文章目录
- 学习过程
- 赛题理解
- 学习目标
- 赛题数据
- 数据标签
- 评测指标
- 解题思路
- BERT
- 代码
学习过程
20年当时自身功底是比较零基础(会写些基础的Python[三个科学计算包]数据分析)，一开始看这块其实挺懵的，不会就去问百度或其他人，当时遇见困难挺害怕的，但22后面开始力扣题【目前已刷好几轮，博客没写力扣文章之前，力扣排名靠前已刷有5遍左右，排名靠后刷3次左右，代码功底也在一步一步提升】不断地刷、遇见代码不懂的代码，也开始去打印print去理解，到后面问其他人的问题越来越少，个人自主学习、自主解决能力也得到了进一步增强。
赛题理解
- 赛题名称：零基础入门NLP之新闻文本分类
- 赛题目标：通过这道赛题可以引导大家走入自然语言处理的世界，带大家接触NLP的预处理、模型构建和模型训练等知识点。
- 赛题任务：赛题以自然语言处理为背景，要求选手对新闻文本进行分类，这是一个典型的字符识别问题。
学习目标
- 理解赛题背景与赛题数据
- 完成赛题报名和数据下载，理解赛题的解题思路
赛题数据
赛题以匿名处理后的新闻数据为赛题数据，数据集报名后可见并可下载。赛题数据为新闻文本，并按照字符级别进行匿名处理。整合划分出14个候选分类类别：财经、彩票、房产、股票、家居、教育、科技、社会、时尚、时政、体育、星座、游戏、娱乐的文本数据。
赛题数据由以下几个部分构成：训练集20w条样本，测试集A包括5w条样本，测试集B包括5w条样本。为了预防选手人工标注测试集的情况，我们将比赛数据的文本按照字符级别进行了匿名处理。
数据标签
处理后的赛题训练数据如下：
在数据集中标签的对应的关系如下：{‘科技’: 0, ‘股票’: 1, ‘体育’: 2, ‘娱乐’: 3, ‘时政’: 4, ‘社会’: 5, ‘教育’: 6, ‘财经’: 7, ‘家居’: 8, ‘游戏’: 9, ‘房产’: 10, ‘时尚’: 11, ‘彩票’: 12, ‘星座’: 13}
评测指标
评价标准为类别f1_score的均值，选手提交结果与实际测试集的类别进行对比，结果越大越好。
解题思路
赛题思路分析：赛题本质是一个文本分类问题，需要根据每句的字符进行分类。但赛题给出的数据是匿名化的，不能直接使用中文分词等操作，这个是赛题的难点。
因此本次赛题的难点是需要对匿名字符进行建模，进而完成文本分类的过程。由于文本数据是一种典型的非结构化数据，因此可能涉及到特征提取和分类模型两个部分。为了减低参赛难度，我们提供了一些解题思路供大家参考：
思路1：TF-IDF + 机器学习分类器
直接使用TF-IDF对文本提取特征，并使用分类器进行分类。在分类器的选择上，可以使用SVM、LR、或者XGBoost。
思路2：FastText
FastText是入门款的词向量，利用Facebook提供的FastText工具，可以快速构建出分类器。
思路3：WordVec + 深度学习分类器
WordVec是进阶款的词向量，并通过构建深度学习分类完成分类。深度学习分类的网络结构可以选择TextCNN、TextRNN或者BiLSTM。
思路4：Bert词向量
Bert是高配款的词向量，具有强大的建模学习能力。
这里使用思路1（TF-IDF + 机器学习分类器） 及 思路4（Bert词向量）
BERT
微调将最后一层的第一个token即[CLS]的隐藏向量作为句子的表示，然后输入到softmax层进行分类。
预训练BERT以及相关代码下载地址：链接: https://pan.baidu.com/s/1zd6wN7elGgp1NyuzYKpvGQ 提取码: tmp5
在原基础上，更改了不少参数，目前调试后该参数效果在原基础上是最佳的
代码
print("程序开始")
import logging
import random
import numpy as np
import torch
logging.basicConfig(level=logging.INFO, format='%(asctime)-15s %(levelname)s: %(message)s')
# set seed
seed = 666
random.seed(seed)
np.random.seed(seed)
torch.cuda.manual_seed(seed)
torch.manual_seed(seed)
# set cuda
gpu = 0
use_cuda = gpu >= 0 and torch.cuda.is_available()
if use_cuda:
torch.cuda.set_device(gpu)
device = torch.device("cuda", gpu)
else:
device = torch.device("cpu")
logging.info("Use cuda: %s, gpu id: %d.", use_cuda, gpu)
print("开始split data to 20 fold")
# split data to 20 fold
fold_num = 20
data_file = 'train_set.csv'
import pandas as pd
def all_data2fold(fold_num, num=20000):
fold_data = []
f = pd.read_csv(data_file, sep='\t', encoding='UTF-8')
texts = f['text'].tolist()[:num]
labels = f['label'].tolist()[:num]
total = len(labels)
index = list(range(total))
np.random.shuffle(index)
all_texts = []
all_labels = []
for i in index:
all_texts.append(texts[i])
all_labels.append(labels[i])
label2id = {}
for i in range(total):
label = str(all_labels[i])
if label not in label2id:
label2id[label] = [i]
else:
label2id[label].append(i)
all_index = [[] for _ in range(fold_num)]
for label, data in label2id.items():
# print(label, len(data))
batch_size = int(len(data) / fold_num)
other = len(data) - batch_size * fold_num
for i in range(fold_num):
cur_batch_size = batch_size + 1 if i < other else batch_size
# print(cur_batch_size)
batch_data = [data[i * batch_size + b] for b in range(cur_batch_size)]
all_index[i].extend(batch_data)
batch_size = int(total / fold_num)
other_texts = []
other_labels = []
other_num = 0
start = 0
for fold in range(fold_num):
num = len(all_index[fold])
texts = [all_texts[i] for i in all_index[fold]]
labels = [all_labels[i] for i in all_index[fold]]
if num > batch_size:
