#coding utf-8
from tqdm import tqdm
import sys
import json

import traceback
from threading import Timer
from tqdm import tqdm
from utils import RedisHelper, data_check, get_feature_data, send_msg_to_feishu
from config import set_config
from log import Log
config_, _ = set_config()
log_ = Log()
redis_helper = RedisHelper()

from feature import get_item_features
from lr_model import LrModel
from utils import exe_sql


if __name__ == "__main__":
    project = 'loghubods'
    datetime = sys.argv[1]
    sql = """
--odps sql 
--********************************************************************--
--author:研发
--create time:2023-12-01 15:48:17
--********************************************************************--
with candidate as (
select
-- 基础特征_用户
mid AS u_id
,machineinfo_brand AS u_brand
,machineinfo_model AS u_device
,SPLIT(machineinfo_system,' ')[0] AS u_system
,machineinfo_system AS u_system_ver
-- 基础特征_视频
,videoid AS i_id
,i_up_id AS i_up_id
,tags as i_tag
,title as i_title
,ceil(log2(i_title_len + 1)) as i_title_len
,ceil(log2(total_time + 1)) as i_play_len
,ceil(log2(i_days_since_upload + 1)) as i_days_since_upload -- 发布时间（距离现在天数）
-- 基础特征_场景
,apptype AS ctx_apptype
,ctx_day AS ctx_day
,ctx_week AS ctx_week
,ctx_hour AS ctx_hour
,ctx_region as ctx_region
,ctx_city as ctx_city
-- 基础特征_交叉
,ui_is_out as ui_is_out
,i_play_len as playtime
-- ,IF(i_play_len > 1,'0','1') AS ui_is_out_new
,rootmid AS ui_root_id
,shareid AS ui_share_id
-- 统计特征_用户
,u_cycle_bucket_7days
,u_cycle_bucket_30days
,u_share_bucket_30days
,ceil(log2(u_1day_exp_cnt + 1)) as u_1day_exp_cnt
,ceil(log2(u_1day_click_cnt + 1)) as u_1day_click_cnt
,ceil(log2(u_1day_share_cnt + 1)) as u_1day_share_cnt
,ceil(log2(u_1day_return_cnt + 1)) as u_1day_return_cnt
,ceil(log2(u_3day_exp_cnt + 1)) as u_3day_exp_cnt
,ceil(log2(u_3day_click_cnt + 1)) as u_3day_click_cnt
,ceil(log2(u_3day_share_cnt + 1)) as u_3day_share_cnt
,ceil(log2(u_3day_return_cnt + 1)) as u_3day_return_cnt
,ceil(log2(u_7day_exp_cnt + 1)) as u_7day_exp_cnt
,ceil(log2(u_7day_click_cnt + 1)) as u_7day_click_cnt
,ceil(log2(u_7day_share_cnt + 1)) as u_7day_share_cnt
,ceil(log2(u_7day_return_cnt + 1)) as u_7day_return_cnt
,ceil(log2(u_3month_exp_cnt + 1)) as u_3month_exp_cnt
,ceil(log2(u_3month_click_cnt + 1)) as u_3month_click_cnt
,ceil(log2(u_3month_share_cnt + 1)) as u_3month_share_cnt
,ceil(log2(u_3month_return_cnt + 1)) as u_3month_return_cnt
,round(if(u_ctr_1day > 10.0, 10.0, u_ctr_1day) / 10.0, 6) as u_ctr_1day
,round(if(u_str_1day > 10.0, 10.0, u_str_1day) / 10.0, 6) as u_str_1day
,round(if(u_rov_1day > 10.0, 10.0, u_rov_1day) / 10.0, 6) as u_rov_1day
,round(if(u_ros_1day > 10.0, 10.0, u_ros_1day) / 10.0, 6) as u_ros_1day
,round(if(u_ctr_3day > 10.0, 10.0, u_ctr_3day) / 10.0, 6) as u_ctr_3day
,round(if(u_str_3day > 10.0, 10.0, u_str_3day) / 10.0, 6) as u_str_3day
,round(if(u_rov_3day > 10.0, 10.0, u_rov_3day) / 10.0, 6) as u_rov_3day
,round(if(u_ros_3day > 10.0, 10.0, u_ros_3day) / 10.0, 6) as u_ros_3day
,round(if(u_ctr_7day > 10.0, 10.0, u_ctr_7day) / 10.0, 6) as u_ctr_7day
,round(if(u_str_7day > 10.0, 10.0, u_str_7day) / 10.0, 6) as u_str_7day
,round(if(u_rov_7day > 10.0, 10.0, u_rov_7day) / 10.0, 6) as u_rov_7day
,round(if(u_ros_7day > 10.0, 10.0, u_ros_7day) / 10.0, 6) as u_ros_7day
,round(if(u_ctr_3month > 10.0, 10.0, u_ctr_3month) / 10.0, 6) as u_ctr_3month
,round(if(u_str_3month > 10.0, 10.0, u_str_3month) / 10.0, 6) as u_str_3month
,round(if(u_rov_3month > 10.0, 10.0, u_rov_3month) / 10.0, 6) as u_rov_3month
