Enhance LSTM model for small datasets and improve performance.

@@ -4,12 +4,19 @@ 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.model_selection import train_test_split, KFold, StratifiedKFold
 from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, confusion_matrix
+from sklearn.feature_extraction.text import TfidfVectorizer
+from sklearn.linear_model import LogisticRegression
 import jieba
-from transformers import BertTokenizer
+from transformers import BertTokenizer, BertModel
 import logging
 import os
+import random
+from torch.optim.lr_scheduler import ReduceLROnPlateau
+from gensim.models import KeyedVectors
+import json
+import torch.nn.functional as F
 
 # 配置日志记录
 logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')
@@ -49,17 +56,90 @@ class TextDataset(Dataset):
             'label': torch.tensor(label, dtype=torch.long)
         }
 
+class AttentionLayer(nn.Module):
+    """注意力层"""
+    def __init__(self, hidden_dim):
+        super().__init__()
+        self.attention = nn.Linear(hidden_dim, 1)
+        
+    def forward(self, lstm_output):
+        attention_weights = torch.softmax(self.attention(lstm_output), dim=1)
+        context_vector = torch.sum(attention_weights * lstm_output, dim=1)
+        return context_vector, attention_weights
+
+# 添加数据增强类
+class TextAugmenter:
+    def __init__(self, language='zh', synonyms_file=None):
+        self.language = language
+        self.synonyms_dict = self._load_synonyms(synonyms_file)
+        
+    def _load_synonyms(self, file_path):
+        base_dict = {
+            "很好": ["非常好", "太好了", "特别好", "相当好", "真不错"],
+            "糟糕": ["差劲", "很差", "不好", "太差", "糟透了"],
+            "一般": ["还行", "凑合", "普通", "马马虎虎", "中等"],
+            "满意": ["很满意", "挺好", "不错", "称心如意"],
+            "生气": ["愤怒", "恼火", "不爽", "气愤"],
+            "失望": ["伤心", "难过", "不满意", "遗憾"],
+            # 添加更多情感词汇对
+        }
+        
+        if file_path and os.path.exists(file_path):
+            try:
+                with open(file_path, 'r', encoding='utf-8') as f:
+                    custom_dict = json.load(f)
+                base_dict.update(custom_dict)
+            except Exception as e:
+                logger.warning(f"加载同义词典失败: {e}")
+                
+        return base_dict
+    
+    def synonym_replacement(self, text, n=1):
+        words = list(jieba.cut(text))
+        new_words = words.copy()
+        num_replaced = 0
+        
+        for word in list(set(words)):
+            if len(word) > 1 and num_replaced < n:
+                synonyms = self._get_synonyms(word)
+                if synonyms:
+                    synonym = random.choice(synonyms)
+                    new_words = [synonym if w == word else w for w in new_words]
+                    num_replaced += 1
+                    
+        return ''.join(new_words)
+    
+    def _get_synonyms(self, word):
+        return self.synonyms_dict.get(word, [])
+    
+    def augment(self, texts, labels, augment_ratio=0.5):
+        augmented_texts = []
+        augmented_labels = []
+        
+        for text, label in zip(texts, labels):
+            augmented_texts.append(text)
+            augmented_labels.append(label)
+            
+            if random.random() < augment_ratio:
+                aug_text = self.synonym_replacement(text)
+                augmented_texts.append(aug_text)
+                augmented_labels.append(label)
+        
+        return np.array(augmented_texts), np.array(augmented_labels)
+
 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):
+    def __init__(self, vocab_size, embedding_dim, hidden_dim, output_dim, n_layers=1, 
+                 bidirectional=True, dropout=0.3, pad_idx=0, pretrained_embeddings=None):
         super().__init__()
         
         # 嵌入层
         self.embedding = nn.Embedding(vocab_size, embedding_dim, padding_idx=pad_idx)
+        if pretrained_embeddings is not None:
+            self.embedding.weight.data.copy_(pretrained_embeddings)
+            self.embedding.weight.requires_grad = True
         
-        # LSTM层
         self.lstm = nn.LSTM(
             embedding_dim,
             hidden_dim,
@@ -69,11 +149,17 @@ class LSTMSentimentModel(nn.Module):
             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)
+        # 注意力层
+        self.attention = AttentionLayer(hidden_dim * 2 if bidirectional else hidden_dim)
+        
+        # 全连接层
+        fc_dim = hidden_dim * 2 if bidirectional else hidden_dim
+        self.fc1 = nn.Linear(fc_dim, fc_dim // 2)
+        self.fc2 = nn.Linear(fc_dim // 2, output_dim)
         
