03250953

models_processing/model_tf/tf_bp_pre.py

@@ -60,7 +60,7 @@ def model_prediction_bp():
         bp.opt.cap = round(target_scaler.transform(np.array([[float(args['cap'])]]))[0, 0], 2)
         # ------------ 获取模型，预测结果------------
         bp.get_model(args)
-        dh.opt.features = json.loads(bp.model_params).get('Model').get('features', ','.join(bp.opt.features)).split(',')
+        dh.opt.features = json.loads(bp.model_params)['Model']['features'].split(',')
         scaled_pre_x, pre_data = dh.pre_data_handler(pre_data, feature_scaler, bp_data=True)
 
         res = list(chain.from_iterable(target_scaler.inverse_transform(bp.predict(scaled_pre_x))))

models_processing/model_tf/tf_cnn_pre.py

@@ -60,7 +60,7 @@ def model_prediction_bp():
         cnn.opt.cap = round(target_scaler.transform(np.array([[float(args['cap'])]]))[0, 0], 2)
 
         cnn.get_model(args)
-        dh.opt.features = json.loads(cnn.model_params).get('Model').get('features', ','.join(cnn.opt.features)).split(',')
+        dh.opt.features = json.loads(cnn.model_params)['Model']['features'].split(',')
         scaled_pre_x, pre_data = dh.pre_data_handler(pre_data, feature_scaler)
         logger.info("---------cap归一化：{}".format(cnn.opt.cap))
 

models_processing/model_tf/tf_lstm_pre.py

@@ -59,7 +59,7 @@ def model_prediction_bp():
         feature_scaler, target_scaler = get_scaler_model_from_mongo(args)
         ts.opt.cap = round(target_scaler.transform(np.array([[float(args['cap'])]]))[0, 0], 2)
         ts.get_model(args)
-        dh.opt.features = json.loads(ts.model_params).get('Model').get('features', ','.join(ts.opt.features)).split(',')
+        dh.opt.features = json.loads(ts.model_params)['Model']['features'].split(',')
         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')

models_processing/model_tf/tf_test_pre.py

@@ -60,7 +60,7 @@ def model_prediction_test():
         ts.opt.cap = round(target_scaler.transform(np.array([[float(args['cap'])]]))[0, 0], 2)
 
         ts.get_model(args)
-        dh.opt.features = json.loads(ts.model_params).get('Model').get('features', ','.join(ts.opt.features)).split(',')
+        dh.opt.features = json.loads(ts.model_params)['Model']['features'].split(',')
         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))))