├── .gitattributes
├── models
    ├── __init__.py
    └── bert_spc.py
├── assets
    └── bert_spc.png
├── README.md
├── datasets
    ├── preprocess.py
    └── Submit_Example.csv
├── bucket_iterator.py
├── infer.py
├── data_utils.py
└── train.py


/.gitattributes:
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1 | # Auto detect text files and perform LF normalization
2 | * text=auto
3 | 


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/models/__init__.py:
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1 | # -*- coding: utf-8 -*-
2 | 
3 | from models.bert_spc import BERT_SPC


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/assets/bert_spc.png:
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https://raw.githubusercontent.com/GeneZC/BERTFinanceNeg/HEAD/assets/bert_spc.png


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/README.md:
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 1 | # BERTFinanceNeg
 2 | 
 3 | BERT-based Finance Negation Detection
 4 | * Baseline for [金融信息负面及主体判定, CCF Big Data & Computing Intelligence Contest, CCF BDCI](https://www.datafountain.cn/competitions/353)
 5 | * [Chen Zhang](https://genezc.github.io).
 6 | 
 7 | ## Requirements
 8 | 
 9 | * Python 3.6
10 | * PyTorch 1.0.0
11 | * pytorch-pretrained-bert
12 | 
13 | ## Usage
14 | 
15 | * Train with command, optional arguments could be found in [train.py](/train.py)
16 | ```bash
17 | python train.py --model_name bert --batch_size 16 --save True 
18 | ```
19 | * Infer with [infer.py](/infer.py)
20 | 
21 | ## Model
22 | 
23 | An overview of the BERT-based baseline is given below
24 | 
25 | ![model](/assets/bert_spc.png)
26 | 
27 | ## Credits
28 | 
29 | * For any issues or suggestions about this work, don't hesitate to create an issue or directly contact me via [gene_zhangchen@163.com](mailto:gene_zhangchen@163.com) !


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/models/bert_spc.py:
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 1 | # -*- coding: utf-8 -*-
 2 | # file: BERT_SPC.py
 3 | # author: songyouwei <youwei0314@gmail.com>
 4 | # Copyright (C) 2019. All Rights Reserved.
 5 | import torch
 6 | import torch.nn as nn
 7 | 
 8 | 
 9 | class BERT_SPC(nn.Module):
10 |     def __init__(self, bert, opt):
11 |         super(BERT_SPC, self).__init__()
12 |         # self.squeeze_embedding = SqueezeEmbedding()
13 |         self.bert = bert
14 |         self.dropout = nn.Dropout(opt.dropout)
15 |         self.dense = nn.Linear(768, opt.polarities_dim)
16 | 
17 |     def forward(self, inputs):
18 |         text_bert_indices, bert_segments_ids = inputs[0], inputs[1]
19 |         # text_bert_len = torch.sum(text_bert_indices != 0, dim=-1)
20 |         # text_bert_indices = self.squeeze_embedding(text_bert_indices, text_bert_len)
21 |         # bert_segments_ids = self.squeeze_embedding(bert_segments_ids, text_bert_len)
22 |         _, pooled_output = self.bert(text_bert_indices, bert_segments_ids, output_all_encoded_layers=False)
23 |         pooled_output = self.dropout(pooled_output)
24 |         logits = self.dense(pooled_output)
25 |         return logits
26 | 


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/datasets/preprocess.py:
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 1 | # -*- coding: utf-8 -*-
 2 | 
 3 | import csv
 4 | 
 5 | 
 6 | # 让文本只保留汉字
 7 | def is_chinese(uchar):
 8 |     if uchar >= u'\u4e00' and uchar <= u'\u9fa5':
 9 |         return True
10 |     else:
11 |         return False
12 | 