fold_texts = texts[:batch_size]
other_texts.extend(texts[batch_size:])
fold_labels = labels[:batch_size]
other_labels.extend(labels[batch_size:])
other_num += num - batch_size
elif num < batch_size:
end = start + batch_size - num
fold_texts = texts + other_texts[start: end]
fold_labels = labels + other_labels[start: end]
start = end
else:
fold_texts = texts
fold_labels = labels
assert batch_size == len(fold_labels)
# shuffle
index = list(range(batch_size))
np.random.shuffle(index)
shuffle_fold_texts = []
shuffle_fold_labels = []
for i in index:
shuffle_fold_texts.append(fold_texts[i])
shuffle_fold_labels.append(fold_labels[i])
data = {'label': shuffle_fold_labels, 'text': shuffle_fold_texts}
fold_data.append(data)
logging.info("Fold lens %s", str([len(data['label']) for data in fold_data]))
return fold_data
fold_data = all_data2fold(20)
print("结束split data to 20 fold")
print("开始build train, dev, test data")
# build train, dev, test data
fold_id = 19
# dev
dev_data = fold_data[fold_id]
# train
train_texts = []
train_labels = []
for i in range(0, fold_id):
data = fold_data[i]
train_texts.extend(data['text'])
train_labels.extend(data['label'])
train_data = {'label': train_labels, 'text': train_texts}
# test
test_data_file = 'test_a.csv'
f = pd.read_csv(test_data_file, sep='\t', encoding='UTF-8')
texts = f['text'].tolist()
test_data = {'label': [0] * len(texts), 'text': texts}
print("结束build train, dev, test data")
print("开始build vocab")
# build vocab
from collections import Counter
from transformers import BasicTokenizer
basic_tokenizer = BasicTokenizer()
class Vocab():
def __init__(self, train_data):
self.min_count = 0
self.pad = 0
self.unk = 1
self._id2word = ['[PAD]', '[UNK]']
self._id2extword = ['[PAD]', '[UNK]']
self._id2label = []
self.target_names = []
self.build_vocab(train_data)
reverse = lambda x: dict(zip(x, range(len(x))))
self._word2id = reverse(self._id2word)
self._label2id = reverse(self._id2label)
logging.info("Build vocab: words %d, labels %d." % (self.word_size, self.label_size))
def build_vocab(self, data):
self.word_counter = Counter()
for text in data['text']:
words = text.split()
for word in words:
self.word_counter[word] += 1
for word, count in self.word_counter.most_common():
if count >= self.min_count:
self._id2word.append(word)
label2name = {0: '科技', 1: '股票', 2: '体育', 3: '娱乐', 4: '时政', 5: '社会', 6: '教育', 7: '财经',
8: '家居', 9: '游戏', 10: '房产', 11: '时尚', 12: '彩票', 13: '星座'}
self.label_counter = Counter(data['label'])
for label in range(len(self.label_counter)):
count = self.label_counter[label]
self._id2label.append(label)
self.target_names.append(label2name[label])
def load_pretrained_embs(self, embfile):
with open(embfile, encoding='utf-8') as f:
lines = f.readlines()
items = lines[0].split()
word_count, embedding_dim = int(items[0]), int(items[1])
index = len(self._id2extword)
embeddings = np.zeros((word_count + index, embedding_dim))
for line in lines[1:]:
values = line.split()
self._id2extword.append(values[0])
vector = np.array(values[1:], dtype='float64')
embeddings[self.unk] += vector
embeddings[index] = vector
index += 1
embeddings[self.unk] = embeddings[self.unk] / word_count
embeddings = embeddings / np.std(embeddings)
reverse = lambda x: dict(zip(x, range(len(x))))
self._extword2id = reverse(self._id2extword)
assert len(set(self._id2extword)) == len(self._id2extword)
