多采用panda的数据处理方式

pandas和csv使用最为频繁，保存数据集时尽量使用csv存储，而不是txt

对于训练集中的数据，content，labels，将原始的list封装成dict，直接转换为dataFrame

data = pd.DataFrame({"samples":content, "labels":labels})

def generate_data(random_state = 24, is_pse_label=True):
    skf = StratifiedKFold(n_splits=5, shuffle=True, random_state=random_state)
    i = 0
    for train_index, dev_index in skf.split(X, y):
        print(i, "TRAIN:", train_index, "TEST:", dev_index)
        DATA_DIR = "./data_StratifiedKFold_{}/data_origin_{}/".format(random_state,i)
        if not os.path.exists(DATA_DIR):
            os.makedirs(DATA_DIR)
        tmp_train_df = train_df.iloc[train_index]
        
        tmp_dev_df = train_df.iloc[dev_index]
        
        test_df.to_csv(DATA_DIR+"test.csv")
        if is_pse_label:
            pse_dir = "data_pse_{}/".format(i)
            pse_df = pd.read_csv(pse_dir+'train.csv')

            tmp_train_df = pd.concat([tmp_train_df, pse_df],ignore_index=True,sort=False)
            
        tmp_train_df.to_csv(DATA_DIR + "train.csv")
        tmp_dev_df.to_csv(DATA_DIR+"dev.csv")
        print(tmp_train_df.shape, tmp_dev_df.shape)
        i+=1