mirror of
https://github.com/open-compass/opencompass.git
synced 2025-05-30 16:03:24 +08:00
139 lines
4.4 KiB
Python
139 lines
4.4 KiB
Python
# flake8: noqa: E501
|
|
import csv
|
|
import os
|
|
import os.path as osp
|
|
import re
|
|
from collections import defaultdict
|
|
from datetime import datetime
|
|
|
|
import numpy as np
|
|
from mmengine import ConfigDict
|
|
|
|
try:
|
|
from prettytable import from_csv
|
|
except ImportError:
|
|
from_csv = None
|
|
|
|
from opencompass.utils import model_abbr_from_cfg
|
|
|
|
from .subjective_post_process import post_process_autoj
|
|
from .utils import get_judgeanswer_and_reference, get_outdir
|
|
|
|
|
|
def post_process_ir(judgement: str):
|
|
"""Input a string like below:
|
|
|
|
Conclusion: [[Correct]]\nReasoning: xxx
|
|
and extract the score
|
|
"""
|
|
matches = re.findall(r'\[\[(.*?)\]\]', judgement)
|
|
if matches:
|
|
matches = matches[0]
|
|
if matches in ['Correct', 'Wrong', '对', '错']:
|
|
if matches == 'Correct' or matches == '对':
|
|
return {'score': 1}
|
|
else:
|
|
return {'score': 0}
|
|
else:
|
|
return None
|
|
else:
|
|
return None
|
|
|
|
|
|
def get_results(
|
|
judged_answers,
|
|
references,
|
|
fout,
|
|
fout_flag,
|
|
model,
|
|
):
|
|
capability_ratings = defaultdict(int)
|
|
capability_counts = defaultdict(int)
|
|
for ans, ref in zip(judged_answers, references):
|
|
lan = ref['others']['lan']
|
|
capability_ratings['total'] += ans['score']
|
|
capability_counts['total'] += 1
|
|
capability_ratings[lan] += ans['score']
|
|
capability_counts[lan] += 1
|
|
|
|
capability_avg_ratings = defaultdict(float)
|
|
|
|
for capability, total_score in capability_ratings.items():
|
|
capability_avg_ratings[
|
|
capability] = total_score / capability_counts[capability]
|
|
|
|
scores = {model: capability_avg_ratings}
|
|
|
|
with open(fout, 'a+', newline='') as csvfile:
|
|
writer = csv.writer(csvfile)
|
|
if fout_flag == 0:
|
|
num_header = [str(i) for i in range(4)]
|
|
writer.writerow(num_header)
|
|
|
|
header = ['模型']
|
|
for category in capability_avg_ratings:
|
|
header.append(category)
|
|
writer.writerow(header)
|
|
|
|
row = [model]
|
|
for category in capability_avg_ratings:
|
|
row.append(scores[model][category])
|
|
writer.writerow(row)
|
|
|
|
|
|
class IRSummarizer:
|
|
"""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, judge_type='autoj') -> None:
|
|
self.tasks = []
|
|
self.cfg = config
|
|
self.eval_model_cfgs = self.cfg['eval']['partitioner']['models']
|
|
self.eval_model_abbrs = [
|
|
model_abbr_from_cfg(model) for model in self.eval_model_cfgs
|
|
]
|
|
self.judge_abbr = model_abbr_from_cfg(self.cfg['judge_model'])
|
|
self.judge_type = judge_type
|
|
assert self.judge_type in ['general', 'autoj']
|
|
self.judge_map = {
|
|
'general': post_process_ir,
|
|
'autoj': post_process_autoj,
|
|
}
|
|
self.judge_function = self.judge_map[self.judge_type]
|
|
|
|
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']
|
|
output_dir, results_folder = get_outdir(self.cfg, time_str)
|
|
fout_flag = 0
|
|
for eval_model_abbr in self.eval_model_abbrs:
|
|
subdir = eval_model_abbr + '_judged-by--' + self.judge_abbr
|
|
subdir_path = os.path.join(results_folder, subdir)
|
|
if os.path.isdir(subdir_path):
|
|
model, judge_model = eval_model_abbr, self.judge_abbr
|
|
fout = osp.join(output_dir,
|
|
'judged-by--' + judge_model + '.csv')
|
|
for dataset in dataset_cfgs:
|
|
judged_answers, references = get_judgeanswer_and_reference(
|
|
dataset, subdir_path, self.judge_function)
|
|
get_results(judged_answers, references, fout, fout_flag,
|
|
model)
|
|
fout_flag += 1
|
|
else:
|
|
print(subdir_path + ' is not exist! please check!')
|
|
with open(fout, 'r') as f:
|
|
x = from_csv(f)
|
|
print(x)
|