return embeddings
def word2id(self, xs):
if isinstance(xs, list):
return [self._word2id.get(x, self.unk) for x in xs]
return self._word2id.get(xs, self.unk)
def extword2id(self, xs):
if isinstance(xs, list):
return [self._extword2id.get(x, self.unk) for x in xs]
return self._extword2id.get(xs, self.unk)
def label2id(self, xs):
if isinstance(xs, list):
return [self._label2id.get(x, self.unk) for x in xs]
return self._label2id.get(xs, self.unk)
@property
def word_size(self):
return len(self._id2word)
@property
def extword_size(self):
return len(self._id2extword)
@property
def label_size(self):
return len(self._id2label)
vocab = Vocab(train_data)
print("结束build vocab")
print("开始build module")
# build module
import torch.nn as nn
import torch.nn.functional as F
class Attention(nn.Module):
def __init__(self, hidden_size):
super(Attention, self).__init__()
self.weight = nn.Parameter(torch.Tensor(hidden_size, hidden_size))
self.weight.data.normal_(mean=0.0, std=0.05)
self.bias = nn.Parameter(torch.Tensor(hidden_size))
b = np.zeros(hidden_size, dtype=np.float32)
self.bias.data.copy_(torch.from_numpy(b))
self.query = nn.Parameter(torch.Tensor(hidden_size))
self.query.data.normal_(mean=0.0, std=0.05)
def forward(self, batch_hidden, batch_masks):
# batch_hidden: b x len x hidden_size (2 * hidden_size of lstm)
# batch_masks: b x len
# linear
key = torch.matmul(batch_hidden, self.weight) + self.bias # b x len x hidden
# compute attention
outputs = torch.matmul(key, self.query) # b x len
masked_outputs = outputs.masked_fill((1 - batch_masks).bool(), float(-1e32))
attn_scores = F.softmax(masked_outputs, dim=1) # b x len
# 对于全零向量，-1e32的结果为 1/len, -inf为nan, 额外补0
masked_attn_scores = attn_scores.masked_fill((1 - batch_masks).bool(), 0.0)
# sum weighted sources
batch_outputs = torch.bmm(masked_attn_scores.unsqueeze(1), key).squeeze(1) # b x hidden
return batch_outputs, attn_scores
# build word encoder
csv_path = './bert/bert-mini/'
bert_path = '/students/julyedu_554294/NLP/news/bert/bert-mini'
dropout = 0.18
from transformers import BertModel
class WordBertEncoder(nn.Module):
def __init__(self):
super(WordBertEncoder, self).__init__()
self.dropout = nn.Dropout(dropout)
self.tokenizer = WhitespaceTokenizer()
self.bert = BertModel.from_pretrained(bert_path)
self.pooled = False
logging.info('Build Bert encoder with pooled {}.'.format(self.pooled))
def encode(self, tokens):
tokens = self.tokenizer.tokenize(tokens)
return tokens
def get_bert_parameters(self):
no_decay = ['bias', 'LayerNorm.weight']
optimizer_parameters = [
{'params': [p for n, p in self.bert.named_parameters() if not any(nd in n for nd in no_decay)],
'weight_decay': 0.01},
{'params': [p for n, p in self.bert.named_parameters() if any(nd in n for nd in no_decay)],
'weight_decay': 0.0}
]
return optimizer_parameters
def forward(self, input_ids, token_type_ids):
# input_ids: sen_num x bert_len
# token_type_ids: sen_num x bert_len
# sen_num x bert_len x 256, sen_num x 256
sequence_output, pooled_output = self.bert(input_ids=input_ids, token_type_ids=token_type_ids)
#print('sequence_output:', sequence_output)
#print('pooled_output:', pooled_output)
if self.pooled:
reps = pooled_output
else:
reps = sequence_output[:, 0, :] # sen_num x 256
if self.training:
reps = self.dropout(reps)
return reps
class WhitespaceTokenizer():
"""WhitespaceTokenizer with vocab."""
