_count = {h_data_count}')
+        # 数据准备好，进行更新
+        rank_by_h(now_date=now_date, now_h=now_h, rule_params=rule_params, project=project, table=table)
+    elif now_min > 50:
+        log_.info('h_recall data is None, use bottom data!')
+        for key, _ in rule_params.items():
+            h_rank_bottom(now_date=now_date, now_h=now_h, rule_key=key, project=project, table=table)
+    else:
+        # 数据没准备好，1分钟后重新检查
+        Timer(60, h_timer_check).start()
+
+
+if __name__ == '__main__':
+    h_timer_check()