Merge branch 'dev_david' of anweiguo/algorithm_platform into dev_awg

models_processing/model_koi/tf_bp_pre.py

@@ -62,10 +62,13 @@ def model_prediction_bp():
         pre_data['cdq'] = args.get('cdq', 1)
         pre_data['dq'] = args.get('dq', 1)
         pre_data['zq'] = args.get('zq', 1)
-        pre_data['model'] = 'bp'
         res_cols = ['date_time', 'power_forecast', 'farm_id', 'cdq', 'dq', 'zq']
-        res_cols += [args['target'], 'model'] if args['algorithm_test'] else res_cols
-        pre_data.rename(columns={args['col_time']: 'date_time'}, inplace=True)
+        if args.get('algorithm_test', 0):
+            pre_data['model'] = 'lstm'
+            res_cols += [args['target'], 'model']
+            pre_data.rename(columns={args['col_time']: 'dateTime'}, inplace=True)
+        else:
+            pre_data.rename(columns={args['col_time']: 'date_time'}, inplace=True)
         pre_data = pre_data[res_cols]
 
         pre_data['power_forecast'] = pre_data['power_forecast'].round(2)

models_processing/model_koi/tf_cnn_pre.py

@@ -63,10 +63,13 @@ def model_prediction_bp():
         pre_data['cdq'] = args.get('cdq', 1)
         pre_data['dq'] = args.get('dq', 1)
         pre_data['zq'] = args.get('zq', 1)
-        pre_data['model'] = 'cnn'
         res_cols = ['date_time', 'power_forecast', 'farm_id', 'cdq', 'dq', 'zq']
-        res_cols += [args['target'], 'model'] if args['algorithm_test'] else res_cols
-        pre_data.rename(columns={args['col_time']: 'date_time'}, inplace=True)
+        if args.get('algorithm_test', 0):
+            pre_data['model'] = 'cnn'
+            res_cols += [args['target'], 'model']
+            pre_data.rename(columns={args['col_time']: 'dateTime'}, inplace=True)
+        else:
+            pre_data.rename(columns={args['col_time']: 'date_time'}, inplace=True)
         pre_data = pre_data[res_cols]
 
         pre_data['power_forecast'] = pre_data['power_forecast'].round(2)

models_processing/model_koi/tf_lstm_pre.py

@@ -62,10 +62,13 @@ def model_prediction_bp():
         pre_data['cdq'] = args.get('cdq', 1)
         pre_data['dq'] = args.get('dq', 1)
         pre_data['zq'] = args.get('zq', 1)
-        pre_data['model'] = 'lstm'
         res_cols = ['date_time', 'power_forecast', 'farm_id', 'cdq', 'dq', 'zq']
-        res_cols += [args['target'], 'model'] if args['algorithm_test'] else res_cols
-        pre_data.rename(columns={args['col_time']: 'date_time'}, inplace=True)
+        if args.get('algorithm_test', 0):
+            pre_data['model'] = 'lstm'
+            res_cols += [args['target'], 'model']
+            pre_data.rename(columns={args['col_time']: 'dateTime'}, inplace=True)
+        else:
+            pre_data.rename(columns={args['col_time']: 'date_time'}, inplace=True)
         pre_data = pre_data[res_cols]
 
         pre_data['power_forecast'] = pre_data['power_forecast'].round(2)