def __init__(self):
vocab_file = csv_path + 'vocab.txt'
self._token2id = self.load_vocab(vocab_file)
self._id2token = {v: k for k, v in self._token2id.items()}
self.max_len = 256
self.unk = 1
logging.info("Build Bert vocab with size %d." % (self.vocab_size))
def load_vocab(self, vocab_file):
f = open(vocab_file, 'r')
lines = f.readlines()
lines = list(map(lambda x: x.strip(), lines))
vocab = dict(zip(lines, range(len(lines))))
return vocab
def tokenize(self, tokens):
assert len(tokens) <= self.max_len - 2
tokens = ["[CLS]"] + tokens + ["[SEP]"]
output_tokens = self.token2id(tokens)
return output_tokens
def token2id(self, xs):
if isinstance(xs, list):
return [self._token2id.get(x, self.unk) for x in xs]
return self._token2id.get(xs, self.unk)
@property
def vocab_size(self):
return len(self._id2token)
# build sent encoder
sent_hidden_size = 256
sent_num_layers = 2
class SentEncoder(nn.Module):
def __init__(self, sent_rep_size):
super(SentEncoder, self).__init__()
self.dropout = nn.Dropout(dropout)
self.sent_lstm = nn.LSTM(
input_size=sent_rep_size,
hidden_size=sent_hidden_size,
num_layers=sent_num_layers,
batch_first=True,
bidirectional=True
)
def forward(self, sent_reps, sent_masks):
# sent_reps: b x doc_len x sent_rep_size
# sent_masks: b x doc_len
sent_hiddens, _ = self.sent_lstm(sent_reps) # b x doc_len x hidden*2
sent_hiddens = sent_hiddens * sent_masks.unsqueeze(2)
if self.training:
sent_hiddens = self.dropout(sent_hiddens)
return sent_hiddens
print("结束build module")
print("开始build model")
# build model
class Model(nn.Module):
def __init__(self, vocab):
super(Model, self).__init__()
self.sent_rep_size = 256
self.doc_rep_size = sent_hidden_size * 2
self.all_parameters = {}
parameters = []
self.word_encoder = WordBertEncoder()
bert_parameters = self.word_encoder.get_bert_parameters()
self.sent_encoder = SentEncoder(self.sent_rep_size)
self.sent_attention = Attention(self.doc_rep_size)
parameters.extend(list(filter(lambda p: p.requires_grad, self.sent_encoder.parameters())))
parameters.extend(list(filter(lambda p: p.requires_grad, self.sent_attention.parameters())))
self.out = nn.Linear(self.doc_rep_size, vocab.label_size, bias=True)
parameters.extend(list(filter(lambda p: p.requires_grad, self.out.parameters())))
if use_cuda:
self.to(device)
if len(parameters) > 0:
self.all_parameters["basic_parameters"] = parameters
self.all_parameters["bert_parameters"] = bert_parameters
logging.info('Build model with bert word encoder, lstm sent encoder.')