13 | def format_str(content):
14 |     content_str = ''
15 |     for i in content:
16 |         if is_chinese(i):
17 |             content_str = content_str + ｉ
18 |     return content_str
19 | 
20 | def process(filename):
21 |     header = []
22 |     rows = []
23 |     with open(filename, 'r', encoding='utf-8-sig') as f:
24 |         f_csv = csv.reader(f)
25 |         for i, row in enumerate(f_csv):
26 |             if i == 0:
27 |                 header = [row[0], row[2]] + row[3:]
28 |                 print(header)
29 |             else:
30 |                 row = [row[0], row[2]] + row[3:]
31 |                 rows.append(row)
32 |     
33 |     with open('Clean_'+filename, 'w', encoding='utf-8-sig', newline='') as f:
34 |         f_csv = csv.writer(f)
35 |         f_csv.writerow(header)
36 |         for row in rows:
37 |             f_csv.writerow(row)
38 | 
39 | if __name__ == '__main__':
40 |     process('Train_Data.csv')
41 |     process('Test_Data.csv')
42 |     


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/bucket_iterator.py:
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 1 | # -*- coding: utf-8 -*-
 2 | 
 3 | import math
 4 | import random
 5 | import torch
 6 | 
 7 | class BucketIterator(object):
 8 |     def __init__(self, data, batch_size, shuffle=True, sort=True):
 9 |         self.shuffle = shuffle
10 |         self.sort = sort
11 |         self.batches = self.sort_and_pad(data, batch_size)
12 |         self.batch_len = len(self.batches)
13 | 
14 |     def sort_and_pad(self, data, batch_size):
15 |         num_batch = int(math.ceil(len(data) / batch_size))
16 |         if self.sort:
17 |             sorted_data = sorted(data, key=lambda x: len(x['bert_text_indices']))
18 |         else:
19 |             sorted_data = data
20 |         batches = []
21 |         for i in range(num_batch):
22 |             batches.append(self.pad_trunc_data(sorted_data[i*batch_size : (i+1)*batch_size]))
23 |         return batches
24 | 
25 |     @staticmethod
26 |     def pad_trunc_data(batch_data):
27 |         batch_bert_text_indices = []
28 |         batch_bert_segment_indices = []
29 |         batch_negative = []
30 |         bert_max_len = max([len(t['bert_text_indices']) for t in batch_data])
31 |         for item in batch_data:
32 |             bert_text_indices, bert_segment_indices, negative = \
33 |                 item['bert_text_indices'], item['bert_segment_indices'], item['negative']
34 |             bert_text_padding = [0] * (bert_max_len - len(bert_text_indices))
35 |             bert_segment_padding = [0] * (bert_max_len - len(bert_segment_indices))
36 |             batch_bert_text_indices.append((bert_text_indices + bert_text_padding)[:512])
37 |             batch_bert_segment_indices.append((bert_segment_indices + bert_segment_padding)[:512])
38 |             batch_negative.append(negative)
39 |         return { 
40 |                 'bert_text_indices': torch.tensor(batch_bert_text_indices), 
41 |                 'bert_segment_indices': torch.tensor(batch_bert_segment_indices), 
42 |                 'negative': torch.tensor(batch_negative), 
43 |                 }
44 | 
45 |     def __iter__(self):
46 |         if self.shuffle:
47 |             random.shuffle(self.batches)
48 |         for idx in range(self.batch_len):
49 |             yield self.batches[idx]
50 | 


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/infer.py:
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  1 | # -*- coding: utf-8 -*-
  2 | 
  3 | 
