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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}')
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