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