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import csv |
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import os |
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import os.path as osp |
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import re |
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from collections import defaultdict |
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from datetime import datetime |
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import numpy as np |
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from mmengine import ConfigDict |
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try: |
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from prettytable import from_csv |
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except ImportError: |
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from_csv = None |
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from opencompass.utils import model_abbr_from_cfg |
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from .subjective_post_process import post_process_autoj |
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from .utils import get_judgeanswer_and_reference, get_outdir |
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def post_process_ir(judgement: str): |
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"""Input a string like below: |
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Conclusion: [[Correct]]\nReasoning: xxx |
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and extract the score |
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""" |
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matches = re.findall(r'\[\[(.*?)\]\]', judgement) |
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if matches: |
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matches = matches[0] |
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if matches in ['Correct', 'Wrong', '对', '错']: |
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if matches == 'Correct' or matches == '对': |
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return {'score': 1} |
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else: |
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return {'score': 0} |
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else: |
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return None |
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else: |
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return None |
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def get_results( |
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judged_answers, |
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references, |
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fout, |
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fout_flag, |
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model, |
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): |
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capability_ratings = defaultdict(int) |
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capability_counts = defaultdict(int) |
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for ans, ref in zip(judged_answers, references): |
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lan = ref['others']['lan'] |
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capability_ratings['total'] += ans['score'] |
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capability_counts['total'] += 1 |
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capability_ratings[lan] += ans['score'] |
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capability_counts[lan] += 1 |
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capability_avg_ratings = defaultdict(float) |
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for capability, total_score in capability_ratings.items(): |
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capability_avg_ratings[ |
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capability] = total_score / capability_counts[capability] |
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scores = {model: capability_avg_ratings} |
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with open(fout, 'a+', newline='') as csvfile: |
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writer = csv.writer(csvfile) |
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if fout_flag == 0: |
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num_header = [str(i) for i in range(4)] |
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writer.writerow(num_header) |
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header = ['模型'] |
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for category in capability_avg_ratings: |
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header.append(category) |
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writer.writerow(header) |
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row = [model] |
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for category in capability_avg_ratings: |
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row.append(scores[model][category]) |
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writer.writerow(row) |
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class IRSummarizer: |
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"""Do the subjectivity analyze based on evaluation results. |
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Args: |
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config (ConfigDict): The configuration object of the evaluation task. |
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It's expected to be filled out at runtime. |
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""" |
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def __init__(self, config: ConfigDict, judge_type='autoj') -> None: |
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self.tasks = [] |
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self.cfg = config |
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self.eval_model_cfgs = self.cfg['eval']['partitioner']['models'] |
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self.eval_model_abbrs = [ |
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model_abbr_from_cfg(model) for model in self.eval_model_cfgs |
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] |
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self.judge_abbr = model_abbr_from_cfg(self.cfg['judge_model']) |
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self.judge_type = judge_type |
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assert self.judge_type in ['general', 'autoj'] |
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self.judge_map = { |
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'general': post_process_ir, |
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'autoj': post_process_autoj, |
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} |
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self.judge_function = self.judge_map[self.judge_type] |
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def summarize(self, |
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time_str: str = datetime.now().strftime('%Y%m%d_%H%M%S')): |
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"""Summarize the subjectivity analysis based on evaluation results. |
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Args: |
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time_str (str): Timestamp for file naming. |
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Returns: |
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pd.DataFrame: The summary results. |
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""" |
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dataset_cfgs = self.cfg['datasets'] |
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output_dir, results_folder = get_outdir(self.cfg, time_str) |
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fout_flag = 0 |
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for eval_model_abbr in self.eval_model_abbrs: |
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subdir = eval_model_abbr + '_judged-by--' + self.judge_abbr |
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subdir_path = os.path.join(results_folder, subdir) |
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if os.path.isdir(subdir_path): |
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model, judge_model = eval_model_abbr, self.judge_abbr |
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fout = osp.join(output_dir, |
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'judged-by--' + judge_model + '.csv') |
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for dataset in dataset_cfgs: |
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judged_answers, references = get_judgeanswer_and_reference( |
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dataset, subdir_path, self.judge_function) |
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get_results(judged_answers, references, fout, fout_flag, |
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model) |
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fout_flag += 1 |
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else: |
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print(subdir_path + ' is not exist! please check!') |
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with open(fout, 'r') as f: |
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x = from_csv(f) |
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print(x) |
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