戒酒的李白
391 lines
15 KiB
Python
391 lines
15 KiB
Python
import torch
|
|
import torch.nn as nn
|
|
import torch.optim as optim
|
|
from torch.utils.data import Dataset, DataLoader
|
|
import numpy as np
|
|
import pandas as pd
|
|
from sklearn.model_selection import train_test_split
|
|
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, confusion_matrix
|
|
import jieba
|
|
from transformers import BertTokenizer
|
|
import logging
|
|
import os
|
|
|
|
# 配置日志记录
|
|
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')
|
|
logger = logging.getLogger('LSTM_model')
|
|
|
|
class TextDataset(Dataset):
|
|
"""文本数据集类，用于加载和预处理文本数据"""
|
|
|
|
def __init__(self, texts, labels, tokenizer, max_length=128):
|
|
self.texts = texts
|
|
self.labels = labels
|
|
self.tokenizer = tokenizer
|
|
self.max_length = max_length
|
|
|
|
def __len__(self):
|
|
return len(self.texts)
|
|
|
|
def __getitem__(self, idx):
|
|
text = str(self.texts[idx])
|
|
label = self.labels[idx]
|
|
|
|
# BERT分词并获得输入ID和注意力掩码
|
|
encoding = self.tokenizer.encode_plus(
|
|
text,
|
|
add_special_tokens=True,
|
|
max_length=self.max_length,
|
|
padding='max_length',
|
|
truncation=True,
|
|
return_attention_mask=True,
|
|
return_tensors='pt'
|
|
)
|
|
|
|
return {
|
|
'text': text,
|
|
'input_ids': encoding['input_ids'].flatten(),
|
|
'attention_mask': encoding['attention_mask'].flatten(),
|
|
'label': torch.tensor(label, dtype=torch.long)
|
|
}
|
|
|
|
class LSTMSentimentModel(nn.Module):
|
|
"""基于LSTM的情感分析模型"""
|
|
|
|
def __init__(self, vocab_size, embedding_dim, hidden_dim, output_dim, n_layers=2,
|
|
bidirectional=True, dropout=0.5, pad_idx=0):
|
|
super().__init__()
|
|
|
|
# 嵌入层
|
|
self.embedding = nn.Embedding(vocab_size, embedding_dim, padding_idx=pad_idx)
|
|
|
|
# LSTM层
|
|
self.lstm = nn.LSTM(
|
|
embedding_dim,
|
|
hidden_dim,
|
|
num_layers=n_layers,
|
|
bidirectional=bidirectional,
|
|
dropout=dropout if n_layers > 1 else 0,
|
|
batch_first=True
|
|
)
|
|
|
|
# 全连接层，如果是双向LSTM，输入维度需要翻倍
|
|
self.fc = nn.Linear(hidden_dim * 2 if bidirectional else hidden_dim, output_dim)
|
|
|
|
# Dropout层
|
|
self.dropout = nn.Dropout(dropout)
|
|
|
|
def forward(self, text, attention_mask=None):
|
|
# 文本通过嵌入层 [batch_size, seq_len] -> [batch_size, seq_len, embedding_dim]
|
|
embedded = self.embedding(text)
|
|
|
|
# 应用dropout
|
|
embedded = self.dropout(embedded)
|
|
|
|
# 通过LSTM [batch_size, seq_len, embedding_dim] -> [batch_size, seq_len, hidden_dim*2]
|
|
if attention_mask is not None:
|
|
# 创建打包的序列
|
|
lengths = attention_mask.sum(dim=1).to('cpu')
|
|
packed_embedded = nn.utils.rnn.pack_padded_sequence(
|
|
embedded, lengths, batch_first=True, enforce_sorted=False
|
|
)
|
|
packed_output, (hidden, cell) = self.lstm(packed_embedded)
|
|
# 解包序列
|
|
output, _ = nn.utils.rnn.pad_packed_sequence(packed_output, batch_first=True)
|
|
else:
|
|
output, (hidden, cell) = self.lstm(embedded)
|
|
|
|
# 如果是双向LSTM，需要拼接最后一层的前向和后向隐藏状态
|
|
if self.lstm.bidirectional:
|
|
hidden = torch.cat([hidden[-2], hidden[-1]], dim=1)
|
|
else:
|
|
hidden = hidden[-1]
|
|
|
|
# 应用dropout
|
|
hidden = self.dropout(hidden)
|
|
|
|
# 全连接层
|
|
return self.fc(hidden)
|
|
|
|
class LSTMModelManager:
|
|
"""LSTM模型管理类，用于训练、评估和预测"""
|
|
|
|
def __init__(self, bert_model_path, model_save_path=None, vocab_size=30522,
|
|
embedding_dim=128, hidden_dim=256, output_dim=2, n_layers=2,
|
|
bidirectional=True, dropout=0.5):
|
|
self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
|
|
self.tokenizer = BertTokenizer.from_pretrained(bert_model_path)
|
|
self.vocab_size = vocab_size
|
|
self.model = LSTMSentimentModel(
|
|
vocab_size=vocab_size,
|
|
embedding_dim=embedding_dim,
|
|
hidden_dim=hidden_dim,
|
|
output_dim=output_dim,
|
|
n_layers=n_layers,
|
|
bidirectional=bidirectional,
|
|
dropout=dropout,
|
|
pad_idx=self.tokenizer.pad_token_id
|
|
).to(self.device)
|
|
|
|
self.model_save_path = model_save_path
|
|
if model_save_path and os.path.exists(model_save_path):
|
|
self.model.load_state_dict(torch.load(model_save_path, map_location=self.device))
|
|
logger.info(f"已从 {model_save_path} 加载模型")
|
|
|
|
def train(self, train_texts, train_labels, val_texts=None, val_labels=None,
|
|
batch_size=32, learning_rate=2e-5, epochs=10, validation_split=0.2):
|
|
"""训练模型"""
|
|
logger.info("开始训练模型...")
|
|
|
|
# 如果没有提供验证集，从训练集中划分
|
|
if val_texts is None or val_labels is None:
|
|
train_texts, val_texts, train_labels, val_labels = train_test_split(
|
|
train_texts, train_labels, test_size=validation_split, random_state=42
|
|
)
|
|
|
|
# 创建数据集和数据加载器
|
|
train_dataset = TextDataset(train_texts, train_labels, self.tokenizer)
|
|
val_dataset = TextDataset(val_texts, val_labels, self.tokenizer)
|
|
|
|
train_dataloader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
|
|
val_dataloader = DataLoader(val_dataset, batch_size=batch_size)
|
|
|
|
# 优化器和损失函数
|
|
optimizer = optim.Adam(self.model.parameters(), lr=learning_rate)
|
|
criterion = nn.CrossEntropyLoss()
|
|
|
|
# 训练循环
|
|
best_val_loss = float('inf')
|
|
for epoch in range(epochs):
|
|
# 训练模式
|
|
self.model.train()
|
|
train_loss = 0
|
|
train_preds = []
|
|
train_labels_list = []
|
|
|
|
for batch in train_dataloader:
|
|
# 获取数据
|
|
input_ids = batch['input_ids'].to(self.device)
|
|
attention_mask = batch['attention_mask'].to(self.device)
|
|
labels = batch['label'].to(self.device)
|
|
|
|
# 前向传播
|
|
optimizer.zero_grad()
|
|
outputs = self.model(input_ids, attention_mask)
|
|
|
|
# 计算损失
|
|
loss = criterion(outputs, labels)
|
|
train_loss += loss.item()
|
|
|
|
# 反向传播
|
|
loss.backward()
|
|
optimizer.step()
|
|
|
|
# 收集预测和标签
|
|
_, predicted = torch.max(outputs, 1)
|
|
train_preds.extend(predicted.cpu().numpy())
|
|
train_labels_list.extend(labels.cpu().numpy())
|
|
|
|
# 计算训练集的评估指标
|
|
train_accuracy = accuracy_score(train_labels_list, train_preds)
|
|
train_f1 = f1_score(train_labels_list, train_preds, average='macro')