para_num = sum([np.prod(list(p.size())) for p in self.parameters()])
logging.info('Model param num: %.2f M.' % (para_num / 1e6))
def forward(self, batch_inputs):
# batch_inputs(batch_inputs1, batch_inputs2): b x doc_len x sent_len
# batch_masks : b x doc_len x sent_len
batch_inputs1, batch_inputs2, batch_masks = batch_inputs
batch_size, max_doc_len, max_sent_len = batch_inputs1.shape[0], batch_inputs1.shape[1], batch_inputs1.shape[2]
batch_inputs1 = batch_inputs1.view(batch_size * max_doc_len, max_sent_len) # sen_num x sent_len
batch_inputs2 = batch_inputs2.view(batch_size * max_doc_len, max_sent_len) # sen_num x sent_len
batch_masks = batch_masks.view(batch_size * max_doc_len, max_sent_len) # sen_num x sent_len
sent_reps = self.word_encoder(batch_inputs1, batch_inputs2) # sen_num x sent_rep_size
sent_reps = sent_reps.view(batch_size, max_doc_len, self.sent_rep_size) # b x doc_len x sent_rep_size
batch_masks = batch_masks.view(batch_size, max_doc_len, max_sent_len) # b x doc_len x max_sent_len
sent_masks = batch_masks.bool().any(2).float() # b x doc_len
sent_hiddens = self.sent_encoder(sent_reps, sent_masks) # b x doc_len x doc_rep_size
doc_reps, atten_scores = self.sent_attention(sent_hiddens, sent_masks) # b x doc_rep_size
batch_outputs = self.out(doc_reps) # b x num_labels
return batch_outputs
model = Model(vocab)
print("结束build model")
print("开始build optimizer")
# build optimizer
learning_rate = 2e-4
bert_lr = 2.5e-5
decay = .95
decay_step = 1000
from transformers import AdamW, get_linear_schedule_with_warmup
class Optimizer:
def __init__(self, model_parameters, steps):
self.all_params = []
self.optims = []
self.schedulers = []
for name, parameters in model_parameters.items():
if name.startswith("basic"):
optim = torch.optim.Adam(parameters, lr=learning_rate)
self.optims.append(optim)
l = lambda step: decay ** (step // decay_step)
scheduler = torch.optim.lr_scheduler.LambdaLR(optim, lr_lambda=l)
self.schedulers.append(scheduler)
self.all_params.extend(parameters)
elif name.startswith("bert"):
optim_bert = AdamW(parameters, bert_lr, eps=1e-8)
self.optims.append(optim_bert)
scheduler_bert = get_linear_schedule_with_warmup(optim_bert, 0, steps)
self.schedulers.append(scheduler_bert)
for group in parameters:
for p in group['params']:
self.all_params.append(p)
else:
Exception("no nameed parameters.")
self.num = len(self.optims)
def step(self):
for optim, scheduler in zip(self.optims, self.schedulers):
optim.step()
scheduler.step()
optim.zero_grad()
def zero_grad(self):
for optim in self.optims:
optim.zero_grad()
def get_lr(self):
lrs = tuple(map(lambda x: x.get_lr()[-1], self.schedulers))
lr = ' %.5f' * self.num
res = lr % lrs
return res
print("结束build optimizer")
print("开始build dataset")
def sentence_split(text, vocab, max_sent_len=256, max_segment=16):
words = text.strip().split()
document_len = len(words)
index = list(range(0, document_len, max_sent_len))
index.append(document_len)
segments = []
for i in range(len(index) - 1):
segment = words[index[i]: index[i + 1]]
assert len(segment) > 0
segment = [word if word in vocab._id2word else '<UNK>' for word in segment]
segments.append([len(segment), segment])
assert len(segments) > 0
if len(segments) > max_segment:
segment_ = int(max_segment / 2)
return segments[:segment_] + segments[-segment_:]
else:
return segments
def get_examples(data, word_encoder, vocab, max_sent_len=256, max_segment=8):
label2id = vocab.label2id
examples = []
for text, label in zip(data['text'], data['label']):
# label
id = label2id(label)
# words
sents_words = sentence_split(text, vocab, max_sent_len-2, max_segment)
doc = []
for sent_len, sent_words in sents_words:
token_ids = word_encoder.encode(sent_words)
sent_len = len(token_ids)
token_type_ids = [0] * sent_len
doc.append([sent_len, token_ids, token_type_ids])
examples.append([id, len(doc), doc])
