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import copy
import numpy as np
import pandas as pd
def process_plot_data(df, flag=False):
# 保留"Model"和"Domain"列，删除其他列
df2 = df[["Model", "Domain"]].copy()
columns_names = ["Model", "Domain", "AR", "ER", "NER", "JS", "CR", "CFM", "SCM",
"CJP", "CTP", "LQA", "JRG", "CU", "LC", "JRG-TAG", "LC-TAG"]
# 计算新的列的值
for col in columns_names[2:]:
if col in ["AR", "ER", "CR", "CFM", "SCM", "CTP", "LQA"]:
df2[col] = df[f"{col}-F1"] * 100
if col == "CJP":
df2[col] = df[[f"{col}-CP-F1", f"{col}-PTP-F1"]].mean(axis=1) * 100
if col == "NER":
df2[col] = df[f"{col}-Acc"] * 100
if col in ["JRG", "LC", "JS", "CU", "JRG-TAG", "LC-TAG"]:
rouge_mean = df[[f"{col}-ROUGE-1", f"{col}-ROUGE-2", f"{col}-ROUGE-L"]].replace('-', np.nan).mean(axis=1)
df2.loc[df[f"{col}-ROUGE-1"] == '-', col] = '-'
df2.loc[df[f"{col}-ROUGE-1"] != '-', col] = rouge_mean * 100
# if col in ["JS", "CU"]:
# df2[col] = df[[f"{col}-ROUGE-1", f"{col}-ROUGE-2", f"{col}-ROUGE-L"]].mean(axis=1) * 100
df2.reindex(columns=columns_names)
flag = True
if flag:
# 保存到Excel文件
with pd.ExcelWriter('scores.xlsx') as writer:
df2.to_excel(writer, sheet_name="Sheet1", index=False)
return df2
def plot_data():
# read df and replace NaN values with an empty string
leaderboard_df = pd.read_excel(
'leaderboard.xlsx',
sheet_name='Sheet2',
header=0,
usecols='A:BE',
nrows=18)
leaderboard_df.fillna("-")
df = process_plot_data(leaderboard_df)
# todo
df.drop(df[df['Model'] == 'Baichuan-7B'].index, inplace=True)
df_nlp = df.iloc[:, [0] + list(range(2, 7))] # todo
df_basic = df.iloc[:, [0] + list(range(7, 12))] # todo
df_complex = df.iloc[:, [0] + list(range(12, 15))] # todo
# Get df_overall
df_overall = df.iloc[:, [0] + list(range(2, 15))]
plot_df_dict = {
"Overall": df_overall,
"Basic Legal NLP": df_nlp,
"Basic Legal Application": df_basic,
"Complex Legal Application": df_complex,
}
return plot_df_dict
def tab_data():
# read df and replace NaN values with an empty string
leaderboard_df = pd.read_excel(
'leaderboard.xlsx',
sheet_name='Sheet2',
header=0,
usecols='A:BE',
nrows=18)
leaderboard_df.fillna("-")
# todo
leaderboard_df.drop(leaderboard_df[leaderboard_df['Model'] == 'Baichuan-7B'].index, inplace=True)
df_nlp = leaderboard_df.iloc[:, list(range(0, 18))] # todo
df_basic = leaderboard_df.iloc[:, list(range(0, 2)) + list(range(18, 42))] # todo
df_complex = leaderboard_df.iloc[:, list(range(0, 2)) + list(range(42, 56))] # todo
# Get df_overall
df_overall = leaderboard_df.iloc[:, list(range(0, 56))]
plot_df_dict = {
"Overall": df_overall,
"Basic Legal NLP": df_nlp,
"Basic Legal Application": df_basic,
"Complex Legal Application": df_complex,
}
return plot_df_dict
if __name__ == "__main__":
df1 = plot_data()
df2 = tab_data()
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