|
|
|
|
# 验证模式
|
|
self.model.eval()
|
|
val_loss = 0
|
|
val_preds = []
|
|
val_labels_list = []
|
|
|
|
with torch.no_grad():
|
|
for batch in val_dataloader:
|
|
input_ids = batch['input_ids'].to(self.device)
|
|
attention_mask = batch['attention_mask'].to(self.device)
|
|
labels = batch['label'].to(self.device)
|
|
|
|
outputs = self.model(input_ids, attention_mask)
|
|
loss = criterion(outputs, labels)
|
|
val_loss += loss.item()
|
|
|
|
_, predicted = torch.max(outputs, 1)
|
|
val_preds.extend(predicted.cpu().numpy())
|
|
val_labels_list.extend(labels.cpu().numpy())
|
|
|
|
# 计算验证集的评估指标
|
|
val_accuracy = accuracy_score(val_labels_list, val_preds)
|
|
val_f1 = f1_score(val_labels_list, val_preds, average='macro')
|
|
|
|
# 计算平均损失
|
|
train_loss /= len(train_dataloader)
|
|
val_loss /= len(val_dataloader)
|
|
|
|
logger.info(f'Epoch {epoch+1}/{epochs} | '
|
|
f'Train Loss: {train_loss:.4f} | '
|
|
f'Train Acc: {train_accuracy:.4f} | '
|
|
f'Train F1: {train_f1:.4f} | '
|
|
f'Val Loss: {val_loss:.4f} | '
|
|
f'Val Acc: {val_accuracy:.4f} | '
|
|
f'Val F1: {val_f1:.4f}')
|
|
|
|
# 保存最佳模型
|
|
if val_loss < best_val_loss and self.model_save_path:
|
|
best_val_loss = val_loss
|
|
torch.save(self.model.state_dict(), self.model_save_path)
|
|
logger.info(f"模型已保存到 {self.model_save_path}")
|
|
|
|
# 如果有保存路径但没有保存过模型，保存最后一轮的模型
|
|
if self.model_save_path and best_val_loss == float('inf'):
|
|
torch.save(self.model.state_dict(), self.model_save_path)
|
|
logger.info(f"最终模型已保存到 {self.model_save_path}")
|
|
|
|
return train_loss, val_loss, val_accuracy, val_f1
|
|
|
|
def evaluate(self, test_texts, test_labels, batch_size=32):
|
|
"""评估模型"""
|
|
logger.info("评估模型...")
|
|
|
|
# 创建测试数据集和数据加载器
|
|
test_dataset = TextDataset(test_texts, test_labels, self.tokenizer)
|
|
test_dataloader = DataLoader(test_dataset, batch_size=batch_size)
|
|
|
|
# 设置为评估模式
|
|
self.model.eval()
|
|
|
|
# 损失函数
|
|
criterion = nn.CrossEntropyLoss()
|
|
test_loss = 0
|
|
test_preds = []
|
|
test_probs = []
|
|
test_labels_list = []
|
|
|
|
with torch.no_grad():
|
|
for batch in test_dataloader:
|
|
input_ids = batch['input_ids'].to(self.device)
|
|
attention_mask = batch['attention_mask'].to(self.device)
|
|
labels = batch['label'].to(self.device)
|
|
|
|
outputs = self.model(input_ids, attention_mask)
|
|
loss = criterion(outputs, labels)
|
|
test_loss += loss.item()
|
|
|
|
probs = torch.softmax(outputs, dim=1)
|
|
_, predicted = torch.max(outputs, 1)
|
|
|
|
test_preds.extend(predicted.cpu().numpy())
|
|
test_probs.extend(probs.cpu().numpy())
|
|
test_labels_list.extend(labels.cpu().numpy())
|
|
|
|
# 计算平均损失
|
|
test_loss /= len(test_dataloader)
|
|
|
|
# 计算评估指标
|
|
accuracy = accuracy_score(test_labels_list, test_preds)
|
|
precision = precision_score(test_labels_list, test_preds, average='macro')
|
|
recall = recall_score(test_labels_list, test_preds, average='macro')
|
|
f1 = f1_score(test_labels_list, test_preds, average='macro')