  4 | import os
  5 | import csv
  6 | import pickle
  7 | import torch
  8 | import torch.nn as nn
  9 | import torch.nn.functional as F
 10 | import argparse
 11 | 
 12 | from data_utils import _tokenize_chinese_text, Tokenizer4Bert
 13 | from models import BERT_SPC
 14 | from pytorch_pretrained_bert import BertModel
 15 | 
 16 | class Inferer:
 17 |     """A simple inference example"""
 18 |     def __init__(self, opt):
 19 |         self.opt = opt
 20 |         print("loading {0} tokenizer...".format(opt.dataset))
 21 |         self.bert_tokenizer = Tokenizer4Bert('bert-base-chinese')
 22 | 
 23 |         self.model_list = []
 24 |         for i, model_name in enumerate(opt.model_name_list):
 25 |             print('loading model {0}... '.format(model_name))
 26 |             bert = BertModel.from_pretrained('bert-base-chinese')
 27 |             model = nn.DataParallel(opt.model_class_list[i](bert, opt).to(opt.device))
 28 |             model.load_state_dict(torch.load(opt.state_dict_path_list[i]))
 29 |             # switch model to evaluation mode
 30 |             model.eval()
 31 |             self.model_list.append(model)
 32 |         
 33 |         torch.autograd.set_grad_enabled(False)
 34 | 
 35 |     def evaluate(self, fname):
 36 |         fin = open(fname, 'r', encoding='utf-8-sig', newline='\n', errors='ignore')
 37 |         fin_csv = csv.reader(fin)
 38 |         fout = open('submission.csv', 'w', encoding='utf-8-sig', newline='')
 39 |         fout_csv = csv.writer(fout)
 40 |         header = ['id','negative','key_entity']
 41 |         fout_csv.writerow(header)
 42 |         for i, row in enumerate(fin_csv):
 43 |             if i == 0:
 44 |                 continue
 45 |             else:
 46 |                 key_entities = []
 47 |                 uid = row[0]
 48 |                 text_raw = row[1]
 49 |                 entities = row[2].split(';')
 50 |                 for entity in entities:
 51 |                     if entity == '' or entity == ' ':
 52 |                         break
 53 |                     aspect = entity
 54 |                     text_left, _, text_right = [s for s in text_raw.partition(entity)]
 55 |                     text_left = _tokenize_chinese_text(text_left)
 56 |                     text_right = _tokenize_chinese_text(text_right)
 57 |                     aspect = _tokenize_chinese_text(aspect)
 58 |                     _text_indices = self.bert_tokenizer.text_to_sequence(text_left+' '+aspect+' '+text_right)
 59 |                     _aspect_indices = self.bert_tokenizer.text_to_sequence(aspect)
 60 |                     bert_text_indices = [(self.bert_tokenizer.text_to_sequence('[CLS]') + _text_indices +\
 61 |                          self.bert_tokenizer.text_to_sequence('[SEP]') + _aspect_indices + self.bert_tokenizer.text_to_sequence('[SEP]'))[:512]]
 62 |                     bert_segment_indices = [([0] * (len(_text_indices) + 2) + [1] * (len(_aspect_indices) + 1))[:512]]
 63 |                     data = {
 64 |                         'bert_text_indices': torch.tensor(bert_text_indices),
 65 |                         'bert_segment_indices': torch.tensor(bert_segment_indices),
 66 |                     }
 67 |                     preds = []
 68 |                     for i, inputs_cols in enumerate(self.opt.inputs_cols_list):
 69 |                         t_inputs = [data[col].to(self.opt.device) for col in inputs_cols]
 70 |                         with torch.no_grad():