logging.info('Total %d docs.' % len(examples))
return examples
print("结束build dataset")
print("开始build loader")
# build loader
def batch_slice(data, batch_size):
batch_num = int(np.ceil(len(data) / float(batch_size)))
for i in range(batch_num):
cur_batch_size = batch_size if i < batch_num - 1 else len(data) - batch_size * i
docs = [data[i * batch_size + b] for b in range(cur_batch_size)]
yield docs
def data_iter(data, batch_size, shuffle=True, noise=1.0):
"""
randomly permute data, then sort by source length, and partition into batches
ensure that the length of sentences in each batch
"""
batched_data = []
if shuffle:
np.random.shuffle(data)
lengths = [example[1] for example in data]
noisy_lengths = [- (l + np.random.uniform(- noise, noise)) for l in lengths]
sorted_indices = np.argsort(noisy_lengths).tolist()
sorted_data = [data[i] for i in sorted_indices]
else:
sorted_data =data
batched_data.extend(list(batch_slice(sorted_data, batch_size)))
if shuffle:
np.random.shuffle(batched_data)
for batch in batched_data:
yield batch
print("结束build loader")
print("开始some function")
# some function
from sklearn.metrics import f1_score, precision_score, recall_score
def get_score(y_ture, y_pred):
y_ture = np.array(y_ture)
y_pred = np.array(y_pred)
f1 = f1_score(y_ture, y_pred, average='macro') * 100
p = precision_score(y_ture, y_pred, average='macro') * 100
r = recall_score(y_ture, y_pred, average='macro') * 100
return str((reformat(p, 2), reformat(r, 2), reformat(f1, 2))), reformat(f1, 2)
def reformat(num, n):
return float(format(num, '0.' + str(n) + 'f'))
print("结束some function")
print("开始build trainer")
# build trainer
import time
from sklearn.metrics import classification_report
clip = 5.0
epochs = 20
early_stops = 6
log_interval = 150
test_batch_size = 64
train_batch_size = 64
save_model = './bert_submission_0306_12_change_canshu_taidaxiugai_kuoda2.bin'
save_test = './bert_submission_0306_12_change_canshu_taidaxiugai_kuoda2.csv'
class Trainer():
def __init__(self, model, vocab):
self.model = model
self.report = True
self.train_data = get_examples(train_data, model.word_encoder, vocab)
self.batch_num = int(np.ceil(len(self.train_data) / float(train_batch_size)))
self.dev_data = get_examples(dev_data, model.word_encoder, vocab)
self.test_data = get_examples(test_data, model.word_encoder, vocab)
# criterion
self.criterion = nn.CrossEntropyLoss()
# label name
self.target_names = vocab.target_names
# optimizer
self.optimizer = Optimizer(model.all_parameters, steps=self.batch_num * epochs)
# count
self.step = 0
self.early_stop = -1
self.best_train_f1, self.best_dev_f1 = 0, 0
self.last_epoch = epochs
def train(self):
logging.info('Start training...')
for epoch in range(1, epochs + 1):
train_f1 = self._train(epoch)
dev_f1 = self._eval(epoch)
if self.best_dev_f1 <= dev_f1:
logging.info(
"Exceed history dev = %.2f, current dev = %.2f" % (self.best_dev_f1, dev_f1))
torch.save(self.model.state_dict(), save_model)
self.best_train_f1 = train_f1
self.best_dev_f1 = dev_f1
self.early_stop = 0
else:
self.early_stop += 1
if self.early_stop == early_stops:
logging.info(
"Eearly stop in epoch %d, best train: %.2f, dev: %.2f" % (
epoch - early_stops, self.best_train_f1, self.best_dev_f1))
self.last_epoch = epoch
break
def test(self):
self.model.load_state_dict(torch.load(save_model))
self._eval(self.last_epoch + 1, test=True)
def _train(self, epoch):
self.optimizer.zero_grad()
self.model.train()
start_time = time.time()
epoch_start_time = time.time()
overall_losses = 0
losses = 0
batch_idx = 1
y_pred = []
y_true = []
for batch_data in data_iter(self.train_data, train_batch_size, shuffle=True):
torch.cuda.empty_cache()
batch_inputs, batch_labels = self.batch2tensor(batch_data)
batch_outputs = self.model(batch_inputs)
loss = self.criterion(batch_outputs, batch_labels)
loss.backward()
loss_value = loss.detach().cpu().item()
losses += loss_value
