Add alg_growth_gh_reply_video_v1: initial version

alg_growth_gh_reply_video_v1.py

@@ -0,0 +1,258 @@
+# -*- 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()