 71 |                             t_outputs = self.model_list[i](t_inputs)
 72 |                         t_preds = t_outputs.argmax(dim=1).cpu().numpy()[0]
 73 |                         preds.append(t_preds)
 74 |                     preds = max(preds, key=preds.count)
 75 |                     if preds == 1:
 76 |                         key_entities.append(entity)
 77 |                 if len(key_entities) == 0:
 78 |                     fout_csv.writerow([uid, '0', ''])
 79 |                 else:
 80 |                     fout_csv.writerow([uid, '1', ';'.join(key_entities)])
 81 |         fin.close()
 82 |         fout.close()
 83 | 
 84 | if __name__ == '__main__':
 85 |     model_classes = {
 86 |         'bert': BERT_SPC
 87 |     }
 88 |     dataset = 'finance'
 89 |     # set your trained models here
 90 |     model_state_dict_paths = {
 91 |         'bert': 'state_dict/bert_'+dataset+'.pkl',
 92 |     }
 93 |     input_colses = {
 94 |         'bert': ['bert_text_indices', 'bert_segment_indices'],
 95 |     }
 96 |     class Option(object): pass
 97 |     opt = Option()
 98 |     opt.model_name_list = ['bert']
 99 |     opt.model_class_list = [model_classes[model_name] for model_name in opt.model_name_list]
100 |     opt.inputs_cols_list = [input_colses[model_name] for model_name in opt.model_name_list]
101 |     opt.dataset = dataset
102 |     opt.state_dict_path_list = [model_state_dict_paths[model_name] for model_name in opt.model_name_list]
103 |     opt.embed_dim = 300
104 |     opt.hidden_dim = 300
105 |     opt.polarities_dim = 2
106 |     opt.dropout = 0.3
107 |     opt.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
108 | 
109 |     inf = Inferer(opt)
110 |     inf.evaluate('./datasets/Clean_Test_Data.csv')
111 | 


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/data_utils.py:
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  1 | # -*- coding: utf-8 -*-
  2 | 
  3 | import os
  4 | import csv
  5 | import pickle
  6 | import random
  7 | import numpy as np
  8 | from pytorch_pretrained_bert import BertTokenizer
  9 | 
 10 | def load_word_vec(path, word2idx=None):
 11 |     fin = open(path, 'r', encoding='utf-8', newline='\n', errors='ignore')
 12 |     word_vec = {}
 13 |     for line in fin:
 14 |         tokens = line.rstrip().split()
 15 |         if word2idx is None or tokens[0] in word2idx.keys():
 16 |             word_vec[tokens[0]] = np.asarray(tokens[1:], dtype='float32')
 17 |     return word_vec
 18 | 
 19 | 
 20 | def build_embedding_matrix(word2idx, embed_dim, type):
 21 |     embedding_matrix_file_name = '{0}_{1}_embedding_matrix.pkl'.format(str(embed_dim), type)
 22 |     if os.path.exists(embedding_matrix_file_name):
 23 |         print('loading embedding_matrix:', embedding_matrix_file_name)
 24 |         embedding_matrix = pickle.load(open(embedding_matrix_file_name, 'rb'))
 25 |     else:
 26 |         print('loading word vectors...')
 27 |         embedding_matrix = np.zeros((len(word2idx), embed_dim))
 28 |         embedding_matrix[1, :] = np.random.uniform(-1/np.sqrt(embed_dim), 1/np.sqrt(embed_dim), (1, embed_dim))
 29 |         fname = './fastText/cc.zh.300.vec'
 30 |         word_vec = load_word_vec(fname, word2idx=word2idx)
 31 |         print('building embedding_matrix:', embedding_matrix_file_name)
 32 |         for word, i in word2idx.items():
 33 |             vec = word_vec.get(word)
 34 |             if vec is not None:
 35 |                 # words not found in embedding index will be all-zeros.