-        # Dropout层
         self.dropout = nn.Dropout(dropout)
+        self.bn = nn.BatchNorm1d(fc_dim // 2)
+        self.relu = nn.ReLU()
         
     def forward(self, text, attention_mask=None):
         # 文本通过嵌入层 [batch_size, seq_len] -> [batch_size, seq_len, embedding_dim]
@@ -95,27 +181,49 @@ class LSTMSentimentModel(nn.Module):
         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)
+        # 应用注意力机制
+        context_vector, attention_weights = self.attention(output)
+        
+        # 应用dropout和全连接层
+        x = self.dropout(context_vector)
+        x = self.fc1(x)
+        x = self.bn(x)
+        x = self.relu(x)
+        x = self.dropout(x)
+        x = self.fc2(x)
+        
+        return x, attention_weights
+
+# 添加早停类
+class EarlyStopping:
+    def __init__(self, patience=5, min_delta=0):
+        self.patience = patience
+        self.min_delta = min_delta
+        self.counter = 0
+        self.best_loss = None
+        self.early_stop = False
+        
+    def __call__(self, val_loss):
+        if self.best_loss is None:
+            self.best_loss = val_loss
+        elif val_loss > self.best_loss - self.min_delta:
+            self.counter += 1
+            if self.counter >= self.patience:
+                self.early_stop = True
         else:
-            hidden = hidden[-1]
-        
-        # 应用dropout
-        hidden = self.dropout(hidden)
-        
-        # 全连接层
-        return self.fc(hidden)
+            self.best_loss = val_loss
+            self.counter = 0
 
 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):
+                 embedding_dim=100, hidden_dim=64, output_dim=2, n_layers=1, 
+                 bidirectional=True, dropout=0.3, word2vec_path=None):
         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.embedding_dim = embedding_dim
         self.model = LSTMSentimentModel(
             vocab_size=vocab_size,
             embedding_dim=embedding_dim,
@@ -132,112 +240,201 @@ class LSTMModelManager:
             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")
         
+        self.augmenter = TextAugmenter()
+        self.early_stopping = EarlyStopping(patience=5)
+        
+        # 加载预训练词向量
+        self.pretrained_embeddings = None
+        if word2vec_path and os.path.exists(word2vec_path):
+            try:
+                word_vectors = KeyedVectors.load_word2vec_format(word2vec_path, binary=True)
+                self.pretrained_embeddings = self._build_embedding_matrix(word_vectors)
+                logger.info("成功加载预训练词向量")
+            except Exception as e:
+                logger.warning(f"加载预训练词向量失败: {e}")
+        
+        # 初始化对抗训练参数
+        self.epsilon = 0.01
+        self.alpha = 0.001
+        
+    def _build_embedding_matrix(self, word_vectors):
+        embedding_matrix = torch.zeros(self.vocab_size, self.embedding_dim)
+        for i in range(self.vocab_size):
+            try:
+                word = self.tokenizer.convert_ids_to_tokens(i)
+                if word in word_vectors:
+                    embedding_matrix[i] = torch.tensor(word_vectors[word])
+            except:
+                continue
+        return embedding_matrix
+    
+    def adversarial_training(self, batch, criterion):
+        """对抗训练步骤"""
+        # 计算原始损失
+        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)
+        
+        # 计算梯度
+        loss.backward(retain_graph=True)
+        
+        # 获取嵌入层的梯度
+        grad_embed = self.model.embedding.weight.grad.data
+        
+        # 生成对抗扰动
+        perturb = self.epsilon * torch.sign(grad_embed)
+        
+        # 应用扰动
+        self.model.embedding.weight.data.add_(perturb)
+        
+        # 计算对抗损失
+        outputs_adv, _ = self.model(input_ids, attention_mask)
+        loss_adv = criterion(outputs_adv, labels)
+        
+        # 恢复原始嵌入
+        self.model.embedding.weight.data.sub_(perturb)
+        
+        return loss + self.alpha * loss_adv
+    
+    def train_logistic_regression(self, train_texts, train_labels, val_texts=None, val_labels=None):
+        vectorizer = TfidfVectorizer(max_features=5000)
+        X_train = vectorizer.fit_transform(train_texts)
+        
+        if val_texts is None:
+            X_train, X_val, y_train, y_val = train_test_split(
+                X_train, train_labels, test_size=0.2, stratify=train_labels
+            )
+        else:
+            X_val = vectorizer.transform(val_texts)
+            y_train, y_val = train_labels, val_labels
+        
+        lr_model = LogisticRegression(class_weight='balanced')
+        lr_model.fit(X_train, y_train)
+        
+        val_pred = lr_model.predict(X_val)
+        lr_accuracy = accuracy_score(y_val, val_pred)
+        lr_f1 = f1_score(y_val, val_pred, average='macro')
+        
+        return lr_accuracy, lr_f1
+    
     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):
+              batch_size=16, epochs=10, learning_rate=2e-4):
         """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
-            )
+        # 首先训练逻辑回归作为基线
+        lr_accuracy, lr_f1 = self.train_logistic_regression(train_texts, train_labels, val_texts, val_labels)
+        logger.info(f"逻辑回归基线模型 - 准确率: {lr_accuracy:.4f}, F1: {lr_f1:.4f}")
         
-        # 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)
+        # 如果数据量小于1000，进行数据增强
+        if len(train_texts) < 1000:
+            train_texts, train_labels = self.augmenter.augment(train_texts, train_labels)
+            logger.info(f"数据增强后的训练集大小: {len(train_texts)}")
         
-        train_dataloader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
-        val_dataloader = DataLoader(val_dataset, batch_size=batch_size)
+        # 创建K折交叉验证
+        kf = StratifiedKFold(n_splits=5, shuffle=True, random_state=42)
+        fold_results = []
         
