Merge branch 'feature_20231204_yangxiaohui_add_lr_v1' of algorithm/rov-offline into master

config.py

@@ -2358,6 +2358,10 @@ class BaseConfig(object):
         },
     }
 
+    # 有广告时的用户跳出分数预测结果存放 redis key 前缀，完整格式：ad:out:model:score:user:{model_key}:{date}
+    KEY_NAME_PREFIX_AD_OUT_MODEL_SCORE_USER = 'ad:out:model:score:user:'
+    # 有广告时的视频跳出分数预测结果存放 redis key 前缀，完整格式：ad:out:model:score:video:{model_key}:{date}
+    KEY_NAME_PREFIX_AD_OUT_MODEL_SCORE_ITEM = 'ad:out:model:score:item:'
     # 用户组有广告时的分享率预测结果存放 redis key 前缀，完整格式：ad:users:group:predict:share:rate:{user_data_key}:{user_rule_key}:{date}
     KEY_NAME_PREFIX_AD_GROUP = 'ad:users:group:predict:share:rate:'
     # 视频有广告时的分享率预测结果存放 redis key 前缀，完整格式：ad:video:predict:share:rate:{video_data_key}:{date}

feature.py

@@ -0,0 +1,155 @@
+#coding utf-8
+import sys
+import pandas as pd
+
+from tqdm import tqdm
+
+from collections import defaultdict
+types = defaultdict(str)
+
+item_sparse_conf = [
+# 基础特征_视频
+'i_id',
+'i_up_id',
+# 'i_tag',
+# 'i_title',
+'i_title_len',
+'i_play_len',
+'i_days_since_upload',
+# 统计特征_视频
+'i_1day_exp_cnt',
+'i_1day_click_cnt',
+'i_1day_share_cnt',
+'i_1day_return_cnt',
+'i_3day_exp_cnt',
+'i_3day_click_cnt',
+'i_3day_share_cnt',
+'i_3day_return_cnt',
+'i_7day_exp_cnt',
+'i_7day_click_cnt',
+'i_7day_share_cnt',
+'i_7day_return_cnt',
+'i_3month_exp_cnt',
+'i_3month_click_cnt',
+'i_3month_share_cnt',
+'i_3month_return_cnt',
+]
+item_dense_conf = [
+'i_ctr_1day',
+'i_str_1day',
+'i_rov_1day',
+'i_ros_1day',
+'i_ctr_3day',
+'i_str_3day',
+'i_rov_3day',
+'i_ros_3day',
+'i_ctr_7day',
+'i_str_7day',
+'i_rov_7day',
+'i_ros_7day',
+'i_ctr_3month',
+'i_str_3month',
+'i_rov_3month',
+'i_ros_3month',
+]
+
+user_sparse_conf = [
+'u_brand',
+'u_device',
+'u_system',
+'u_system_ver',
+'ctx_region',
+'ctx_city',
+# 统计特征_用户
+'u_cycle_bucket_7days',
+'u_cycle_bucket_30days',
+'u_share_bucket_30days',
+'u_1day_exp_cnt',
+'u_1day_click_cnt',
+'u_1day_share_cnt',
+'u_1day_return_cnt',
+'u_3day_exp_cnt',
+'u_3day_click_cnt',
+'u_3day_share_cnt',
+'u_3day_return_cnt',
+'u_7day_exp_cnt',
+'u_7day_click_cnt',
+'u_7day_share_cnt',
+'u_7day_return_cnt',
+'u_3month_exp_cnt',
+'u_3month_click_cnt',
+'u_3month_share_cnt',
+'u_3month_return_cnt',
+]
+
+user_dense_conf = [
+'u_ctr_1day',
+'u_str_1day',
+'u_rov_1day',
+'u_ros_1day',
+'u_ctr_3day',
+'u_str_3day',
+'u_rov_3day',
+'u_ros_3day',
+'u_ctr_7day',
+'u_str_7day',
+'u_rov_7day',
+'u_ros_7day',
+'u_ctr_3month',
+'u_str_3month',
+'u_rov_3month',
+'u_ros_3month',
+]
+
+def format_x(x):
+    if x is None:
+        x = ''
+    return str(x).replace(' ', '').replace(':', '_')
+def sparse_fea_2_feature(v, k):
+    f_k = format_x(k)
+    f_v = format_x(v)
+    if len(f_v) < 1:
+        return (None, None)
+    return ('#'.join([f_k, f_v]), 1.0)
+
+def dense_fea_2_feature(v, k):
+    f_k = format_x(k)
+    f_v = format_x(v)
+    if len(f_v) < 1:
+        return (None, None)
+    return (f_k, float(f_v))
+
+def get_features(sparse_conf, dense_conf, row):
+    features = dict(map(lambda k:sparse_fea_2_feature(row[k], k), sparse_conf))
+    dense_features = dict(map(lambda k:dense_fea_2_feature(row[k], k), dense_conf))
+    features.update(dense_features)
+    if None in features:
+        del(features[None])
+    return features
+
+def get_item_features(row):
+    return get_features(item_sparse_conf, item_dense_conf, row)
+
+def get_user_features(row):
+    return get_features(user_sparse_conf, user_dense_conf, row)
+     
+label_col = 'ui_is_out'
+
+sparse_fea_cols = [
+# 'u_id',
+# 基础特征_场景
+#'ctx_day','
+'ctx_apptype',
+'ctx_week',
+'ctx_hour',
+# 基础特征_交叉
+#'ui_is_out',
+#'playtime',
+#'ui_root_id',
+#'ui_share_id',
+]
+
+dense_fea_cols = [
+
+]
+

