| from __future__ import print_function |
|
|
| import sys |
| import glob |
| import numpy as np |
|
|
| DATASET = 'SS-Twitter' |
| METHOD = 'new' |
|
|
| |
| if len(sys.argv) == 3: |
| DATASET = sys.argv[1] |
| METHOD = sys.argv[2] |
|
|
| RESULTS_DIR = 'results/' |
| RESULT_PATHS = glob.glob('{}/{}_{}_*_results.txt'.format(RESULTS_DIR, DATASET, METHOD)) |
|
|
| if not RESULT_PATHS: |
| print('Could not find results for \'{}\' using \'{}\' in directory \'{}\'.'.format(DATASET, METHOD, RESULTS_DIR)) |
| else: |
| 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(DATASET)) |
| 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)) |
|
|