03251035

models_processing/model_tf/tf_bp_pre.py

@@ -65,7 +65,7 @@ def model_prediction_bp():
 
         res = list(chain.from_iterable(target_scaler.inverse_transform(bp.predict(scaled_pre_x))))
         pre_data['farm_id'] = args.get('farm_id', 'null')
-        if args.get('algorithm_test', 0):
+        if int(args.get('algorithm_test', 0)):
             pre_data[args['model_name']] = res[:len(pre_data)]
             pre_data.rename(columns={args['col_time']: 'dateTime'}, inplace=True)
             pre_data = pre_data[['dateTime', 'farm_id', args['target'], args['model_name'], 'dq']]
@@ -80,7 +80,7 @@ def model_prediction_bp():
             res_cols = ['date_time', 'power_forecast', 'farm_id']
 
         pre_data = pre_data[res_cols]
-        pre_data['power_forecast'] = pre_data['power_forecast'].round(2)
+        pre_data.loc[:, 'power_forecast'] = pre_data['power_forecast'].round(2)
         pre_data.loc[pre_data['power_forecast'] > float(args['cap']), 'power_forecast'] = float(args['cap'])
         pre_data.loc[pre_data['power_forecast'] < 0, 'power_forecast'] = 0
 

models_processing/model_tf/tf_cnn_pre.py

@@ -66,7 +66,7 @@ def model_prediction_bp():
 
         res = list(chain.from_iterable(target_scaler.inverse_transform(cnn.predict(scaled_pre_x))))
         pre_data['farm_id'] = args.get('farm_id', 'null')
-        if args.get('algorithm_test', 0):
+        if int(args.get('algorithm_test', 0)):
             pre_data[args['model_name']] = res[:len(pre_data)]
             pre_data.rename(columns={args['col_time']: 'dateTime'}, inplace=True)
             pre_data = pre_data[['dateTime', 'farm_id', args['target'], args['model_name'], 'dq']]
@@ -81,7 +81,7 @@ def model_prediction_bp():
             res_cols = ['date_time', 'power_forecast', 'farm_id']
         pre_data = pre_data[res_cols]
 
-        pre_data['power_forecast'] = pre_data['power_forecast'].round(2)
+        pre_data.loc[:, 'power_forecast'] = pre_data['power_forecast'].round(2)
         pre_data.loc[pre_data['power_forecast'] > float(args['cap']), 'power_forecast'] = float(args['cap'])
         pre_data.loc[pre_data['power_forecast'] < 0, 'power_forecast'] = 0
 

models_processing/model_tf/tf_lstm_pre.py

@@ -63,7 +63,7 @@ def model_prediction_bp():
         scaled_pre_x, pre_data = dh.pre_data_handler(pre_data, feature_scaler)
         res = list(chain.from_iterable(target_scaler.inverse_transform(ts.predict(scaled_pre_x))))
         pre_data['farm_id'] = args.get('farm_id', 'null')
-        if args.get('algorithm_test', 0):
+        if int(args.get('algorithm_test', 0)):
             pre_data[args['model_name']] = res[:len(pre_data)]
             pre_data.rename(columns={args['col_time']: 'dateTime'}, inplace=True)
             pre_data = pre_data[['dateTime', 'farm_id', args['target'], args['model_name'], 'dq']]
@@ -78,7 +78,7 @@ def model_prediction_bp():
             res_cols = ['date_time', 'power_forecast', 'farm_id']
         pre_data = pre_data[res_cols]
 
-        pre_data['power_forecast'] = pre_data['power_forecast'].round(2)
+        pre_data.loc[:, 'power_forecast'] = pre_data['power_forecast'].round(2)
         pre_data.loc[pre_data['power_forecast'] > float(args['cap']), 'power_forecast'] = float(args['cap'])
         pre_data.loc[pre_data['power_forecast'] < 0, 'power_forecast'] = 0
 

models_processing/model_tf/tf_test_pre.py

@@ -65,7 +65,7 @@ def model_prediction_test():
 
         res = list(chain.from_iterable(target_scaler.inverse_transform(ts.predict(scaled_pre_x))))
         pre_data['farm_id'] = args.get('farm_id', 'null')
-        if args.get('algorithm_test', 0):
+        if int(args.get('algorithm_test', 0)):
             pre_data[args['model_name']] = res[:len(pre_data)]
             pre_data.rename(columns={args['col_time']: 'dateTime'}, inplace=True)
             pre_data = pre_data[['dateTime', 'farm_id', args['target'], args['model_name'], 'dq']]
@@ -79,7 +79,7 @@ def model_prediction_test():
             pre_data.rename(columns={args['col_time']: 'date_time'}, inplace=True)
             res_cols = ['date_time', 'power_forecast', 'farm_id']
         pre_data = pre_data[res_cols]
-        pre_data['power_forecast'] = pre_data['power_forecast'].round(2)
+        pre_data.loc[:, 'power_forecast'] = pre_data['power_forecast'].round(2)
         pre_data.loc[pre_data['power_forecast'] > float(args['cap']), 'power_forecast'] = float(args['cap'])
         pre_data.loc[pre_data['power_forecast'] < 0, 'power_forecast'] = 0
         insert_data_into_mongo(pre_data, args)