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| from __future__ import print_function | |
| # allow us to import the codebase directory | |
| import sys | |
| import glob | |
| import numpy as np | |
| from os.path import dirname, abspath | |
| sys.path.insert(0, dirname(dirname(abspath(__file__)))) | |
| DATASETS = ['SE0714', 'Olympic', 'PsychExp', 'SS-Twitter', 'SS-Youtube', | |
| 'SCv1', 'SV2-GEN'] # 'SE1604' excluded due to Twitter's ToS | |
| def get_results(dset): | |
| METHOD = 'last' | |
| RESULTS_DIR = 'results/' | |
| RESULT_PATHS = glob.glob('{}/{}_{}_*_results.txt'.format(RESULTS_DIR, dset, METHOD)) | |
| assert len(RESULT_PATHS) | |
| scores = [] | |
| for path in RESULT_PATHS: | |
| with open(path) as f: | |
| score = f.readline().split(':')[1] | |
| scores.append(float(score)) | |
| average = np.mean(scores) | |
| maximum = max(scores) | |
| minimum = min(scores) | |
| std = np.std(scores) | |
| print('Dataset: {}'.format(dset)) | |
| print('Method: {}'.format(METHOD)) | |
| print('Number of results: {}'.format(len(scores))) | |
| print('--------------------------') | |
| print('Average: {}'.format(average)) | |
| print('Maximum: {}'.format(maximum)) | |
| print('Minimum: {}'.format(minimum)) | |
| print('Standard deviaton: {}'.format(std)) | |
| for dset in DATASETS: | |
| get_results(dset) | |