05060958

models_processing/model_tf/tf_lstm_pre.py

@@ -36,6 +36,7 @@ def update_config():
     request_args = request.values.to_dict()
     # features参数规则：1.有传入，解析，覆盖 2. 无传入，不覆盖，原始值
     request_args['features'] = request_args['features'].split(',') if 'features' in request_args else current_config['features']
+    request_args['time_series'] = request_args.get('time_series', 1)
     current_config.update(request_args)
 
     # 存储到请求上下文
@@ -62,7 +63,7 @@ def model_prediction_bp():
         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)['Model']['features'].split(',')
-        scaled_pre_x, pre_data = dh.pre_data_handler(pre_data, feature_scaler)
+        scaled_pre_x, pre_data = dh.pre_data_handler(pre_data, feature_scaler, time_series=args['time_series'])
         res = list(chain.from_iterable(target_scaler.inverse_transform(ts.predict(scaled_pre_x))))
         pre_data['farm_id'] = args.get('farm_id', 'null')
         if int(args.get('algorithm_test', 0)):

models_processing/model_tf/tf_lstm_train.py

@@ -31,6 +31,7 @@ def update_config():
     request_args = request.values.to_dict()
     # features参数规则：1.有传入，解析，覆盖 2. 无传入，不覆盖，原始值
     request_args['features'] = request_args['features'].split(',') if 'features' in request_args else current_config['features']
+    request_args['time_series'] = request_args.get('time_series', 1)
     current_config.update(request_args)
 
     # 存储到请求上下文
@@ -52,24 +53,24 @@ def model_training_bp():
     try:
         # ------------ 获取数据，预处理训练数据 ------------
         train_data = get_data_from_mongo(args)
-        train_x, train_y, valid_x, valid_y, scaled_train_bytes, scaled_target_bytes, scaled_cap = dh.train_data_handler(train_data)
+        train_x, train_y, valid_x, valid_y, scaled_train_bytes, scaled_target_bytes, scaled_cap = dh.train_data_handler(train_data, time_series=args['time_series'])
         ts.opt.cap = round(scaled_cap, 2)
         ts.opt.Model['input_size'] = len(dh.opt.features)
         # ------------ 训练模型，保存模型 ------------
         # 1. 如果是加强训练模式，先加载预训练模型特征参数，再预处理训练数据
         # 2. 如果是普通模式，先预处理训练数据，再根据训练数据特征加载模型
-        model = ts.train_init() if ts.opt.Model['add_train'] else ts.get_keras_model(ts.opt)
+        model = ts.train_init() if ts.opt.Model['add_train'] else ts.get_keras_model(ts.opt, time_series=args['time_series'])
         if ts.opt.Model['add_train']:
             if model:
                 feas = json.loads(ts.model_params)['features']
                 if set(feas).issubset(set(dh.opt.features)):
                     dh.opt.features = list(feas)
-                    train_x, train_y, valid_x, valid_y, scaled_train_bytes, scaled_target_bytes, scaled_cap = dh.train_data_handler(train_data)
+                    train_x, train_y, valid_x, valid_y, scaled_train_bytes, scaled_target_bytes, scaled_cap = dh.train_data_handler(train_data, time_series=args['time_series'])
                 else:
-                    model = ts.get_keras_model(ts.opt)
+                    model = ts.get_keras_model(ts.opt, time_series=args['time_series'])
                     logger.info("训练数据特征，不满足，加强训练模型特征")
             else:
-                model = ts.get_keras_model(ts.opt)
+                model = ts.get_keras_model(ts.opt, time_series=args['time_series'])
         ts_model = ts.training(model, [train_x, train_y, valid_x, valid_y])
         args['Model']['features'] = ','.join(dh.opt.features)
         args['params'] = json.dumps(args)