零基础入门NLP - 新闻文本分类(五、基于深度学习的文本分类2 - 2 - TextCNN)
TextCNN利用CNN（卷积神经网络）进行文本特征抽取，不同大小的卷积核分别抽取n-gram特征，卷积计算出的特征图经过MaxPooling保留最大的特征值，然后将拼接成一个向量作为文本的表示。
基于TextCNN原始论文的设定，分别采用了100个大小为2,3,4的卷积核，最后得到的文本向量大小为100*3=300维。
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)
# split data to 10 fold
fold_num = 10
data_file = '../data/train_set.csv'
import pandas as pd
def all_data2fold(fold_num, num=10000):
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(10)
# build train, dev, test data
fold_id = 9
# 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 = '../data/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}
# build vocab
from collections import Counter
from transformers import BasicTokenizer
basic_tokenizer = BasicTokenizer()
class Vocab():
def __init__(self, train_data):
self.min_count = 5
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)
# 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
word2vec_path = '../emb/word2vec.txt'
dropout = 0.15
class WordCNNEncoder(nn.Module):
def __init__(self, vocab):
super(WordCNNEncoder, self).__init__()
self.dropout = nn.Dropout(dropout)
self.word_dims = 100
self.word_embed = nn.Embedding(vocab.word_size, self.word_dims, padding_idx=0)
extword_embed = vocab.load_pretrained_embs(word2vec_path)
extword_size, word_dims = extword_embed.shape
logging.info("Load extword embed: words %d, dims %d." % (extword_size, word_dims))
self.extword_embed = nn.Embedding(extword_size, word_dims, padding_idx=0)
self.extword_embed.weight.data.copy_(torch.from_numpy(extword_embed))
self.extword_embed.weight.requires_grad = False
input_size = self.word_dims
self.filter_sizes = [2, 3, 4] # n-gram window
self.out_channel = 100
self.convs = nn.ModuleList([nn.Conv2d(1, self.out_channel, (filter_size, input_size), bias=True)
for filter_size in self.filter_sizes])
def forward(self, word_ids, extword_ids):
# word_ids: sen_num x sent_len
# extword_ids: sen_num x sent_len
# batch_masks: sen_num x sent_len
sen_num, sent_len = word_ids.shape
word_embed = self.word_embed(word_ids) # sen_num x sent_len x 100
extword_embed = self.extword_embed(extword_ids)
batch_embed = word_embed + extword_embed
if self.training:
batch_embed = self.dropout(batch_embed)
batch_embed.unsqueeze_(1) # sen_num x 1 x sent_len x 100
pooled_outputs = []
for i in range(len(self.filter_sizes)):
filter_height = sent_len - self.filter_sizes[i] + 1
conv = self.convs[i](batch_embed)
hidden = F.relu(conv) # sen_num x out_channel x filter_height x 1
mp = nn.MaxPool2d((filter_height, 1)) # (filter_height, filter_width)
pooled = mp(hidden).reshape(sen_num,
self.out_channel) # sen_num x out_channel x 1 x 1 -> sen_num x out_channel
pooled_outputs.append(pooled)
reps = torch.cat(pooled_outputs, dim=1) # sen_num x total_out_channel
if self.training:
reps = self.dropout(reps)
return reps
# 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
# build model
class Model(nn.Module):
def __init__(self, vocab):
super(Model, self).__init__()
self.sent_rep_size = 300
self.doc_rep_size = sent_hidden_size * 2
self.all_parameters = {}
parameters = []
self.word_encoder = WordCNNEncoder(vocab)
parameters.extend(list(filter(lambda p: p.requires_grad, self.word_encoder.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
logging.info('Build model with cnn 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)
# build optimizer
learning_rate = 2e-4
decay = .75
decay_step = 1000
class Optimizer:
def __init__(self, model_parameters):
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)
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
# 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 '' 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, 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, max_segment)
doc = []
for sent_len, sent_words in sents_words:
word_ids = vocab.word2id(sent_words)
extword_ids = vocab.extword2id(sent_words)
doc.append([sent_len, word_ids, extword_ids])
examples.append([id, len(doc), doc])
logging.info('Total %d docs.' % len(examples))
return examples
# 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]
batched_data.extend(list(batch_slice(sorted_data, batch_size)))
if shuffle:
np.random.shuffle(batched_data)
for batch in batched_data:
yield batch
# 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'))
# build trainer
import time
from sklearn.metrics import classification_report
clip = 5.0
epochs = 1
early_stops = 3
log_interval = 50
test_batch_size = 128
train_batch_size = 128
save_model = './cnn.bin'
save_test = './cnn.csv'
class Trainer():
def __init__(self, model, vocab):
self.model = model
self.report = True
self.train_data = get_examples(train_data, vocab)
self.batch_num = int(np.ceil(len(self.train_data) / float(train_batch_size)))
self.dev_data = get_examples(dev_data, vocab)
# criterion
self.criterion = nn.CrossEntropyLoss()
# label name
self.target_names = vocab.target_names
# optimizer
self.optimizer = Optimizer(model.all_parameters)
# 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()
y_pred = []
y_true = []
with torch.no_grad():
for batch_data in data_iter(self.dev_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
# train
trainer = Trainer(model, vocab)
trainer.train()
# test
trainer.test()