,round(if(u_ros_3month > 10.0, 10.0, u_ros_3month) / 10.0, 6) as u_ros_3month
-- 统计特征_视频
,ceil(log2(i_1day_exp_cnt + 1)) as i_1day_exp_cnt
,ceil(log2(i_1day_click_cnt + 1)) as i_1day_click_cnt
,ceil(log2(i_1day_share_cnt + 1)) as i_1day_share_cnt
,ceil(log2(i_1day_return_cnt + 1)) as i_1day_return_cnt
,ceil(log2(i_3day_exp_cnt + 1)) as i_3day_exp_cnt
,ceil(log2(i_3day_click_cnt + 1)) as i_3day_click_cnt
,ceil(log2(i_3day_share_cnt + 1)) as i_3day_share_cnt
,ceil(log2(i_3day_return_cnt + 1)) as i_3day_return_cnt
,ceil(log2(i_7day_exp_cnt + 1)) as i_7day_exp_cnt
,ceil(log2(i_7day_click_cnt + 1)) as i_7day_click_cnt
,ceil(log2(i_7day_share_cnt + 1)) as i_7day_share_cnt
,ceil(log2(i_7day_return_cnt + 1)) as i_7day_return_cnt
,ceil(log2(i_3month_exp_cnt + 1)) as i_3month_exp_cnt
,ceil(log2(i_3month_click_cnt + 1)) as i_3month_click_cnt
,ceil(log2(i_3month_share_cnt + 1)) as i_3month_share_cnt
,ceil(log2(i_3month_return_cnt + 1)) as i_3month_return_cnt
,round(if(i_ctr_1day > 10.0, 10.0, i_ctr_1day) / 10.0, 6) as i_ctr_1day
,round(if(i_str_1day > 10.0, 10.0, i_str_1day) / 10.0, 6) as i_str_1day
,round(if(i_rov_1day > 10.0, 10.0, i_rov_1day) / 10.0, 6) as i_rov_1day
,round(if(i_ros_1day > 10.0, 10.0, i_ros_1day) / 10.0, 6) as i_ros_1day
,round(if(i_ctr_3day > 10.0, 10.0, i_ctr_3day) / 10.0, 6) as i_ctr_3day
,round(if(i_str_3day > 10.0, 10.0, i_str_3day) / 10.0, 6) as i_str_3day
,round(if(i_rov_3day > 10.0, 10.0, i_rov_3day) / 10.0, 6) as i_rov_3day
,round(if(i_ros_3day > 10.0, 10.0, i_ros_3day) / 10.0, 6) as i_ros_3day
,round(if(i_ctr_7day > 10.0, 10.0, i_ctr_7day) / 10.0, 6) as i_ctr_7day
,round(if(i_str_7day > 10.0, 10.0, i_str_7day) / 10.0, 6) as i_str_7day
,round(if(i_rov_7day > 10.0, 10.0, i_rov_7day) / 10.0, 6) as i_rov_7day
,round(if(i_ros_7day > 10.0, 10.0, i_ros_7day) / 10.0, 6) as i_ros_7day
,round(if(i_ctr_3month > 10.0, 10.0, i_ctr_3month) / 10.0, 6) as i_ctr_3month
,round(if(i_str_3month > 10.0, 10.0, i_str_3month) / 10.0, 6) as i_str_3month
,round(if(i_rov_3month > 10.0, 10.0, i_rov_3month) / 10.0, 6) as i_rov_3month
,round(if(i_ros_3month > 10.0, 10.0, i_ros_3month) / 10.0, 6) as i_ros_3month
from
user_video_features_data_final
where dt='{datetime}'
and ad_ornot = '0'
and apptype != '13'
), candidate_user as (
    SELECT 
    u_id,
    max(u_brand) as u_brand,
    max(u_device) as u_device,
    max(u_system) as u_system,
    max(u_system_ver) as u_system_ver,
    max(ctx_region) as ctx_region,
    max(ctx_city) as ctx_city,
    max(u_cycle_bucket_7days) as u_cycle_bucket_7days,
    max(u_cycle_bucket_30days) as u_cycle_bucket_30days,
    max(u_share_bucket_30days) as u_share_bucket_30days,
    max(u_1day_exp_cnt) as u_1day_exp_cnt,
    max(u_1day_click_cnt) as u_1day_click_cnt,
    max(u_1day_share_cnt) as u_1day_share_cnt,
    max(u_1day_return_cnt) as u_1day_return_cnt,
    max(u_3day_exp_cnt) as u_3day_exp_cnt,
    max(u_3day_click_cnt) as u_3day_click_cnt,
    max(u_3day_share_cnt) as u_3day_share_cnt,
    max(u_3day_return_cnt) as u_3day_return_cnt,
    max(u_7day_exp_cnt) as u_7day_exp_cnt,
    max(u_7day_click_cnt) as u_7day_click_cnt,
    max(u_7day_share_cnt) as u_7day_share_cnt,
    max(u_7day_return_cnt) as u_7day_return_cnt,
    max(u_3month_exp_cnt) as u_3month_exp_cnt,
    max(u_3month_click_cnt) as u_3month_click_cnt,
    max(u_3month_share_cnt) as u_3month_share_cnt,
    max(u_3month_return_cnt) as u_3month_return_cnt,
    max(u_ctr_1day) as u_ctr_1day,
    max(u_str_1day) as u_str_1day,
    max(u_rov_1day) as u_rov_1day,
    max(u_ros_1day) as u_ros_1day,
    max(u_ctr_3day) as u_ctr_3day,