get_sample_ad_out_v1_item.py

@@ -0,0 +1,260 @@
+#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_v1'
+    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)
+

get_sample_ad_out_v1_item.sh

@@ -0,0 +1,3 @@
+
+/root/anaconda3/bin/python get_sample_ad_out_v1_item.py $*
+

get_sample_ad_out_v1_user.py

@@ -0,0 +1,259 @@
+#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_user_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_user
+    """.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_v1'
+    lr_model = LrModel('model/{}.json'.format(model_key))
+    user_h_dict = {}
+    k_col = 'u_id'
+    dt = datetime
+    key_name_prefix = f"{config_.KEY_NAME_PREFIX_AD_OUT_MODEL_SCORE_USER}{model_key}"
+    print(key_name_prefix)
+    mean_user_h = 0.0
+    count_user_h = 0
+    with data.open_reader() as reader:
+        for row in tqdm(reader):
+            k = str(row['u_id'])
+            user_features = get_user_features(row)
+            user_h = lr_model.predict_h(user_features)
+            redis_helper.set_data_to_redis(f"{key_name_prefix}:{k}", user_h, 28 * 3600)
+            user_h_dict[k] = user_h
+            mean_user_h += user_h
+            count_user_h += 1
+            # print(user_features)
+            # print(user_h)
+    mean_user_h = mean_user_h / count_user_h 
+    user_h_dict['mean'] = mean_user_h 
+    print(mean_user_h)
+    print(count_user_h)
+    k = 'mean'
+    redis_helper.set_data_to_redis(f"{key_name_prefix}:{k}", mean_user_h, 28 * 3600)
+    with open('{}.json'.format(key_name_prefix), 'w') as fout:
+        json.dump(user_h_dict, fout, indent=2, ensure_ascii=False, sort_keys=True)
+