-        # 优化器和损失函数
-        optimizer = optim.Adam(self.model.parameters(), lr=learning_rate)
-        criterion = nn.CrossEntropyLoss()
+        for fold, (train_idx, val_idx) in enumerate(kf.split(train_texts, train_labels)):
+            logger.info(f"训练第 {fold+1} 折...")
+            
+            # 重置模型
+            self.model = self._create_model()
+            optimizer = optim.AdamW(self.model.parameters(), lr=learning_rate)
+            scheduler = ReduceLROnPlateau(optimizer, mode='min', patience=2)
+            criterion = nn.CrossEntropyLoss(weight=torch.tensor([1.0, 1.0]).to(self.device))
+            
+            # 准备数据
+            X_train, X_val = train_texts[train_idx], train_texts[val_idx]
+            y_train, y_val = train_labels[train_idx], train_labels[val_idx]
+            
+            train_dataset = TextDataset(X_train, y_train, self.tokenizer)
+            val_dataset = TextDataset(X_val, y_val, self.tokenizer)
+            
+            train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
+            val_loader = DataLoader(val_dataset, batch_size=batch_size)
             
-        # 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 = []
+                # 训练和验证逻辑
+                train_loss = self._train_epoch(train_loader, optimizer, criterion)
+                val_loss, val_acc, val_f1 = self._validate(val_loader, criterion)
                 
-            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)
+                scheduler.step(val_loss)
+                
+                if val_loss < best_val_loss:
+                    best_val_loss = val_loss
+                    if self.model_save_path:
+                        torch.save(self.model.state_dict(), 
+                                 f"{self.model_save_path}_fold{fold}.pt")
+                
+                if self.early_stopping(val_loss):
+                    break
+            
+            fold_results.append({
+                'val_loss': val_loss,
+                'val_accuracy': val_acc,
+                'val_f1': val_f1
+            })
+        
+        # 计算平均结果
+        avg_val_loss = np.mean([res['val_loss'] for res in fold_results])
+        avg_val_acc = np.mean([res['val_accuracy'] for res in fold_results])
+        avg_val_f1 = np.mean([res['val_f1'] for res in fold_results])
+        
+        logger.info(f"交叉验证平均结果 - 损失: {avg_val_loss:.4f}, 准确率: {avg_val_acc:.4f}, F1: {avg_val_f1:.4f}")
+        
+        # 如果LSTM模型效果比逻辑回归差，给出警告
+        if avg_val_acc < lr_accuracy:
+            logger.warning("LSTM模型性能低于逻辑回归基线，建议使用逻辑回归模型")
         
-                # 前向传播
+        return avg_val_loss, avg_val_acc, avg_val_f1
+
+    def _train_epoch(self, train_loader, optimizer, criterion):
+        self.model.train()
+        total_loss = 0
+        for batch in train_loader:
             optimizer.zero_grad()
-                outputs = self.model(input_ids, attention_mask)
             
-                # 计算损失
-                loss = criterion(outputs, labels)
-                train_loss += loss.item()
+            # 使用对抗训练
+            loss = self.adversarial_training(batch, criterion)
             
-                # 反向传播
             loss.backward()
+            torch.nn.utils.clip_grad_norm_(self.model.parameters(), max_norm=1.0)
             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())
+            total_loss += loss.item()
             
-            # 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')
+        return total_loss / len(train_loader)
     
-            # 验证模式
+    def _validate(self, val_loader, criterion):
         self.model.eval()
-            val_loss = 0
+        total_loss = 0
         val_preds = []
         val_labels_list = []
         
         with torch.no_grad():
-                for batch in val_dataloader:
+            for batch in val_loader:
                 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)
+                outputs, _ = self.model(input_ids, attention_mask)
                 loss = criterion(outputs, labels)
-                    val_loss += loss.item()
+                total_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}")
+        avg_loss = total_loss / len(val_loader)
+        accuracy = accuracy_score(val_labels_list, val_preds)
+        f1 = f1_score(val_labels_list, val_preds, average='macro')
         
-        return train_loss, val_loss, val_accuracy, val_f1
+        return avg_loss, accuracy, f1
     
     def evaluate(self, test_texts, test_labels, batch_size=32):
         """评估模型"""
@@ -263,7 +460,7 @@ class LSTMModelManager:
                 attention_mask = batch['attention_mask'].to(self.device)
                 labels = batch['label'].to(self.device)
                 
-                outputs = self.model(input_ids, attention_mask)
+                outputs, _ = self.model(input_ids, attention_mask)
                 loss = criterion(outputs, labels)
                 test_loss += loss.item()
                 
@@ -327,7 +524,7 @@ class LSTMModelManager:
                 input_ids = batch['input_ids'].to(self.device)
                 attention_mask = batch['attention_mask'].to(self.device)
                 
-                outputs = self.model(input_ids, attention_mask)
+                outputs, _ = self.model(input_ids, attention_mask)
                 probs = torch.softmax(outputs, dim=1)
                 _, predicted = torch.max(outputs, 1)