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from ..smp import *
from ..dataset.utils.judge_util import build_judge
from ..dataset.utils.multiple_choice import extract_answer_from_item
from .matching_util import can_infer
from .mp_util import track_progress_rich
def MMMU_result_transfer(result_path):
res = {}
result_data = load(result_path)
mcq = result_data['A'].notna()
lt = len(result_data)
for i in range(lt):
line = result_data.iloc[i]
if mcq[i]:
options = {
cand: line[cand]
for cand in string.ascii_uppercase
if cand in line and not pd.isna(line[cand])
}
prediction = line['prediction']
infer_prediction = can_infer(prediction, options)
res[line['id']] = infer_prediction
else:
res[line['id']] = line['prediction']
result_json = result_path.replace('.xlsx', '.json')
dump(res, result_json)
return result_json
def MMTBench_result_transfer(eval_file, dataset='default', **judge_kwargs):
logger = get_logger('Evaluation')
nproc = judge_kwargs.pop('nproc', 4)
rd.seed(2680)
suffix = eval_file.split('.')[-1]
model = judge_kwargs['model']
assert model in ['chatgpt-0125', 'exact_matching', 'gpt-4-0125']
name_str_map = {
'chatgpt-0125': 'openai',
'gpt-4-0125': 'gpt4'
}
name_str = name_str_map[model] if model in name_str_map else model
if model == 'exact_matching':
model = None
elif gpt_key_set():
model = build_judge(**judge_kwargs)
if not model.working():
logger.error('The OPENAI API is not working properly, will use exact matching for evaluation')
model = None
else:
logger.error('OPENAI_API_KEY is not set properly, will use exact matching for evaluation')
model = None
logger.info(f'Evaluating {eval_file}')
result_file = eval_file.replace(f'.{suffix}', f'_{name_str}_option.pkl')
result = {}
if osp.exists(result_file):
result = load(result_file)
data = load(eval_file)
assert 'index' in data, 'Essentail columns missing in the eval_file.'
data = data.sort_values(by='index')
data['prediction'] = [str(x) for x in data['prediction']]
for k in data.keys():
data[k.lower() if k not in list(string.ascii_uppercase) else k] = data.pop(k)
idx2lines = {data.iloc[i]['index']: data.iloc[i] for i in range(len(data))}
idx2lines = {k: v for k, v in idx2lines.items() if k not in result}
indices = list(idx2lines.keys())
lines = [idx2lines[i] for i in indices]
tups = [(model, line) for line in lines]
res = track_progress_rich(
extract_answer_from_item,
tups,
nproc=nproc,
chunksize=nproc,
save=result_file,
keys=indices)
for i, r in zip(indices, res):
if i in result:
assert result[i]['opt'] == r['opt'] and result[i]['log'] == r['log']
else:
result[i] = r
indices = list(data['index'])
data['opt'] = [result[i]['opt'] for i in data['index']]
data['log'] = [result[i]['log'] for i in data['index']]
# load split
output_path = eval_file.replace(f'.{suffix}', f'_{name_str}_submission.tsv')
dump(data, eval_file.replace(f'.{suffix}', f'_{name_str}_submission.tsv'))
return output_path
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