再次调整预测判定与投放过滤参与的阈值

data_preprocess.py

@@ -1291,7 +1291,7 @@ def predict_data_simple(df_input, group_route_str, output_dir, predict_dir=".",
                 )
                 df_match = df_drop_gap[
                     (df_drop_gap['pct_abs_gap'] <= pct_threshold)
-                    & (df_drop_gap['price_abs_gap'] <= 1.0)
+                    & (df_drop_gap['price_abs_gap'] <= 0.1)
                     & same_sign_mask
                 ].copy()
                 # df_match = df_drop_gap[(df_drop_gap['pct_abs_gap'] <= pct_threshold) & (df_drop_gap['price_abs_gap'] <= 1.0)].copy()
@@ -1409,7 +1409,7 @@ def predict_data_simple(df_input, group_route_str, output_dir, predict_dir=".",
                 )
                 df_match_1 = df_rise_gap_1[
                     (df_rise_gap_1['pct_abs_gap'] <= pct_threshold_1)
-                    & (df_rise_gap_1['price_abs_gap'] <= 1.0)
+                    & (df_rise_gap_1['price_abs_gap'] <= 0.1)
                     & same_sign_mask_1
                 ].copy()
                 # df_match_1 = df_rise_gap_1.loc[(df_rise_gap_1['pct_abs_gap'] <= pct_threshold_1) & (df_rise_gap_1['price_abs_gap'] <= 1.0)].copy()

descending_cabin_task.py

@@ -517,7 +517,7 @@ def _process_one_task(row, runner):
         return None
     
     drop_price_sample_size = int(task.get("drop_price_sample_size", "0"))
-    if drop_price_sample_size < 1:  # 丢弃历史降价样本数过少(小于1)的
+    if drop_price_sample_size < 2:  # 丢弃历史降价样本数过少(小于2)的
         return None
     
     flight_day = task.get("flight_day")