get_sample_ad_out_v1_user.sh

@@ -0,0 +1,3 @@
+
+/root/anaconda3/bin/python get_sample_ad_out_v1_user.py $*
+

lr_model.py

@@ -0,0 +1,57 @@
+#coding utf-8
+import json
+import math
+
+def load_json(filename):
+    with open(filename, 'r') as fin:
+        json_data = json.load(fin)
+    return json_data
+
+def wx(w_dict, kv):
+    k, v = kv
+    w = w_dict.get(k, 0.0)
+    return w * v
+
+def sigmoid(x):
+  return 1.0 / (1.0 + math.exp(-x))
+
+def libsvm_row_to_features(row):
+    items = row.strip().split(' ')
+    label = items[0]
+    features = {}
+    for kv in items[1:]:
+        k, v = kv.split(':')
+        features[k] = float(v)
+    return label, features
+
+class LrModel:
+    def __init__(self, w_json_file):
+        self.w_dict = load_json(w_json_file)
+    
+    def predict_h(self, features):
+        h = sum(map(lambda x: wx(self.w_dict, x), features.items()))
+        return h
+
+    def predict(self, features):
+        bias = self.w_dict.get('bias', 0.0)
+        h = self.predict_h(features)
+        score = sigmoid(h + bias)
+        return score
+
+def test():
+    lr_model = LrModel('model/ad_out_v2_model_v1.day.json')
+    rows = [
+        (0.279004,'0 u_brand#vivo:1 u_device#V1829A:1 u_system#Android:1 u_system_ver#Android10:1 i_id#17015839:1 i_up_id#24811642:1 i_title_len#5:1 i_play_len#8:1 i_days_since_upload#4:1 ctx_week#3:1 ctx_hour#8:1 ctx_region#山西:1 ctx_city#临汾:1 u_3month_exp_cnt#4:1 u_3month_click_cnt#4:1 u_3month_share_cnt#2:1 u_3month_return_cnt#6:1 i_1day_exp_cnt#16:1 i_1day_click_cnt#15:1 i_1day_share_cnt#12:1 i_1day_return_cnt#15:1 i_3day_exp_cnt#18:1 i_3day_click_cnt#17:1 i_3day_share_cnt#14:1 i_3day_return_cnt#15:1 i_7day_exp_cnt#18:1 i_7day_click_cnt#18:1 i_7day_share_cnt#14:1 i_7day_return_cnt#15:1 i_3month_exp_cnt#19:1 i_3month_click_cnt#18:1 i_3month_share_cnt#14:1 i_3month_return_cnt#16:1 u_ctr_3month:0.066667 u_str_3month:0.0375 u_rov_3month:0.283333 u_ros_3month:1.0 i_ctr_1day:0.070518 i_str_1day:0.007219 i_rov_1day:0.044376 i_ros_1day:0.871681 i_ctr_3day:0.070359 i_str_3day:0.007297 i_rov_3day:0.017242 i_ros_3day:0.335814 i_ctr_7day:0.070245 i_str_7day:0.007044 i_rov_7day:0.012268 i_ros_7day:0.247943 i_ctr_3month:0.06989 i_str_3month:0.007203 i_rov_3month:0.012624 i_ros_3month:0.250784'),
+        (0.454255,'1 u_brand#vivo:1 u_device#V2230A:1 u_system#Android:1 u_system_ver#Android13:1 i_id#17141266:1 i_up_id#65303321:1 i_title_len#5:1 i_play_len#8:1 i_days_since_upload#3:1 ctx_week#3:1 ctx_hour#17:1 ctx_region#河北:1 ctx_city#邯郸:1 u_3month_exp_cnt#3:1 u_3month_click_cnt#3:1 i_1day_exp_cnt#19:1 i_1day_click_cnt#19:1 i_1day_share_cnt#16:1 i_1day_return_cnt#19:1 i_3day_exp_cnt#21:1 i_3day_click_cnt#21:1 i_3day_share_cnt#18:1 i_3day_return_cnt#20:1 i_7day_exp_cnt#22:1 i_7day_click_cnt#21:1 i_7day_share_cnt#18:1 i_7day_return_cnt#20:1 i_3month_exp_cnt#22:1 i_3month_click_cnt#21:1 i_3month_share_cnt#18:1 i_3month_return_cnt#20:1 u_ctr_3month:0.1 i_ctr_1day:0.078281 i_str_1day:0.009801 i_rov_1day:0.066601 i_ros_1day:0.868055 i_ctr_3day:0.076168 i_str_3day:0.010706 i_rov_3day:0.036384 i_ros_3day:0.446198 i_ctr_7day:0.07423 i_str_7day:0.011886 i_rov_7day:0.022732 i_ros_7day:0.257645 i_ctr_3month:0.07423 i_str_3month:0.011886 i_rov_3month:0.022732 i_ros_3month:0.257645'),
+        (lr_model.predict({}), '0 dsiaod:1 dsaodadsa:1.2'),
+    ]
+    for std_score, row in rows:
+        label, features = libsvm_row_to_features(row)
+        score = lr_model.predict(features)
+        score_diff = std_score - score
+        print(std_score, score, score_diff)
+        assert(abs(score_diff) < 10e-6)
+
+if __name__ == '__main__':
+    test() 
+

