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# flake8: noqa: E501
import csv
import os
import os.path as osp
import re
from collections import defaultdict
from datetime import datetime
from itertools import product
import mmengine
from mmengine import ConfigDict
try:
from prettytable import from_csv
except ImportError:
from_csv = None
from opencompass.partitioners.sub_naive import remove_duplicate_pairs
from opencompass.utils import dataset_abbr_from_cfg, model_abbr_from_cfg
def match_general_answer(s):
temp = s[0]
if temp in ['A', 'B', 'C', 'D']:
return temp
else:
return None
def match_GPT4_answer(s):
if result := re.findall('(?:选择：|Choice: )([ABCD])', s):
return result[0]
else:
return None
judge_map = {'smart': match_GPT4_answer, 'other': match_general_answer}
def call_function(name, arg):
if name in judge_map:
return judge_map[name](arg)
else:
print('Function not found in the map.')
class Corev2Summarizer:
"""Do the subjectivity analyze based on evaluation results.
Args:
config (ConfigDict): The configuration object of the evaluation task.
It's expected to be filled out at runtime.
"""
def __init__(self, config: ConfigDict, match_method='smart') -> None:
self.tasks = []
self.cfg = config
self.match_method = match_method
self.base_models = self.cfg['eval']['partitioner']['base_models']
self.compare_models = self.cfg['eval']['partitioner']['compare_models']
self.judge_abbr = model_abbr_from_cfg(self.cfg['judge_model'])
def summarize(self,
time_str: str = datetime.now().strftime('%Y%m%d_%H%M%S')):
"""Summarize the subjectivity analysis based on evaluation results.
Args:
time_str (str): Timestamp for file naming.
Returns:
pd.DataFrame: The summary results.
"""
dataset_cfgs = self.cfg['datasets']
work_dir = self.cfg['work_dir']
self.work_dir = work_dir
self.time_str = time_str
output_path = osp.join(self.work_dir, 'summary',
f'summary_{self.time_str}.txt')
output_dir = osp.join(osp.split(output_path)[0], f'{self.time_str}')
mmengine.mkdir_or_exist(output_dir)
results_folder = osp.join(work_dir, 'results')
model_combinations = list(
product(self.base_models, self.compare_models))
unique_combinations = remove_duplicate_pairs(
[combo for combo in model_combinations if combo[0] != combo[1]])
for model_pair in unique_combinations:
model1, model2, judge_model = model_pair[0]['abbr'], model_pair[1][
'abbr'], self.judge_abbr
subdir = model1 + '_' + model2 + '_judged-by--' + self.judge_abbr
subdir_path = os.path.join(results_folder, subdir)
if os.path.isdir(subdir_path):
fout = osp.join(output_dir,
'judged-by--' + judge_model + '-report.csv')
for dataset in dataset_cfgs:
dataset_abbr = dataset_abbr_from_cfg(dataset)
filename = os.path.join(subdir_path,
dataset_abbr + '.json')
partial_filename = os.path.join(subdir_path,
dataset_abbr + '_0.json')
if osp.exists(osp.realpath(filename)):
result = mmengine.load(filename)
elif osp.exists(osp.realpath(partial_filename)):
filename = partial_filename
result = {}
i = 1
partial_dict_flag = 0
while osp.exists(osp.realpath(filename)):
res = mmengine.load(filename)
for k, v in res.items():
result[partial_dict_flag] = v
partial_dict_flag += 1
filename = os.path.join(
subdir_path,
dataset_abbr + '_' + str(i) + '.json')
i += 1
else:
result = {}
if len(result) == 0:
print('*' * 100)
print('There are no results for ' + filename + ' or ' +
partial_filename)
print('*' * 100)
assert len(result) > 0
judged_answers = []
references = []
for k, v in result.items():
judged_answers.append(
call_function(self.match_method, v['prediction']))
references.append(v['gold'])
successful_judged_answers = len(
judged_answers) - judged_answers.count(None)
print(
f'Among {len(judged_answers)} judgements, successfully extracted {successful_judged_answers} judgements.'
)
if successful_judged_answers == 0:
print('*' * 100)
print(
'There are no extracted judgements, please change your judge model or check your prompt!!!'
)
print('*' * 100)
assert successful_judged_answers > 0
win_both_model1, win_both_model2, half_draw_model1, half_draw_model2, categories = defaultdict(
float), defaultdict(float), defaultdict(
float), defaultdict(float), defaultdict(float)
model1 = references[0]['answer1']
model2 = references[0]['answer2']
for prediction, reference in zip(judged_answers,
references):
if prediction is not None:
categories[reference['capability'].split('-')
[0]] += 1
categories[reference['capability']] += 1
winner = ''
if prediction == 'A':
winner = reference['answer1']
elif prediction == 'B':
winner = reference['answer2']
elif prediction == 'C':
win_both_model1[reference['capability'].split(
'-')[0]] += 1
win_both_model2[reference['capability'].split(
'-')[0]] += 1
win_both_model1[reference['capability']] += 1
win_both_model2[reference['capability']] += 1
if model1 == winner:
half_draw_model1[reference['capability'].split(
'-')[0]] += 1
win_both_model1[reference['capability'].split(
'-')[0]] += 1
half_draw_model1[reference['capability']] += 1
win_both_model1[reference['capability']] += 1
elif model2 == winner:
half_draw_model2[reference['capability'].split(
'-')[0]] += 1
win_both_model2[reference['capability'].split(
'-')[0]] += 1
half_draw_model2[reference['capability']] += 1
win_both_model2[reference['capability']] += 1
for capability in categories:
if capability not in half_draw_model1:
win_both_model1[capability] = 0.0
half_draw_model1[capability] = 0.0
else:
win_both_model1[capability] = round(
(win_both_model1[capability] /
categories[capability]) * 100, 2)
half_draw_model1[capability] = round(
(half_draw_model1[capability] /
categories[capability]) * 100, 2)
if capability not in half_draw_model2:
win_both_model2[capability] = 0.0
half_draw_model2[capability] = 0.0
else:
win_both_model2[capability] = round(
(win_both_model2[capability] /
categories[capability]) * 100, 2)
half_draw_model2[capability] = round(
(half_draw_model2[capability] /
categories[capability]) * 100, 2)
scores = {
'win_both_' + model1: win_both_model1,
'half_draw_' + model1: half_draw_model1,
'win_both_' + model2: win_both_model2,
'half_draw_' + model2: half_draw_model2
}
rows = list(scores.keys())
columns = list(scores[rows[0]].keys())
with open(fout, 'a+', newline='') as csvfile:
writer = csv.writer(csvfile)
writer.writerow([model1 + '_vs_' + model2] + columns)
for row in rows:
writer.writerow(
[row] +
[scores[row][column] for column in columns])
else:
print(subdir_path + ' is not exist! please check!')
with open(fout, 'r') as f:
x = from_csv(f)
print(x)
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