 36 |                 embedding_matrix[i] = vec
 37 |         pickle.dump(embedding_matrix, open(embedding_matrix_file_name, 'wb'))
 38 |     return embedding_matrix
 39 | 
 40 | class Tokenizer4Bert:
 41 |     def __init__(self, pretrained_bert_name):
 42 |         self.tokenizer = BertTokenizer.from_pretrained(pretrained_bert_name)
 43 | 
 44 |     def text_to_sequence(self, text):
 45 |         words = text.split()
 46 |         sequence = [self.tokenizer.vocab[w] if w in self.tokenizer.vocab else self.tokenizer.vocab['[UNK]'] for w in words]
 47 |         if len(sequence) == 0:
 48 |             sequence = [0]
 49 |         return sequence
 50 | 
 51 | class Tokenizer(object):
 52 |     def __init__(self, word2idx=None):
 53 |         if word2idx is None:
 54 |             self.word2idx = {}
 55 |             self.idx2word = {}
 56 |             self.idx = 0
 57 |             self.word2idx['<pad>'] = self.idx
 58 |             self.idx2word[self.idx] = '<pad>'
 59 |             self.idx += 1
 60 |             self.word2idx['<unk>'] = self.idx
 61 |             self.idx2word[self.idx] = '<unk>'
 62 |             self.idx += 1
 63 |         else:
 64 |             self.word2idx = word2idx
 65 |             self.idx2word = {v:k for k,v in word2idx.items()}
 66 | 
 67 |     def fit_on_text(self, text):
 68 |         words = text.split()
 69 |         for word in words:
 70 |             if word not in self.word2idx:
 71 |                 self.word2idx[word] = self.idx
 72 |                 self.idx2word[self.idx] = word
 73 |                 self.idx += 1
 74 | 
 75 |     def text_to_sequence(self, text):
 76 |         words = text.split()
 77 |         unknownidx = 1
 78 |         sequence = [self.word2idx[w] if w in self.word2idx else unknownidx for w in words]
 79 |         if len(sequence) == 0:
 80 |             sequence = [0]
 81 |         return sequence
 82 | 
 83 | 
 84 | class ABSADataset(object):
 85 |     def __init__(self, data):
 86 |         self.data = data
 87 | 
 88 |     def __getitem__(self, index):
 89 |         return self.data[index]
 90 | 
 91 |     def __len__(self):
 92 |         return len(self.data)
 93 | 
 94 | def _tokenize_chinese_text(text):
 95 |     """Adds whitespace around any CJK character."""
 96 |     output = []
 97 |     for char in text:
 98 |         cp = ord(char)
 99 |         if _is_chinese_char(cp):
100 |             output.append(' ')
101 |             output.append(char)
102 |             output.append(' ')
103 |         else:
104 |             output.append(char)
105 |     return ''.join(output)
106 | 
107 | def _is_chinese_char(cp):
108 |     """Checks whether cp is the codepoint of a CJK character."""
109 |     if ((cp >= 0x4E00 and cp <= 0x9FFF) or  
110 |         (cp >= 0x3000 and cp <= 0x303f) or # punctuation
111 |         (cp >= 0x3400 and cp <= 0x4DBF) or  
112 |         (cp >= 0x20000 and cp <= 0x2A6DF) or  
113 |         (cp >= 0x2A700 and cp <= 0x2B73F) or  
114 |         (cp >= 0x2B740 and cp <= 0x2B81F) or  
115 |         (cp >= 0x2B820 and cp <= 0x2CEAF) or
116 |         (cp >= 0xF900 and cp <= 0xFAFF) or  
117 |         (cp >= 0x2F800 and cp <= 0x2FA1F)):  
118 |         return True
119 |     return False
120 | 
121 | class ABSADatesetReader:
122 |     @staticmethod
123 |     def __read_text__(fnames):
124 |         text = ''
125 |         for fname in fnames:
126 |             fin = open(fname, 'r', encoding='utf-8-sig', newline='\n', errors='ignore')
127 |             f_csv = csv.reader(fin)
128 |             for i, row in enumerate(f_csv):
129 |                 if i == 0:
130 |                     continue
131 |                 else:
132 |                     text_raw = _tokenize_chinese_text(row[1])
133 |                 text += text_raw + ' '
134 |             fin.close()
135 |                 