model/ad_out_v1.json

@@ -0,0 +1,29218 @@
+{
+  "bias": -0.0680416,
+  "ctx_apptype#0": 0.154693,
+  "ctx_apptype#17": -0.170167,
+  "ctx_apptype#18": 0.01295,
+  "ctx_apptype#19": 0.00723347,
+  "ctx_apptype#22": -0.207505,
+  "ctx_apptype#3": -0.134736,
+  "ctx_apptype#4": 0.0269848,
+  "ctx_apptype#5": -0.120473,
+  "ctx_apptype#6": -0.371889,
+  "ctx_city#七台河": 0.127732,
+  "ctx_city#万宁": -0.044033,
+  "ctx_city#三亚": 0.047661,
+  "ctx_city#三明": 0.0318108,
+  "ctx_city#三门峡": 0.0728751,
+  "ctx_city#上海": -0.108464,
+  "ctx_city#上饶": 0.0928462,
+  "ctx_city#东方": 0.028649,
+  "ctx_city#东莞": 0.0478834,
+  "ctx_city#东营": 0.174188,
+  "ctx_city#中卫": -0.0585551,
+  "ctx_city#中山": 0.0198762,
+  "ctx_city#临夏回族自治州": 0.126146,
+  "ctx_city#临汾": 0.116349,
+  "ctx_city#临沂": 0.165179,
+  "ctx_city#临沧": 0.122374,
+  "ctx_city#临高县": -0.0767778,
+  "ctx_city#丹东": 0.058373,
+  "ctx_city#丽水": 0.234553,
+  "ctx_city#丽江": -0.0440252,
+  "ctx_city#乌兰察布": -0.0256678,
+  "ctx_city#乌海": 0.0851041,
+  "ctx_city#乌鲁木齐": -0.129721,
+  "ctx_city#乐东黎族自治县": -0.0172385,
+  "ctx_city#乐山": -0.00843033,
+  "ctx_city#九江": 0.125541,
+  "ctx_city#云浮": 0.127878,
+  "ctx_city#五家渠": 0.0478796,
+  "ctx_city#五指山": -0.0223104,
+  "ctx_city#亳州": 0.186276,
+  "ctx_city#仙桃": 0.0322017,
+  "ctx_city#伊春": 0.121008,
+  "ctx_city#伊犁哈萨克自治州": -0.0217273,
+  "ctx_city#佛山": 0.00372581,
+  "ctx_city#佳木斯": 0.117267,
+  "ctx_city#保亭黎族苗族自治县": 0.0631182,
+  "ctx_city#保定": 0.0851775,
+  "ctx_city#保山": 0.0601087,
+  "ctx_city#信阳": 0.0626729,
+  "ctx_city#儋州": 0.134906,
+  "ctx_city#克孜勒苏柯尔克孜自治州": -0.0342878,
+  "ctx_city#克拉玛依": -0.108669,
+  "ctx_city#六安": 0.0327568,
+  "ctx_city#六盘水": 0.0397914,
+  "ctx_city#兰州": -0.103236,
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+  "u_system_ver#WindowsServer2016x64": 0.0120177,
+  "u_system_ver#WindowsUnKnowx64": -0.0241662,
+  "u_system_ver#iOS10.0": -0.00447717,
+  "u_system_ver#iOS10.0.1": 0.00841458,
+  "u_system_ver#iOS10.0.2": 0.021559,
+  "u_system_ver#iOS10.1": -0.00306181,
+  "u_system_ver#iOS10.1.1": 0.0785825,
+  "u_system_ver#iOS10.2": 0.0818787,
+  "u_system_ver#iOS10.2.1": -0.024236,
+  "u_system_ver#iOS10.3": -0.0061653,
+  "u_system_ver#iOS10.3.1": 0.0275691,
+  "u_system_ver#iOS10.3.2": 0.00454329,
+  "u_system_ver#iOS10.3.3": -0.0724198,
+  "u_system_ver#iOS11.0": 0.016529,
+  "u_system_ver#iOS11.0.1": -0.00107159,
+  "u_system_ver#iOS11.0.2": -0.0074998,
+  "u_system_ver#iOS11.0.3": -0.0418826,
+  "u_system_ver#iOS11.1": 0.00302129,
+  "u_system_ver#iOS11.1.1": 0.00971092,
+  "u_system_ver#iOS11.1.2": -0.0138409,
+  "u_system_ver#iOS11.2": -0.0525745,
+  "u_system_ver#iOS11.2.1": 0.0498701,
+  "u_system_ver#iOS11.2.2": -0.0386283,
+  "u_system_ver#iOS11.2.5": 0.0451826,
