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import os |
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from glob import glob |
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import numpy as np |
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from config import Config |
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config = Config() |
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eval_txts = sorted(glob('e_results/*_eval.txt')) |
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print('eval_txts:', [_.split(os.sep)[-1] for _ in eval_txts]) |
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score_panel = {} |
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sep = '&' |
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metrics = ['sm', 'wfm', 'hce'] |
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if 'DIS5K' not in config.task: |
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metrics.remove('hce') |
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for metric in metrics: |
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print('Metric:', metric) |
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current_line_nums = [] |
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for idx_et, eval_txt in enumerate(eval_txts): |
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with open(eval_txt, 'r') as f: |
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lines = [l for l in f.readlines()[3:] if '.' in l] |
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current_line_nums.append(len(lines)) |
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for idx_et, eval_txt in enumerate(eval_txts): |
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with open(eval_txt, 'r') as f: |
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lines = [l for l in f.readlines()[3:] if '.' in l] |
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for idx_line, line in enumerate(lines[:min(current_line_nums)]): |
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properties = line.strip().strip(sep).split(sep) |
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dataset = properties[0].strip() |
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ckpt = properties[1].strip() |
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if int(ckpt.split('--epoch_')[-1].strip()) < 0: |
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continue |
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targe_idx = { |
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'sm': [5, 2, 2, 5, 2], |
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'wfm': [3, 3, 8, 3, 8], |
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'hce': [7, -1, -1, 7, -1] |
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}[metric][['DIS5K', 'COD', 'HRSOD', 'DIS5K+HRSOD+HRS10K', 'P3M-10k'].index(config.task)] |
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if metric != 'hce': |
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score_sm = float(properties[targe_idx].strip()) |
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else: |
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score_sm = int(properties[targe_idx].strip().strip('.')) |
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if idx_et == 0: |
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score_panel[ckpt] = [] |
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score_panel[ckpt].append(score_sm) |
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metrics_min = ['hce', 'mae'] |
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max_or_min = min if metric in metrics_min else max |
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score_max = max_or_min(score_panel.values(), key=lambda x: np.sum(x)) |
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good_models = [] |
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for k, v in score_panel.items(): |
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if (np.sum(v) <= np.sum(score_max)) if metric in metrics_min else (np.sum(v) >= np.sum(score_max)): |
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print(k, v) |
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good_models.append(k) |
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with open(eval_txt, 'r') as f: |
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lines = f.readlines() |
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info4good_models = lines[:3] |
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metric_names = [m.strip() for m in lines[1].strip().strip('&').split('&')[2:]] |
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testset_mean_values = {metric_name: [] for metric_name in metric_names} |
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for good_model in good_models: |
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for idx_et, eval_txt in enumerate(eval_txts): |
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with open(eval_txt, 'r') as f: |
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lines = f.readlines() |
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for line in lines: |
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if set([good_model]) & set([_.strip() for _ in line.split(sep)]): |
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info4good_models.append(line) |
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metric_scores = [float(m.strip()) for m in line.strip().strip('&').split('&')[2:]] |
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for idx_score, metric_score in enumerate(metric_scores): |
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testset_mean_values[metric_names[idx_score]].append(metric_score) |
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if 'DIS5K' in config.task: |
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testset_mean_values_lst = ['{:<4}'.format(int(np.mean(v_lst[:-1]).round())) if name == 'HCE' else '{:.3f}'.format(np.mean(v_lst[:-1])).lstrip('0') for name, v_lst in testset_mean_values.items()] |
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sample_line_for_placing_mean_values = info4good_models[-2] |
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numbers_placed_well = sample_line_for_placing_mean_values.replace(sample_line_for_placing_mean_values.split('&')[1].strip(), 'DIS-TEs').strip().split('&')[3:] |
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for idx_number, (number_placed_well, testset_mean_value) in enumerate(zip(numbers_placed_well, testset_mean_values_lst)): |
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numbers_placed_well[idx_number] = number_placed_well.replace(number_placed_well.strip(), testset_mean_value) |
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testset_mean_line = '&'.join(sample_line_for_placing_mean_values.replace(sample_line_for_placing_mean_values.split('&')[1].strip(), 'DIS-TEs').split('&')[:3] + numbers_placed_well) + '\n' |
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info4good_models.append(testset_mean_line) |
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info4good_models.append(lines[-1]) |
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info = ''.join(info4good_models) |
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print(info) |
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with open(os.path.join('e_results', 'eval-{}_best_on_{}.txt'.format(config.task, metric)), 'w') as f: |
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f.write(info + '\n') |
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