Update inference.py

inference.py

@@ -62,14 +62,16 @@ def load_model_and_scaler(args):
 def predict_future(args, model, scaler, device):
     df_input = pd.read_csv(args.predict_input_file)
     df_input['date'] = pd.to_datetime(df_input['date'])
+
     cols_to_scale = ['air_pres', 'wind_dir', 'wind_speed', 'air_temp', 'residual']
+    
     raw_input = df_input[cols_to_scale].tail(args.seq_len).values
     input_scaled = scaler.transform(raw_input)
     batch_x = torch.from_numpy(input_scaled).float().unsqueeze(0).to(device)
     
     df_stamp_enc = df_input.tail(args.seq_len)[['date']].reset_index(drop=True)
-    # ⭐️ 1번 문제 해결: 불필요한 timeenc=0 인자 제거
-    enc_mark = time_features(df_stamp_enc, freq=args.freq)
+    # ⭐️ 수정: DataFrame[['date']] 대신 Series['date']를 전달
+    enc_mark = time_features(pd.to_datetime(df_stamp_enc['date']), freq=args.freq)
     batch_x_mark = torch.from_numpy(enc_mark).float().unsqueeze(0).to(device)
     
     dec_inp_label = input_scaled[-args.label_len:]
@@ -80,8 +82,8 @@ def predict_future(args, model, scaler, device):
     last_date = df_stamp_enc['date'].iloc[-1]
     future_dates = pd.date_range(start=last_date, periods=args.pred_len + 1, freq='5T')[1:]
     df_stamp_dec = pd.DataFrame({'date': list(df_stamp_enc['date'].values[-args.label_len:]) + list(future_dates)})
-    # ⭐️ 1번 문제 해결: 불필요한 timeenc=0 인자 제거
-    dec_mark = time_features(df_stamp_dec, freq=args.freq)
+    # ⭐️ 수정: DataFrame[['date']] 대신 Series['date']를 전달
+    dec_mark = time_features(pd.to_datetime(df_stamp_dec['date']), freq=args.freq)
     batch_y_mark = torch.from_numpy(dec_mark).float().unsqueeze(0).to(device)
 
     with torch.no_grad():
@@ -106,15 +108,14 @@ def evaluate_performance(args, model, scaler, device):
     raw_data = df_eval[cols_to_scale].values
     data_scaled = scaler.transform(raw_data)
     
-    # ⭐️ 1번 문제 해결: 불필요한 timeenc=0 인자 제거
-    df_stamp = time_features(df_eval[['date']], freq=args.freq)
+    # ⭐️ 수정: DataFrame[['date']] 대신 Series['date']를 전달
+    df_stamp = time_features(pd.to_datetime(df_eval['date']), freq=args.freq)
 
     preds_unscaled = []
     trues_unscaled = []
 
     num_samples = len(data_scaled) - args.seq_len - args.pred_len + 1
     for i in tqdm(range(num_samples), desc="Evaluating", file=sys.stderr):
-        # ... (이하 로직은 이전과 동일, 수정 없음) ...
         s_begin = i
         s_end = s_begin + args.seq_len
         batch_x = data_scaled[s_begin:s_end]
@@ -146,7 +147,7 @@ def evaluate_performance(args, model, scaler, device):
         trues_unscaled.append(true_unscaled)
 
     return np.array(preds_unscaled), np.array(trues_unscaled)
-
+    
 if __name__ == '__main__':
     
     final_output = {}