+  "u_system_ver#iOS11.2.6": -0.0387208,
+  "u_system_ver#iOS11.3": 0.037206,
+  "u_system_ver#iOS11.3.1": 0.0098698,
+  "u_system_ver#iOS11.4": -0.00696738,
+  "u_system_ver#iOS11.4.1": -0.040639,
+  "u_system_ver#iOS12.0": -0.102568,
+  "u_system_ver#iOS12.0.1": 0.0333744,
+  "u_system_ver#iOS12.1": -0.0309841,
+  "u_system_ver#iOS12.1.1": -0.0608764,
+  "u_system_ver#iOS12.1.2": -0.0413038,
+  "u_system_ver#iOS12.1.3": -0.0329187,
+  "u_system_ver#iOS12.1.4": 0.0111948,
+  "u_system_ver#iOS12.2": -0.176412,
+  "u_system_ver#iOS12.3": -0.0095046,
+  "u_system_ver#iOS12.3.1": -0.107664,
+  "u_system_ver#iOS12.3.2": -0.0630981,
+  "u_system_ver#iOS12.4": 0.028594,
+  "u_system_ver#iOS12.4.1": -0.019457,
+  "u_system_ver#iOS12.4.2": 0.0245251,
+  "u_system_ver#iOS12.4.3": -0.0451235,
+  "u_system_ver#iOS12.4.4": -0.0373604,
+  "u_system_ver#iOS12.4.5": 0.0168909,
+  "u_system_ver#iOS12.4.6": -0.0339746,
+  "u_system_ver#iOS12.4.7": -0.0709766,
+  "u_system_ver#iOS12.4.8": -0.0955947,
+  "u_system_ver#iOS12.4.9": 0.00755319,
+  "u_system_ver#iOS12.5": 0.00486578,
+  "u_system_ver#iOS12.5.1": -0.063806,
+  "u_system_ver#iOS12.5.2": -0.0518584,
+  "u_system_ver#iOS12.5.3": -0.0875373,
+  "u_system_ver#iOS12.5.4": -0.084012,
+  "u_system_ver#iOS12.5.5": -0.240195,
+  "u_system_ver#iOS12.5.6": -0.259702,
+  "u_system_ver#iOS12.5.7": -0.523958,
+  "u_system_ver#iOS13.0": -0.0657359,
+  "u_system_ver#iOS13.1": 0.00201342,
+  "u_system_ver#iOS13.1.1": -0.0460363,
+  "u_system_ver#iOS13.1.2": -0.180376,
+  "u_system_ver#iOS13.1.3": -0.0229236,
+  "u_system_ver#iOS13.2": -0.0149153,
+  "u_system_ver#iOS13.2.2": 0.00642652,
+  "u_system_ver#iOS13.2.3": -0.0249761,
+  "u_system_ver#iOS13.3": -0.10433,
+  "u_system_ver#iOS13.3.1": 0.0206728,
+  "u_system_ver#iOS13.4": -0.0872443,
+  "u_system_ver#iOS13.4.1": -0.0817286,
+  "u_system_ver#iOS13.5": 0.0238999,
+  "u_system_ver#iOS13.5.1": -0.0572186,
+  "u_system_ver#iOS13.6": -0.0596482,
+  "u_system_ver#iOS13.6.1": -0.103791,
+  "u_system_ver#iOS13.7": -0.0916792,
+  "u_system_ver#iOS14.0": 0.00764645,
+  "u_system_ver#iOS14.0.1": -0.0656945,
+  "u_system_ver#iOS14.1": -0.00684459,
+  "u_system_ver#iOS14.2": -0.0948605,
+  "u_system_ver#iOS14.2.1": 0.0194013,
+  "u_system_ver#iOS14.3": -0.0245143,
+  "u_system_ver#iOS14.4": -0.087033,
+  "u_system_ver#iOS14.4.1": -0.0216799,
+  "u_system_ver#iOS14.4.2": -0.0679826,
+  "u_system_ver#iOS14.5": -0.00240593,
+  "u_system_ver#iOS14.5.1": -0.12297,
+  "u_system_ver#iOS14.6": -0.140203,
+  "u_system_ver#iOS14.7": -0.0166302,
+  "u_system_ver#iOS14.7.1": -0.154952,
+  "u_system_ver#iOS14.8": -0.181427,
+  "u_system_ver#iOS14.8.1": -0.077867,
+  "u_system_ver#iOS15.0": 0.0479662,