    max(u_str_3day) as u_str_3day,
    max(u_rov_3day) as u_rov_3day,
    max(u_ros_3day) as u_ros_3day,
    max(u_ctr_7day) as u_ctr_7day,
    max(u_str_7day) as u_str_7day,
    max(u_rov_7day) as u_rov_7day,
    max(u_ros_7day) as u_ros_7day,
    max(u_ctr_3month) as u_ctr_3month,
    max(u_str_3month) as u_str_3month,
    max(u_rov_3month) as u_rov_3month,
    max(u_ros_3month) as u_ros_3month
    FROM 
    candidate
    group by u_id
), candidate_item as (
    select
    i_id,
    max(i_up_id) as i_up_id,
    max(i_title_len) as i_title_len,
    max(i_play_len) as i_play_len,
    max(i_days_since_upload) as i_days_since_upload,
    max(i_1day_exp_cnt) as i_1day_exp_cnt,
    max(i_1day_click_cnt) as i_1day_click_cnt,
    max(i_1day_share_cnt) as i_1day_share_cnt,
    max(i_1day_return_cnt) as i_1day_return_cnt,
    max(i_3day_exp_cnt) as i_3day_exp_cnt,
    max(i_3day_click_cnt) as i_3day_click_cnt,
    max(i_3day_share_cnt) as i_3day_share_cnt,
    max(i_3day_return_cnt) as i_3day_return_cnt,
    max(i_7day_exp_cnt) as i_7day_exp_cnt,
    max(i_7day_click_cnt) as i_7day_click_cnt,
    max(i_7day_share_cnt) as i_7day_share_cnt,
    max(i_7day_return_cnt) as i_7day_return_cnt,
    max(i_3month_exp_cnt) as i_3month_exp_cnt,
    max(i_3month_click_cnt) as i_3month_click_cnt,
    max(i_3month_share_cnt) as i_3month_share_cnt,
    max(i_3month_return_cnt) as i_3month_return_cnt,
    max(i_ctr_1day) as i_ctr_1day,
    max(i_str_1day) as i_str_1day,
    max(i_rov_1day) as i_rov_1day,
    max(i_ros_1day) as i_ros_1day,
    max(i_ctr_3day) as i_ctr_3day,
    max(i_str_3day) as i_str_3day,
    max(i_rov_3day) as i_rov_3day,
    max(i_ros_3day) as i_ros_3day,
    max(i_ctr_7day) as i_ctr_7day,
    max(i_str_7day) as i_str_7day,
    max(i_rov_7day) as i_rov_7day,
    max(i_ros_7day) as i_ros_7day,
    max(i_ctr_3month) as i_ctr_3month,
    max(i_str_3month) as i_str_3month,
    max(i_rov_3month) as i_rov_3month,
    max(i_ros_3month) as i_ros_3month
    FROM 
    candidate
    group by i_id
)
SELECT
*
from candidate_item
    """.format(datetime=datetime)
    # print(sql)
    data = exe_sql(project, sql)
    print('sql done')
    # data.to_csv('./data/ad_out_sample_v2_item.{datetime}'.format(datetime=datetime), sep='\t')
    # data = pd.read_csv('./data/ad_out_sample_v2_item.{datetime}'.format(datetime=datetime), sep='\t', dtype=str)
    model_key = 'ad_out_v2_model_v1.day'
    lr_model = LrModel('model/{}.json'.format(model_key))
    item_h_dict = {}
    k_col = 'i_id'
    dt = datetime
    key_name_prefix = f"{config_.KEY_NAME_PREFIX_AD_OUT_MODEL_SCORE_ITEM}{model_key}"
    print(key_name_prefix)
    mean_item_h = 0.0
    count_item_h = 0
    with data.open_reader() as reader:
        for row in tqdm(reader):
            k = str(row['i_id'])
            item_features = get_item_features(row)
            item_h = lr_model.predict_h(item_features)
            redis_helper.set_data_to_redis(f"{key_name_prefix}:{k}", item_h, 28 * 3600)
            item_h_dict[k] = item_h
            mean_item_h += item_h
            count_item_h += 1
            # print(item_features)
            # print(item_h)
    mean_item_h = mean_item_h / count_item_h 
    item_h_dict['mean'] = mean_item_h 
    print(mean_item_h)
    print(count_item_h)
    k = 'mean'
    redis_helper.set_data_to_redis(f"{key_name_prefix}:{k}", mean_item_h, 28 * 3600)
    with open('{}.json'.format(key_name_prefix), 'w') as fout:
        json.dump(item_h_dict, fout, indent=2, ensure_ascii=False, sort_keys=True)