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@@ -18,17 +18,17 @@ class DataHandler(object):
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self.logger = logger
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self.opt = argparse.Namespace(**args)
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- def get_train_data(self, dfs, col_time, target, time_series=1):
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+ def get_train_data(self, dfs, col_time, target, time_series=1, lstm_type=1):
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train_x, valid_x, train_y, valid_y = [], [], [], []
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for i, df in enumerate(dfs, start=1):
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if len(df) < self.opt.Model["time_step"]:
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self.logger.info("特征处理-训练数据-不满足time_step")
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- if time_series == 2:
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+ if lstm_type == 2:
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datax, datay = self.get_timestep_features_lstm2(df, col_time, target, is_train=True)
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- elif time_series == 3:
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+ elif lstm_type == 3:
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datax, datay = self.get_timestep_features_bilstm(df, col_time, target, is_train=True)
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else:
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- datax, datay = self.get_timestep_features(df, col_time, target, is_train=True)
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+ datax, datay = self.get_timestep_features(df, col_time, target, is_train=True, time_series=time_series)
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if len(datax) < 10:
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self.logger.info("特征处理-训练数据-无法进行最小分割")
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continue
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@@ -74,18 +74,18 @@ class DataHandler(object):
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features_x = np.concatenate((features_x, np.expand_dims(df_repeated, 0)), axis=0)
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return features_x
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- def get_timestep_features(self, norm_data, col_time, target, is_train):
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+ def get_timestep_features(self, norm_data, col_time, target, is_train, time_series=1):
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"""
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步长分割数据，获取时序训练集
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"""
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time_step = self.opt.Model["time_step"]
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feature_data = norm_data.reset_index(drop=True)
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- time_step_loc = time_step - 1
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+ time_step_loc = time_step*time_series - 1
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train_num = int(len(feature_data))
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label_features = [col_time, target] if is_train is True else [col_time, target]
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nwp_cs = self.opt.features
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- nwp = [feature_data.loc[i:i + time_step_loc, nwp_cs].reset_index(drop=True) for i in range(train_num - time_step + 1)] # 数据库字段 'C_T': 'C_WS170'
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- labels = [feature_data.loc[i:i + time_step_loc, label_features].reset_index(drop=True) for i in range(train_num - time_step + 1)]
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+ nwp = [feature_data.loc[i:i + time_step_loc, nwp_cs].reset_index(drop=True) for i in range(train_num - time_step*time_series + 1)] # 数据库字段 'C_T': 'C_WS170'
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+ labels = [feature_data.loc[i:i + time_step_loc, label_features].reset_index(drop=True) for i in range(train_num - time_step*time_series + 1)]
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features_x, features_y = [], []
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for i, row in enumerate(zip(nwp, labels)):
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features_x.append(row[0])
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David