+  "u_system_ver#iOS15.0.1": 0.0822174,
+  "u_system_ver#iOS15.0.2": -0.128803,
+  "u_system_ver#iOS15.1": -0.130203,
+  "u_system_ver#iOS15.1.1": 0.0694984,
+  "u_system_ver#iOS15.2": 0.0453237,
+  "u_system_ver#iOS15.2.1": -0.0471349,
+  "u_system_ver#iOS15.3": -0.0925672,
+  "u_system_ver#iOS15.3.1": 0.0339289,
+  "u_system_ver#iOS15.4": 0.0435488,
+  "u_system_ver#iOS15.4.1": 0.092686,
+  "u_system_ver#iOS15.5": 0.134683,
+  "u_system_ver#iOS15.6": 0.0190261,
+  "u_system_ver#iOS15.6.1": 0.130025,
+  "u_system_ver#iOS15.7": -0.0118688,
+  "u_system_ver#iOS15.7.1": -0.112565,
+  "u_system_ver#iOS15.7.2": -0.0860621,
+  "u_system_ver#iOS15.7.3": -0.208391,
+  "u_system_ver#iOS15.7.4": -0.134457,
+  "u_system_ver#iOS15.7.5": -0.154078,
+  "u_system_ver#iOS15.7.6": -0.147602,
+  "u_system_ver#iOS15.7.7": -0.158578,
+  "u_system_ver#iOS15.7.8": -0.185496,
+  "u_system_ver#iOS15.7.9": -0.305223,
+  "u_system_ver#iOS15.8": -0.338732,
+  "u_system_ver#iOS16.0": 0.00904611,
+  "u_system_ver#iOS16.0.1": 0.0171633,
+  "u_system_ver#iOS16.0.2": 0.00980273,
+  "u_system_ver#iOS16.0.3": 0.00759118,
+  "u_system_ver#iOS16.1": 0.0808684,
+  "u_system_ver#iOS16.1.1": 0.0825193,
+  "u_system_ver#iOS16.1.2": 0.110254,
+  "u_system_ver#iOS16.2": 0.119501,
+  "u_system_ver#iOS16.3": 0.0389274,
+  "u_system_ver#iOS16.3.1": 0.0849038,
+  "u_system_ver#iOS16.4": -0.0123681,
+  "u_system_ver#iOS16.4.1": 0.0125857,
+  "u_system_ver#iOS16.5": 0.0855124,
+  "u_system_ver#iOS16.5.1": 0.0643442,
+  "u_system_ver#iOS16.6": 0.0679727,
+  "u_system_ver#iOS16.6.1": 0.131507,
+  "u_system_ver#iOS16.7": 0.00839878,
+  "u_system_ver#iOS16.7.1": -0.0731998,
+  "u_system_ver#iOS16.7.2": -0.0364528,
+  "u_system_ver#iOS17.0": -0.0288167,
+  "u_system_ver#iOS17.0.1": -0.0315307,
+  "u_system_ver#iOS17.0.2": 0.123037,
+  "u_system_ver#iOS17.0.3": -0.0626062,
+  "u_system_ver#iOS17.1": 0.0841623,
+  "u_system_ver#iOS17.1.1": 0.159174,
+  "u_system_ver#iOS17.1.2": 0.111291,
+  "u_system_ver#iOS17.2": 0.0697445,
+  "u_system_ver#iOS9.0": 0.00862702,
+  "u_system_ver#iOS9.0.2": 0.00369103,
+  "u_system_ver#iOS9.1": 0.108123,
+  "u_system_ver#iOS9.2": 0.0431719,
+  "u_system_ver#iOS9.2.1": 0.039346,
+  "u_system_ver#iOS9.3": 0.0255919,
+  "u_system_ver#iOS9.3.1": 0.00364794,
+  "u_system_ver#iOS9.3.2": 0.0332959,
+  "u_system_ver#iOS9.3.3": -0.0213764,
+  "u_system_ver#iOS9.3.4": 0.064181,
+  "u_system_ver#iOS9.3.5": 0.0287995,
+  "u_system_ver#macOS10.13.6": -0.00900209,
+  "u_system_ver#macOS10.15.7": 0.0375739,
+  "u_system_ver#macOS11.5.2": 0.0494947,
+  "u_system_ver#macOS12.6.3": 0.0182431,
+  "u_system_ver#macOS13.3.0": 0.0172364,
+  "u_system_ver#macOS14.0.0": 0.0191403,
+  "u_system_ver#win10": 0.0811702
+}

