| import argparse | |
| from collections import defaultdict | |
| from statistics import mean | |
| from sys import meta_path | |
| import numpy as np | |
| import pandas as pd | |
| import torch | |
| parser = argparse.ArgumentParser() | |
| parser.add_argument('scores') | |
| parser.add_argument('out') | |
| args = parser.parse_args() | |
| scores = pd.read_csv(args.scores, header=None) | |
| obs_ids = scores.iloc[:, 0] | |
| dropped = 0 | |
| total = 0 | |
| with open(args.out, 'w') as f: | |
| f.write('observation_id,class_id\n') | |
| for id in obs_ids: | |
| scrs = np.array(scores[scores.iloc[:, 0] == id].iloc[0, 1:]) | |
| scrs = torch.softmax(torch.from_numpy(scrs), dim=0).numpy() | |
| max_score = np.max(scrs) | |
| cls_id = np.argmax(scrs) | |
| if max_score < 0.09: | |
| cls_id = -1 | |
| dropped += 1 | |
| total += 1 | |
| f.write(f'{id},{cls_id}\n') | |
| print(f'dropped {dropped} out of {total}') | |