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import json
from tqdm import tqdm
import argparse
import numpy as np
def save_data(data,file_path):
with open(file_path, 'w', encoding='utf8') as f:
json_data=json.dumps(data,ensure_ascii=False)
f.write(json_data+'\n')
def load_data(file_path,is_training=False):
with open(file_path, 'r', encoding='utf8') as f:
lines = f.readlines()
result=[]
for l,line in tqdm(enumerate(lines)):
data = json.loads(line)
result.append(data)
return result
def recls(line):
mat=[]
for l in line:
s=[v for v in l['score'].values()]
mat.append(s)
mat=np.array(mat)
batch,num_labels=mat.shape
for i in range(len(line)):
index = np.unravel_index(np.argmax(mat, axis=None), mat.shape)
line[index[0]]['label'] = int(index[1])
mat[index[0],:] = np.zeros((num_labels,))
mat[:,index[1]] = np.zeros((batch,))
return line
def chid_m(data):
lines={}
for d in data:
if d['line_id'] not in lines.keys():
lines[d['line_id']]=[]
lines[d['line_id']].append(d)
result=[]
for k,v in lines.items():
result.extend(recls(v))
return result
def submit(file_path):
lines = chid_m(load_data(file_path))
result={}
for line in tqdm(lines):
data = line
result[data['id']]=data['label']
return result
if __name__=="__main__":
parser = argparse.ArgumentParser(description="train")
parser.add_argument("--data_path", type=str,default="")
parser.add_argument("--save_path", type=str,default="")
args = parser.parse_args()
save_data(submit(args.data_path), args.save_path)
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