overall_losses += loss_value
y_pred.extend(torch.max(batch_outputs, dim=1)[1].cpu().numpy().tolist())
y_true.extend(batch_labels.cpu().numpy().tolist())
nn.utils.clip_grad_norm_(self.optimizer.all_params, max_norm=clip)
for optimizer, scheduler in zip(self.optimizer.optims, self.optimizer.schedulers):
optimizer.step()
scheduler.step()
self.optimizer.zero_grad()
self.step += 1
if batch_idx % log_interval == 0:
elapsed = time.time() - start_time
lrs = self.optimizer.get_lr()
logging.info(
'| epoch {:3d} | step {:3d} | batch {:3d}/{:3d} | lr{} | loss {:.4f} | s/batch {:.2f}'.format(
epoch, self.step, batch_idx, self.batch_num, lrs,
losses / log_interval,
elapsed / log_interval))
losses = 0
start_time = time.time()
batch_idx += 1
overall_losses /= self.batch_num
during_time = time.time() - epoch_start_time
# reformat
overall_losses = reformat(overall_losses, 4)
score, f1 = get_score(y_true, y_pred)
logging.info(
'| epoch {:3d} | score {} | f1 {} | loss {:.4f} | time {:.2f}'.format(epoch, score, f1,
overall_losses,
during_time))
if set(y_true) == set(y_pred) and self.report:
report = classification_report(y_true, y_pred, digits=4, target_names=self.target_names)
logging.info('\n' + report)
return f1
def _eval(self, epoch, test=False):
self.model.eval()
start_time = time.time()
data = self.test_data if test else self.dev_data
y_pred = []
y_true = []
with torch.no_grad():
for batch_data in data_iter(data, test_batch_size, shuffle=False):
torch.cuda.empty_cache()
batch_inputs, batch_labels = self.batch2tensor(batch_data)
batch_outputs = self.model(batch_inputs)
y_pred.extend(torch.max(batch_outputs, dim=1)[1].cpu().numpy().tolist())
y_true.extend(batch_labels.cpu().numpy().tolist())
score, f1 = get_score(y_true, y_pred)
during_time = time.time() - start_time
if test:
df = pd.DataFrame({'label': y_pred})
df.to_csv(save_test, index=False, sep=',')
else:
logging.info(
'| epoch {:3d} | dev | score {} | f1 {} | time {:.2f}'.format(epoch, score, f1,
during_time))
if set(y_true) == set(y_pred) and self.report:
report = classification_report(y_true, y_pred, digits=4, target_names=self.target_names)
logging.info('\n' + report)
return f1
def batch2tensor(self, batch_data):
'''
[[label, doc_len, [[sent_len, [sent_id0, ...], [sent_id1, ...]], ...]]
'''
batch_size = len(batch_data)
doc_labels = []
doc_lens = []
doc_max_sent_len = []
for doc_data in batch_data:
doc_labels.append(doc_data[0])
doc_lens.append(doc_data[1])
sent_lens = [sent_data[0] for sent_data in doc_data[2]]
max_sent_len = max(sent_lens)
doc_max_sent_len.append(max_sent_len)
max_doc_len = max(doc_lens)
max_sent_len = max(doc_max_sent_len)
batch_inputs1 = torch.zeros((batch_size, max_doc_len, max_sent_len), dtype=torch.int64)
batch_inputs2 = torch.zeros((batch_size, max_doc_len, max_sent_len), dtype=torch.int64)
batch_masks = torch.zeros((batch_size, max_doc_len, max_sent_len), dtype=torch.float32)
batch_labels = torch.LongTensor(doc_labels)
for b in range(batch_size):
for sent_idx in range(doc_lens[b]):
sent_data = batch_data[b][2][sent_idx]
for word_idx in range(sent_data[0]):
batch_inputs1[b, sent_idx, word_idx] = sent_data[1][word_idx]
batch_inputs2[b, sent_idx, word_idx] = sent_data[2][word_idx]
batch_masks[b, sent_idx, word_idx] = 1
if use_cuda:
batch_inputs1 = batch_inputs1.to(device)
batch_inputs2 = batch_inputs2.to(device)
batch_masks = batch_masks.to(device)
batch_labels = batch_labels.to(device)
return (batch_inputs1, batch_inputs2, batch_masks), batch_labels
print("结束build trainer")
print("开始train")
# train
trainer = Trainer(model, vocab)
trainer.train()
print("结束train")
print("开始test")
# test
trainer.test()
print("结束test")
print("程序结束")
logging.basicConfig(level=logging.INFO, format='%(asctime)-15s %(levelname)s: %(message)s')
logging.info("Use cuda: %s, gpu id: %d.", use_cuda, gpu)
score:0.9221
比赛源自：阿里云天池大赛 - 零基础入门NLP - 新闻文本分类