136 |         return text
137 | 
138 |     @staticmethod
139 |     def __read_data__(fname, bert_tokenizer=None):
140 |         all_data = []
141 |         fin = open(fname, 'r', encoding='utf-8-sig', newline='\n', errors='ignore')
142 |         f_csv = csv.reader(fin)
143 |         for i, row in enumerate(f_csv):
144 |             if i == 0:
145 |                 continue
146 |             else:
147 |                 text_raw = row[1]
148 |                 if row[2] != '':
149 |                     entities = row[2].split(';')
150 |                 else:
151 |                     continue
152 |                 if row[4] != '':
153 |                     key_entities = row[4].split(';')
154 |                 else:
155 |                     key_entities = []
156 |                 for entity in entities:
157 |                     if entity == '':
158 |                         continue
159 |                     aspect = entity
160 |                     text_left, _, text_right = [s for s in text_raw.partition(entity)]
161 |                     text_left = _tokenize_chinese_text(text_left)
162 |                     text_right = _tokenize_chinese_text(text_right)
163 |                     aspect = _tokenize_chinese_text(aspect)
164 |                     _text_indices = bert_tokenizer.text_to_sequence(text_left+' '+aspect+' '+text_right)
165 |                     _aspect_indices = bert_tokenizer.text_to_sequence(aspect)
166 |                     bert_text_indices = bert_tokenizer.text_to_sequence('[CLS]') + _text_indices + \
167 |                          bert_tokenizer.text_to_sequence('[SEP]') + _aspect_indices + bert_tokenizer.text_to_sequence('[SEP]')
168 |                     bert_segment_indices = [0] * (len(_text_indices) + 2) + [1] * (len(_aspect_indices) + 1)
169 |                     if entity in key_entities:
170 |                         negative = 1
171 |                     else:
172 |                         negative = 0
173 |                     data = {
174 |                         'bert_text_indices': bert_text_indices,
175 |                         'bert_segment_indices': bert_segment_indices,
176 |                         'negative': negative,
177 |                     }
178 | 
179 |                     all_data.append(data)
180 |         fin.close()  
181 |         return all_data
182 | 
183 |     def __init__(self, dataset='finance', embed_dim=300):
184 |         print("preparing {0} dataset...".format(dataset))
185 |         fname = {
186 |             'finance': {
187 |                 'train': './datasets/Clean_Train_Data.csv',
188 |                 'test': './datasets/Clean_Test_Data.csv'
189 |             },
190 |         }
191 |         text = ABSADatesetReader.__read_text__([fname[dataset]['train'], fname[dataset]['test']])
192 |         
193 |         bert_tokenizer = Tokenizer4Bert('bert-base-chinese')
194 |         #exit(0)
195 |         self.train_data = ABSADataset(ABSADatesetReader.__read_data__(fname[dataset]['train'], bert_tokenizer))
196 | 


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/train.py:
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  1 | # -*- coding: utf-8 -*-
  2 | 
  3 | import os
  4 | import math
  5 | import argparse
  6 | import random
  7 | import numpy
  8 | import torch
  9 | import torch.nn as nn
 10 | from bucket_iterator import BucketIterator
 11 | from sklearn import metrics
 12 | from data_utils import ABSADatesetReader
 13 | from models import BERT_SPC
 14 | from pytorch_pretrained_bert import BertModel
 15 | 
 16 | class Instructor:
 17 |     def __init__(self, opt):
 18 |         self.opt = opt
 19 | 
 20 |         absa_dataset = ABSADatesetReader(dataset=opt.dataset, embed_dim=opt.embed_dim)
 21 |         valset_len = int(len(absa_dataset.train_data) * 0.1)