|
|
conf_matrix = confusion_matrix(test_labels_list, test_preds)
|
|
|
|
logger.info(f'Test Loss: {test_loss:.4f}')
|
|
logger.info(f'Accuracy: {accuracy:.4f}')
|
|
logger.info(f'Precision: {precision:.4f}')
|
|
logger.info(f'Recall: {recall:.4f}')
|
|
logger.info(f'F1 Score: {f1:.4f}')
|
|
logger.info(f'Confusion Matrix:\n{conf_matrix}')
|
|
|
|
return {
|
|
'loss': test_loss,
|
|
'accuracy': accuracy,
|
|
'precision': precision,
|
|
'recall': recall,
|
|
'f1': f1,
|
|
'confusion_matrix': conf_matrix,
|
|
'predictions': test_preds,
|
|
'probabilities': test_probs
|
|
}
|
|
|
|
def predict_batch(self, texts, batch_size=32):
|
|
"""批量预测文本的情感"""
|
|
if not texts:
|
|
return None, None
|
|
|
|
# 确保文本是列表格式
|
|
if isinstance(texts, str):
|
|
texts = [texts]
|
|
|
|
# 创建数据集（没有标签，使用占位符）
|
|
dummy_labels = [0] * len(texts)
|
|
dataset = TextDataset(texts, dummy_labels, self.tokenizer)
|
|
dataloader = DataLoader(dataset, batch_size=batch_size)
|
|
|
|
# 设置为评估模式
|
|
self.model.eval()
|
|
|
|
all_preds = []
|
|
all_probs = []
|
|
|
|
with torch.no_grad():
|
|
for batch in dataloader:
|
|
input_ids = batch['input_ids'].to(self.device)
|
|
attention_mask = batch['attention_mask'].to(self.device)
|
|
|
|
outputs = self.model(input_ids, attention_mask)
|
|
probs = torch.softmax(outputs, dim=1)
|
|
_, predicted = torch.max(outputs, 1)
|
|
|
|
all_preds.extend(predicted.cpu().numpy())
|
|
all_probs.extend(probs.cpu().numpy())
|
|
|
|
return all_preds, all_probs
|
|
|
|
def predict(self, text):
|
|
"""预测单个文本的情感"""
|
|
predictions, probabilities = self.predict_batch([text])
|
|
if predictions is not None and len(predictions) > 0:
|
|
return predictions[0], probabilities[0]
|
|
return None, None
|
|
|
|
# 创建全局模型实例
|
|
lstm_model_manager = LSTMModelManager(
|
|
bert_model_path='model_pro/bert_model',
|
|
model_save_path='model_pro/lstm_model.pt'
|
|
)
|
|
|
|
# 测试代码
|
|
if __name__ == "__main__":
|
|
# 加载数据
|
|
train_data = pd.read_csv('model_pro/train.csv')
|
|
dev_data = pd.read_csv('model_pro/dev.csv')
|
|
test_data = pd.read_csv('model_pro/test.csv')
|
|
|
|
# 处理数据
|
|
train_texts = train_data['text'].values
|
|
train_labels = train_data['label'].values
|
|
|
|
dev_texts = dev_data['text'].values
|
|
dev_labels = dev_data['label'].values
|
|
|
|
test_texts = test_data['text'].values
|
|
test_labels = test_data['label'].values
|
|
|
|
# 训练模型
|
|
lstm_model_manager.train(
|
|
train_texts, train_labels,
|
|
val_texts=dev_texts, val_labels=dev_labels,
|
|
batch_size=32, epochs=5
|
|
)
|
|
|
|
# 评估模型
|
|
results = lstm_model_manager.evaluate(test_texts, test_labels)
|
|
|
|
# 测试预测功能
|
|
test_sentences = [
|
|
"这件事情做得非常好",
|
|
"服务太差了，态度恶劣",
|
|
"这个产品质量一般，但价格便宜",
|
|
"我对这家公司非常满意",
|
|
]
|
|
|
|
for sentence in test_sentences:
|
|
pred, prob = lstm_model_manager.predict(sentence)
|
|
label = '良好' if pred == 0 else '不良'
|
|
confidence = prob[pred]
|
|
print(f"句子: '{sentence}' 预测结果: {label} (置信度: {confidence:.2%})") |