modify

evaluation_processing/evaluation_accuracy/.ipynb_checkpoints/evaluation_accuracy-checkpoint.py

@@ -162,10 +162,10 @@ def mae(y_true, y_pred):
     
 def compute_accuracy(df,args):
     col_time,col_rp,col_pp = args['col_time'],args['col_rp'],args['col_pp']
-    df['datetime'] = df[col_time].apply(lambda x:pd.to_datetime(x).strftime("%Y-%m-%d")) 
+    df[col_time] = df[col_time].apply(lambda x:pd.to_datetime(x).strftime("%Y-%m-%d")) 
     # 按日期分组并计算 RMSE 和 MAE
 
-    results = df.groupby('datetime').apply(
+    results = df.groupby(col_time).apply(
         lambda group: pd.Series({
             "RMSE": rmse(group[col_rp], group[col_pp]),
             "MAE": mae(group[col_rp], group[col_pp])

evaluation_processing/evaluation_accuracy/evaluation_accuracy.py

@@ -162,10 +162,10 @@ def mae(y_true, y_pred):
     
 def compute_accuracy(df,args):
     col_time,col_rp,col_pp = args['col_time'],args['col_rp'],args['col_pp']
-    df['datetime'] = df[col_time].apply(lambda x:pd.to_datetime(x).strftime("%Y-%m-%d")) 
+    df[col_time] = df[col_time].apply(lambda x:pd.to_datetime(x).strftime("%Y-%m-%d")) 
     # 按日期分组并计算 RMSE 和 MAE
 
-    results = df.groupby('datetime').apply(
+    results = df.groupby(col_time).apply(
         lambda group: pd.Series({
             "RMSE": rmse(group[col_rp], group[col_pp]),
             "MAE": mae(group[col_rp], group[col_pp])