| | from __future__ import print_function |
| |
|
| | |
| | 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'] |
| |
|
| | 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) |
| |
|