 22 |         self.train_data_loader = BucketIterator(data=absa_dataset.train_data[:-valset_len], batch_size=opt.batch_size, shuffle=True)
 23 |         self.val_data_loader = BucketIterator(data=absa_dataset.train_data[-valset_len:], batch_size=opt.batch_size, shuffle=False)
 24 |         bert = BertModel.from_pretrained('bert-base-chinese')
 25 |         self.model = nn.DataParallel(opt.model_class(bert, opt).to(opt.device))
 26 | 
 27 |         self._print_args()
 28 | 
 29 |     def _print_args(self):
 30 |         n_trainable_params, n_nontrainable_params = 0, 0
 31 |         for p in self.model.parameters():
 32 |             n_params = torch.prod(torch.tensor(p.shape)).item()
 33 |             if p.requires_grad:
 34 |                 n_trainable_params += n_params
 35 |             else:
 36 |                 n_nontrainable_params += n_params
 37 |         print('>> n_trainable_params: {0}, n_nontrainable_params: {1}'.format(n_trainable_params, n_nontrainable_params))
 38 |         print('>> training arguments:')
 39 |         for arg in vars(self.opt):
 40 |             print('>>> {0}: {1}'.format(arg, getattr(self.opt, arg)))
 41 | 
 42 |     def _reset_params(self):
 43 |         for p in self.model.parameters():
 44 |             if p.requires_grad:
 45 |                 if len(p.shape) > 1:
 46 |                     self.opt.initializer(p)
 47 |                 else:
 48 |                     stdv = 1. / math.sqrt(p.shape[0])
 49 |                     torch.nn.init.uniform_(p, a=-stdv, b=stdv)
 50 | 
 51 |     def _train(self, criterion, optimizer):
 52 |         max_test_acc = 0
 53 |         max_f1 = 0
 54 |         global_step = 0
 55 |         continue_not_increase = 0
 56 |         for epoch in range(self.opt.num_epoch):
 57 |             print('>' * 100)
 58 |             print('epoch: ', epoch)
 59 |             n_correct, n_total = 0, 0
 60 |             increase_flag = False
 61 |             for i_batch, sample_batched in enumerate(self.train_data_loader):
 62 |                 global_step += 1
 63 | 
 64 |                 # switch model to training mode, clear gradient accumulators
 65 |                 self.model.train()
 66 |                 optimizer.zero_grad()
 67 | 
 68 |                 inputs = [sample_batched[col].to(self.opt.device) for col in self.opt.inputs_cols]
 69 |                 targets = sample_batched['negative'].to(self.opt.device)
 70 | 
 71 |                 outputs = self.model(inputs)
 72 |                 loss = criterion(outputs, targets)
 73 |                 loss.backward()
 74 |                 optimizer.step()
 75 | 
 76 |                 if global_step % self.opt.log_step == 0:
 77 |                     n_correct += (torch.argmax(outputs, -1) == targets).sum().item()
 78 |                     n_total += len(outputs)
 79 |                     train_acc = n_correct / n_total
 80 | 
 81 |                     test_acc, f1 = self._evaluate_acc_f1()
 82 |                     if test_acc > max_test_acc:
 83 |                         max_test_acc = test_acc
 84 |                     if f1 > max_f1:
 85 |                         increase_flag = True
 86 |                         max_f1 = f1
 87 |                         if self.opt.save:
 88 |                             if not os.path.exists('state_dict'):
 89 |                                 os.mkdir('state_dict')
 90 |                             torch.save(self.model.state_dict(), 'state_dict/'+self.opt.model_name+'_'+self.opt.dataset+'.pkl')
 91 |                             print('>> best model saved.')
 92 |                     print('loss: {:.4f}, acc: {:.4f}, test_acc: {:.4f}, f1: {:.4f}'.format(loss.item(), train_acc, test_acc, f1))
 93 |             if increase_flag == False:
 94 |                 continue_not_increase += 1
 95 |                 if continue_not_increase >= 5:
 96 |                     print('early stop.')
