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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:
for line in data:
json_data=json.dumps(line,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
import copy
def csl_scorted(data):
lines={}
new_data=copy.deepcopy(data)
for d in data:
if d['texta'] not in lines.keys():
lines[d['texta']]={}
lines[d['texta']][d['id']]=d['score'][d['choice'][0]]
result=[]
id2preds={}
for k,v in lines.items():
v=sorted(v.items(), key=lambda x: x[1], reverse=True)
# print(v)
for i,(text_id, score) in enumerate(v):
if i<len(v)/2:
label=0
else:
label=1
id2preds[text_id]=label
for d in range(len(new_data)):
new_data[d]['label']=id2preds[new_data[d]['id']]
return new_data
def submit(file_path):
id2label={1:'0',0:'1'}
lines=csl_scorted(load_data(file_path))
result=[]
for line in tqdm(lines):
data = line
result.append({'id':data['id'],'label':str(id2label[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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