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ensemble_without_weight.py
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# -*- coding: utf-8 -*-
import json
import argparse
import numpy as np
from collections import OrderedDict
import os
__author__ = "[email protected]"
path = '/home/ljp/data/lic2020/'
single_dict = {
'output_albert_xxlarge_utf8':
{
'2_1': 75.66131,
'2_3': 74.78274,
'3_2': 74.83024,
'4_2': 74.71253
},
'output_data_join_utf8':
{
'1_2': 73.90175,
'5_1': 75.89828,
'6_2': 74.77744,
'8_3': 76.59578,
'14_3': 77.01477,
'17_2': 77.51061,
'17_3':78.87438,
'22_5': 77.63886,
'23_2': 77.44467,
'23_3': 77.96674,
'23_4':78.5541,
'23_5': 78.48484,
'25_4':78.18604,
'26_1': 77.13503,
'26_2':77.49921,
'26_5':78.35698,
'27_1':78.2233,
'27_2':77.68519,
'27_5':78.04175,
'28_5':78.26716,
},
'output_roberta_utf8':
{
'2_5': 75.2279,
'3_2': 74.54158,
'4_4': 75.02555,
'5_4': 74.44874,
'6_3': 74.87999
},
'output_utf8':
{
'0': 69.42017,
'1': 70.98569,
'2': 73.52785,
'3': 73.26201,
'4': 71.64866,
'5': 71.30978,
'6': 72.71333,
'7': 73.31789,
'11':73.84171,
'12':75.60135
}
}
def ensemble_v1(pred_file, division, input_files):
"""
text出现频次 和 probability 加权排序
"""
if 8 in input_files:
input('error! model 8 doesn\'t have the same key as other model! ')
res_list = [json.load(open(path+"output_utf8/{}_{}_nbest_predictions_utf8.json".format(i, division))) for i in input_files]
for item in res_list:
print(len(item))
res_json = {}
for k in list(res_list[0].keys()):
text_list = {}
for i in range(len(res_list)):
for j in range(len(res_list[i][k])):
text = res_list[i][k][j]["text"]
prob = res_list[i][k][j]["probability"]
if not text_list.get(text):
# TODO(aitingliu): start_logit 和 end_logit也可以加进来，看看有没有效果增强
text_list[text] = 1 * prob # 1 * prob
else:
text_list[text] += 1 * prob # 1 * prob
# print(text_list)
# print(sorted(text_list.items(), key=lambda d: d[1], reverse=True))
res_json[k] = sorted(text_list.items(), key=lambda d: d[1], reverse=True)[0][0]
json.dump(res_json, open(pred_file, "w"), ensure_ascii=False, indent=4)
def ensemble_v4_multiple(res_list, pred_file, weight):
"""
res_list: 单模型输出的json
对模型在test1上F1的权重w做下列计算作为输入的weight
weight = (w-50) / (w.max())
input_files = [2, 3, 6, 7]
"""
res_json = {}
try:
for k in list(res_list[0].keys()):
text_list = {}
for i in range(len(res_list)):
for j in range(len(res_list[i][k])):
text = res_list[i][k][j]["text"]
prob = res_list[i][k][j]["probability"]
if not text_list.get(text):
text_list[text] = weight[i] * prob # 1 * prob
else:
text_list[text] += weight[i] * prob # 1 * prob
res_json[k] = sorted(text_list.items(), key=lambda d: d[1], reverse=True)[0][0]
# json.dump(res_json, open(pred_file, "w"), ensure_ascii=False, indent=4)
return res_json
except Exception as e1:
print("err1: ", pred_file, e1)
def get_json_dict(division, model_lst=[]):
json_dict = {}
if len(model_lst) != 0: # 只load指定文件
for item in model_lst:
model_type, param = item.split('/')
obj = json.load(open(datapath+'{}_{}_nbest_predictions_utf8.json'.format(item, division)))
json_dict[model_type+'/'+ param] = (obj, single_dict[model_type][param])
return json_dict
# load全部文件
for model_type, param_dict in single_dict.items():
for param, f1 in param_dict.items():
obj = json.load(open(datapath+'{}/{}_{}_nbest_predictions_utf8.json'.format(model_type, param, division)))
json_dict[model_type+'/'+ param] = (obj, f1)
return json_dict
def ensemble_one(model_lst, division, version, weight_all=None):
obj_dict = get_json_dict(division, model_lst)
res_list = [obj_dict[key][0] for key in model_lst]
if version == 'v4':
f1_np = np.array([obj_dict[key][1] for key in model_lst])
f1_np = (f1_np - weight_all) / f1_np.max()
if version == 'v4':
file_name = '_'.join(model_lst) + '_w' + str(weight_all) + '.json'
pred_ans = ensemble_v4_multiple(res_list, file_name, f1_np)
elif version == 'v1' and weight_all == None:
file_name = '_'.join(model_lst) + '.json'
pred_ans = ensemble_v1_multiple(res_list, file_name)
else:
input('version input error! version=', version)
sim_name = []
for item in model_lst:
if 'xxlarge' in item:
sim_name.append('xx'+item.split('/')[1])
elif 'output_roberta_utf8' in item:
sim_name.append('ro'+item.split('/')[1])
elif 'output_data_join_utf8' in item:
sim_name.append('dj'+item.split('/')[1])
else:
sim_name.append(item.split('/')[1])
if version == 'v4':
file_name = '-'.join(sim_name) + '_w' + str(weight_all) + '.json'
elif version == 'v1':
file_name = '-'.join(sim_name) + '.json'
else:
input('version input error! version=', version)
file_name = '{}_ensemble_{}_'.format(division, version) + file_name
print('file_name: ', file_name)
json.dump(pred_ans, open(os.path.join(export_datapath, file_name), "w"), ensure_ascii=False, indent=4)
if __name__ == "__main__":
'''
参数： group datapath export_datapath constant division
'''
#group = {'output_data_join_utf8': ["26_2", "14_3", "23_3", "26_5", "17_2", "23_4", "27_1", "27_2", "17_3", "23_5", "27_5", "22_5", "25_4", "28_5", "23_2", "26_1"]} # 按照single_dict的模型类型，指定参与ensemble的模型名称。原始json文件名类似：14_3_test1_nbest_predictions_utf8.json
L = ["14","17","21","22","23","25","26","27","28","29","33"]
group = {'output_data_join_utf8':[]}
for i in range(1,6):
for l in L:
group['output_data_join_utf8'] += [l+"_"+str(i)]
datapath = './results/' # 输出的ensemble json存放路径
export_datapath = '/home_export/bzw/MRC/code/lic2020/results/ensemble_test1'# 新ensemble json保存目录
#constant = 71.5 # constant
division = 'test1' # test1/train/dev
version = 'v1' # ensemble version
model_name_list = []
for name in group:
for sub in group[name]:
model_name_list.append(name+'/'+sub)
model_name_list = sorted(model_name_list)
print(model_name_list)
ensemble_one(model_name_list, division, version)