feat:修改日志

XGB/vov_xgboost_train.py

@@ -229,19 +229,42 @@ def fetch_data(label_datetime: datetime, feature_start_datetime: datetime, view_
     return df
 
 
-def xgb_multi_dt_data(t_1_label_dt: datetime):
+def xgb_train_multi_dt_data(t_1_label_dt: datetime):
+    """
+    XGB模型多天训练数据
+    :param t_1_label_dt:
+    :return:
+    """
     with concurrent.futures.ThreadPoolExecutor(3) as executor:
-        logger.info(f"VOV模型特征数据处理:t_1_label_future.label_datetime: {t_1_label_dt.strftime('%Y%m%d')}")
-        t_1_label_future = executor.submit(fetch_data, t_1_label_dt, t_1_label_dt - timedelta(1), t_1_label_dt)
+        t_1_feature_dt = t_1_label_dt - timedelta(1)
+        logger.info(
+            f"VOV模型特征数据处理 --- t_1_label_future:"
+            f"label_datetime: {t_1_label_dt.strftime('%Y%m%d')}"
+            f"feature_datetime: {t_1_feature_dt.strftime('%Y%m%d')}"
+            f"view_rate_datetime: {t_1_label_dt.strftime('%Y%m%d')}"
+        )
+        t_1_label_future = executor.submit(fetch_data, t_1_label_dt, t_1_feature_dt, t_1_label_dt)
 
         t_2_label_dt = t_1_label_dt - timedelta(1)
-        logger.info(f"VOV模型特征数据处理:t_2_label_future.label_datetime: {t_2_label_dt.strftime('%Y%m%d')}")
-        t_2_label_future = executor.submit(fetch_data, t_2_label_dt, t_2_label_dt - timedelta(1), t_2_label_dt)
+        t_2_feature_dt = t_2_label_dt - timedelta(1)
+        logger.info(
+            f"VOV模型特征数据处理 --- t_2_label_future:"
+            f"label_datetime: {t_2_label_dt.strftime('%Y%m%d')}"
+            f"feature_datetime: {t_2_feature_dt.strftime('%Y%m%d')}"
+            f"view_rate_datetime: {t_2_label_dt.strftime('%Y%m%d')}"
+        )
+        t_2_label_future = executor.submit(fetch_data, t_2_label_dt, t_2_feature_dt, t_2_label_dt)
 
         t_3_label_dt = t_1_label_dt - timedelta(2)
-        logger.info(f"VOV模型特征数据处理:t_3_label_future.label_datetime: {t_3_label_dt.strftime('%Y%m%d')}")
+        t_3_feature_dt = t_3_label_dt - timedelta(1)
+        logger.info(
+            f"VOV模型特征数据处理 --- t_3_label_future:"
+            f"label_datetime: {t_3_label_dt.strftime('%Y%m%d')}"
+            f"feature_datetime: {t_3_feature_dt.strftime('%Y%m%d')}"
+            f"view_rate_datetime: {t_3_label_dt.strftime('%Y%m%d')}"
+        )
+        t_3_label_future = executor.submit(fetch_data, t_3_label_dt, t_3_feature_dt, t_3_label_dt)
 
-        t_3_label_future = executor.submit(fetch_data, t_3_label_dt, t_3_label_dt - timedelta(1), t_3_label_dt)
         t_1_label_df = t_1_label_future.result()
         t_2_label_df = t_2_label_future.result()
         t_3_label_df = t_3_label_future.result()
@@ -249,9 +272,25 @@ def xgb_multi_dt_data(t_1_label_dt: datetime):
     return pd.concat([t_1_label_df, t_2_label_df, t_3_label_df], ignore_index=True)
 
 
+def xgb_predict_dt_data(label_datetime: datetime):
+    """
+    获取预估数据
+    :param label_datetime:
+    :return:
+    """
+    feature_start_datetime = label_datetime
+    logger.info(
+        f"VOV模型预测数据处理 --- predict_df: "
+        f"label_datetime: {label_datetime.strftime('%Y%m%d')}"
+        f"feature_datetime: {feature_start_datetime.strftime('%Y%m%d')}"
+        f"view_rate_datetime: {label_datetime.strftime('%Y%m%d')}"
+    )
+    return fetch_data(label_datetime, feature_start_datetime, label_datetime)
+
+
 def _main():
     logger.info(f"XGB模型训练")
-    train_df = xgb_multi_dt_data((datetime.now() - timedelta(days=1)))
+    train_df = xgb_train_multi_dt_data((datetime.now() - timedelta(days=1)))
     trains_array = train_df[features_name].values
     trains_label_array = train_df['label'].values
 
@@ -273,10 +312,7 @@ def _main():
     model.fit(trains_array, trains_label_array)
 
     logger.info("获取评测数据")
-    start_label_datetime = datetime.now() - timedelta(days=1)
-    feature_start_datetime = start_label_datetime
-
-    predict_df = fetch_data(start_label_datetime, feature_start_datetime, start_label_datetime)
+    predict_df = xgb_predict_dt_data((datetime.now() - timedelta(days=1)))
     tests_array = predict_df[features_name].values
     y_pred = model.predict_proba(tests_array)[:, 1]
     predict_df["y_pred"] = y_pred