nohug.sh

@@ -0,0 +1,30 @@
+
+mkdir -p log
+
+if [ -f $1 ]
+then
+    chmod +x $1
+else
+   echo "${1} not exist"
+   exit -1
+fi
+
+script_str=$(echo $1 | tr /. _)
+args_str=$(echo $(IFS="_"; echo "${*:2}") | tr /. _)
+
+echo $args_str
+if [ -z "$args_str" ]; then
+   log_file=log/${script_str}.log
+else
+   log_file=log/${script_str}.${args_str}.log
+fi
+
+time=$(date "+%Y%m%d-%H%M%S")
+if [ -f $log_file ]
+then
+    cp $log_file ${log_file}.${time}
+fi
+
+nohup ./$1 ${*:2}  > $log_file 2>&1&
+echo $log_file
+

utils.py

@@ -10,6 +10,7 @@ from odps import ODPS
 from config import set_config
 from db_helper import HologresHelper, MysqlHelper, RedisHelper
 from log import Log
+from collections import defaultdict
 
 config_, env = set_config()
 log_ = Log()
@@ -29,7 +30,20 @@ def execute_sql_from_odps(project, sql, connect_timeout=3000, read_timeout=50000
     )
     records = odps.execute_sql(sql=sql)
     return records
-
+def exe_sql(project, sql, connect_timeout=3000, read_timeout=500000,
+                          pool_maxsize=1000, pool_connections=1000):
+    odps = ODPS(
+        access_id=config_.ODPS_CONFIG['ACCESSID'],
+        secret_access_key=config_.ODPS_CONFIG['ACCESSKEY'],
+        project=project,
+        endpoint=config_.ODPS_CONFIG['ENDPOINT'],
+        connect_timeout=connect_timeout,
+        read_timeout=read_timeout,
+        pool_maxsize=pool_maxsize,
+        pool_connections=pool_connections
+    )
+    records = odps.execute_sql(sql)
+    return records
 
 def get_data_from_odps(date, project, table, connect_timeout=3000, read_timeout=500000,
                        pool_maxsize=1000, pool_connections=1000):