feat:添加脚本

XGB/vov_xgboost_train.py

@@ -247,7 +247,7 @@ def xgb_train_multi_dt_data(t_1_label_dt: datetime):
     :return:
     """
     with concurrent.futures.ThreadPoolExecutor(3) as executor:
-        t_1_feature_dt = t_1_label_dt - timedelta(1)
+        t_1_feature_dt = t_1_label_dt - timedelta(2)
         logger.info(
             f"VOV模型特征数据处理 --- t_1_label_future:"
             f"\t label_datetime: {t_1_label_dt.strftime('%Y%m%d')} "
@@ -290,18 +290,19 @@ def xgb_predict_dt_data(label_datetime: datetime):
     :return:
     """
     feature_start_datetime = label_datetime
+    view_rate_datetime = label_datetime + timedelta(2)
     logger.info(
         f"VOV模型预测数据处理 --- predict_df: "
         f"\t label_datetime: {label_datetime.strftime('%Y%m%d')} "
         f"\t feature_datetime: {feature_start_datetime.strftime('%Y%m%d')} "
-        f"\t view_rate_datetime: {label_datetime.strftime('%Y%m%d')} "
+        f"\t view_rate_datetime: {view_rate_datetime.strftime('%Y%m%d')} "
     )
-    return fetch_data(label_datetime, feature_start_datetime, label_datetime)
+    return fetch_data(label_datetime, feature_start_datetime, view_rate_datetime)
 
 
 def _main():
     logger.info(f"XGB模型训练")
-    train_df = xgb_train_multi_dt_data((datetime.now() - timedelta(days=1)))
+    train_df = xgb_train_multi_dt_data((datetime.now() - timedelta(days=4)))
     trains_array = train_df[features_name].values
     trains_label_array = train_df['label'].values
 
@@ -323,7 +324,7 @@ def _main():
     model.fit(trains_array, trains_label_array)
 
     logger.info("获取评测数据")
-    predict_df = xgb_predict_dt_data((datetime.now() - timedelta(days=1)))
+    predict_df = xgb_predict_dt_data((datetime.now() - timedelta(days=3)))
     tests_array = predict_df[features_name].values
     y_pred = model.predict_proba(tests_array)[:, 1]
     predict_df["y_pred"] = y_pred
@@ -387,15 +388,15 @@ def _main():
     )
 
     # 写入Redis
-    redis_key = f"redis:lower_vov_vid:{datetime.now().strftime('%Y%m%d')}"
-
-    logger.info(f"当前环境为: {config_manager.get_env()}, 要写入的Redis Key为: {redis_key}")
-    host, port, password = config_manager.get_algorithm_redis_info()
-    alg_redis = RedisHelper.RedisHelper(host=host, port=port, password=password)
-    for vid in filtered_vid.tolist():
-        alg_redis.add_number_to_set(redis_key, vid)
-
-    alg_redis.set_expire(redis_key, 86400)
+    # redis_key = f"redis:lower_vov_vid:{datetime.now().strftime('%Y%m%d')}"
+    #
+    # logger.info(f"当前环境为: {config_manager.get_env()}, 要写入的Redis Key为: {redis_key}")
+    # host, port, password = config_manager.get_algorithm_redis_info()
+    # alg_redis = RedisHelper.RedisHelper(host=host, port=port, password=password)
+    # for vid in filtered_vid.tolist():
+    #     alg_redis.add_number_to_set(redis_key, vid)
+    #
+    # alg_redis.set_expire(redis_key, 86400)
 
 
 if __name__ == '__main__':
@@ -438,8 +439,9 @@ if __name__ == '__main__':
         card_json['i18n_header']['zh_cn']['template'] = "red"
         card_json['i18n_header']['zh_cn']["title"]['content'] = "XGB模型训练预测失败"
         card_json['i18n_elements']['zh_cn'][0]['content'] = msg_text
-    # 发送通知
-    feishu_inform_util.send_card_msg_to_feishu(
-        webhook=config_manager.get_vov_model_inform_feishu_webhook(),
-        card_json=card_json
-    )
+    if config_manager.get_env() == "pro":
+        # 发送通知
+        feishu_inform_util.send_card_msg_to_feishu(
+            webhook=config_manager.get_vov_model_inform_feishu_webhook(),
+            card_json=card_json
+        )