 97 |                     break
 98 |             else:
 99 |                 continue_not_increase = 0                    
100 |         return max_test_acc, max_f1
101 | 
102 |     def _evaluate_acc_f1(self):
103 |         # switch model to evaluation mode
104 |         self.model.eval()
105 |         n_test_correct, n_test_total = 0, 0
106 |         t_targets_all, t_outputs_all = None, None
107 |         with torch.no_grad():
108 |             for t_batch, t_sample_batched in enumerate(self.val_data_loader):
109 |                 t_inputs = [t_sample_batched[col].to(opt.device) for col in self.opt.inputs_cols]
110 |                 t_targets = t_sample_batched['negative'].to(opt.device)
111 |                 t_outputs = self.model(t_inputs)
112 | 
113 |                 n_test_correct += (torch.argmax(t_outputs, -1) == t_targets).sum().item()
114 |                 n_test_total += len(t_outputs)
115 | 
116 |                 if t_targets_all is None:
117 |                     t_targets_all = t_targets
118 |                     t_outputs_all = t_outputs
119 |                 else:
120 |                     t_targets_all = torch.cat((t_targets_all, t_targets), dim=0)
121 |                     t_outputs_all = torch.cat((t_outputs_all, t_outputs), dim=0)
122 | 
123 |         test_acc = n_test_correct / n_test_total
124 |         f1 = metrics.f1_score(t_targets_all.cpu(), torch.argmax(t_outputs_all, -1).cpu(), labels=[0, 1], average='macro')
125 |         return test_acc, f1
126 | 
127 |     def run(self):
128 |         # Loss and Optimizer
129 |         criterion = nn.CrossEntropyLoss()
130 |         _params = filter(lambda p: p.requires_grad, self.model.parameters())
131 |         optimizer = self.opt.optimizer(_params, lr=self.opt.learning_rate, weight_decay=self.opt.l2reg)
132 |         max_test_acc, max_f1 = self._train(criterion, optimizer)
133 |         print('max_test_acc: {0}     max_f1: {1}'.format(max_test_acc, max_f1))
134 | 
135 | 
136 | if __name__ == '__main__':
137 |     # Hyper Parameters
138 |     parser = argparse.ArgumentParser()
139 |     parser.add_argument('--model_name', default='bert', type=str)
140 |     parser.add_argument('--dataset', default='finance', type=str, help='finance')
141 |     parser.add_argument('--optimizer', default='adam', type=str)
142 |     parser.add_argument('--initializer', default='xavier_uniform_', type=str)
143 |     parser.add_argument('--learning_rate', default=2e-5, type=float)
144 |     parser.add_argument('--dropout', default=0.3, type=float)
145 |     parser.add_argument('--l2reg', default=1e-5, type=float)
146 |     parser.add_argument('--num_epoch', default=100, type=int)
147 |     parser.add_argument('--batch_size', default=32, type=int)
148 |     parser.add_argument('--log_step', default=30, type=int)
149 |     parser.add_argument('--embed_dim', default=300, type=int)
150 |     parser.add_argument('--hidden_dim', default=300, type=int)
151 |     parser.add_argument('--polarities_dim', default=2, type=int)
152 |     parser.add_argument('--save', default=False, type=bool)
153 |     parser.add_argument('--seed', default=776, type=int)
154 |     parser.add_argument('--device', default=None, type=str)
155 |     opt = parser.parse_args()
156 | 
157 |     model_classes = {
158 |         'bert': BERT_SPC,
159 |     }
160 |     input_colses = {
161 |         'bert': ['bert_text_indices', 'bert_segment_indices'],
162 |     }
163 |     initializers = {
164 |         'xavier_uniform_': torch.nn.init.xavier_uniform_,
165 |         'xavier_normal_': torch.nn.init.xavier_normal,
166 |         'orthogonal_': torch.nn.init.orthogonal_,
167 |     }
168 |     optimizers = {
169 |         'adam': torch.optim.Adam,  # default lr=0.001
170 |         'rmsprop': torch.optim.RMSprop,  # default lr=0.01
171 |         'sgd': torch.optim.SGD,
172 |     }
173 |     opt.model_class = model_classes[opt.model_name]
174 |     opt.inputs_cols = input_colses[opt.model_name]
175 |     opt.initializer = initializers[opt.initializer]
176 |     opt.optimizer = optimizers[opt.optimizer]
177 |     opt.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') \
178 |         if opt.device is None else torch.device(opt.device)
179 | 
180 |     if opt.seed is not None:
181 |         random.seed(opt.seed)
182 |         numpy.random.seed(opt.seed)
183 |         torch.manual_seed(opt.seed)
184 |         torch.cuda.manual_seed(opt.seed)
185 |         torch.backends.cudnn.deterministic = True
186 |         torch.backends.cudnn.benchmark = False
187 | 
188 |     ins = Instructor(opt)
189 |     ins.run()
190 | 


--------------------------------------------------------------------------------
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--------------------------------------------------------------------------------
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