GeoLLM / Task2 /split_data_Geo.py
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# 数据读取和划分
# 读取./Task2.xlsx文件, 其中"Yes or No"sheet中为判断数据集,"Factoid"sheet中为填空数据集
# 两类数据均有200条,列名为"序号"、"问题"、"答案"、文本
# 每大类的200条数据分为5小类问题,每类40条,按序号顺序依次排列(如地质灾害发育特征、地质灾害经济损失评估等)
# 考虑到构建数据集的时候,连续相邻问题的文本可能来源于同一段,因此切分测试集和训练集的时候不能直接按原始顺序划分,要随机抽取构建初始数据集。
# 随机抽取时,每小类20条测试+20训练条训练(每大类共计100测试100训练)。
def split_data(data_path, output_path):
import pandas as pd
import random
# 读取数据
data_yes_no = pd.read_excel(data_path, sheet_name='Yes or No')
data_factoid = pd.read_excel(data_path, sheet_name='Factoid')
# 划分数据 (Yes or No)
train_data_yes_no = []
test_data_yes_no = []
for category in range(5):
category_data = data_yes_no[category * 40:(category + 1) * 40]
random.seed(42)
train = category_data.sample(n=20, random_state=42)
test = category_data.drop(train.index)
train_data_yes_no.append(train)
test_data_yes_no.append(test)
# 划分数据 (Factoid)
train_data_factoid = []
test_data_factoid = []
for category in range(5):
category_data = data_factoid[category * 40:(category + 1) * 40]
random.seed(42)
train = category_data.sample(n=20, random_state=42)
test = category_data.drop(train.index)
train_data_factoid.append(train)
test_data_factoid.append(test)
# 合并两个sheet的数据
train_data_yes_no = pd.concat(train_data_yes_no)
test_data_yes_no = pd.concat(test_data_yes_no)
train_data_factoid = pd.concat(train_data_factoid)
test_data_factoid = pd.concat(test_data_factoid)
# 创建一个 Excel writer 对象
writer = pd.ExcelWriter(output_path, engine='xlsxwriter')
# 将数据写入不同的 sheet
train_data_yes_no.to_excel(writer, sheet_name='Yes or No Train', index=False)
test_data_yes_no.to_excel(writer, sheet_name='Yes or No Test', index=False)
train_data_factoid.to_excel(writer, sheet_name='Factoid Train', index=False)
test_data_factoid.to_excel(writer, sheet_name='Factoid Test', index=False)
# 保存 Excel 文件
writer.close()
print(f"数据已保存到 {output_path}")
if __name__ == "__main__":
import pandas as pd
import random
data_path = './data/Task2.xlsx'
output_path = './data/train_test_data.xlsx'
split_data(data_path, output_path)