The BERT_CTM module is finally completed

 import os
 import os
-from transformers.models.bert import BertTokenizer, BertModel
-import torch
+os.environ["TOKENIZERS_PARALLELISM"] = "false"
+import pandas as pd
 from tqdm import tqdm
 from tqdm import tqdm
+from transformers.models.bert import BertTokenizer, BertModel
+from contextualized_topic_models.models.ctm import CombinedTM
+from contextualized_topic_models.utils.data_preparation import TopicModelDataPreparation
 import numpy as np
 import numpy as np
+import torch
 import jieba
 import jieba
-from contextualized_topic_models.utils.data_preparation import TopicModelDataPreparation
-from contextualized_topic_models.models.ctm import CombinedTM
+import pickle  # 用于保存和加载模型
 
 
 class BERT_CTM_Model:
 class BERT_CTM_Model:
-    def __init__(self, bert_model_path, ctm_tokenizer_path, n_components=12, num_epochs=50, device=None):
-        # 确定设备 (CPU/GPU)
-        self.device = device if device else ("cuda" if torch.cuda.is_available() else "cpu")
-        
-        # 检查模型路径是否存在
-        if not os.path.exists(bert_model_path):
-            raise ValueError(f"BERT模型路径不存在: {bert_model_path}")
-        if not os.path.exists(ctm_tokenizer_path):
-            raise ValueError(f"CTM分词器路径不存在: {ctm_tokenizer_path}")
-
+    def __init__(self, bert_model_path, ctm_tokenizer_path, n_components=12, num_epochs=50, model_save_path='./ctm_model'):
+        self.bert_model_path = bert_model_path
+        self.ctm_tokenizer_path = ctm_tokenizer_path
+        self.n_components = n_components
+        self.num_epochs = num_epochs
+        self.model_save_path = model_save_path
         # 加载BERT模型和tokenizer
         # 加载BERT模型和tokenizer
-        self.tokenizer = BertTokenizer.from_pretrained(bert_model_path)
-        self.model = BertModel.from_pretrained(bert_model_path).to(self.device)
+        self.tokenizer = BertTokenizer.from_pretrained(self.bert_model_path)
+        self.model = BertModel.from_pretrained(self.bert_model_path)
 
 
         # 创建CTM数据预处理对象
         # 创建CTM数据预处理对象
-        self.tp = TopicModelDataPreparation(ctm_tokenizer_path)
-        self.n_components = n_components
-        self.num_epochs = num_epochs
-        self.ctm_model = None
+        self.tp = TopicModelDataPreparation(self.ctm_tokenizer_path)
 
 
+    def chinese_tokenize(self, text):
+        """使用jieba对中文文本进行分词"""
+        return " ".join(jieba.cut(text))
+    
     def get_bert_embeddings(self, texts):
     def get_bert_embeddings(self, texts):
-        """使用BERT模型批量生成文本的嵌入向量"""
+        """使用BERT模型生成文本的嵌入向量"""
         embeddings = []
         embeddings = []
         for text in tqdm(texts, desc="Processing texts with BERT"):
         for text in tqdm(texts, desc="Processing texts with BERT"):
-            inputs = self.tokenizer(text, return_tensors="pt", padding="max_length", truncation=True, max_length=80).to(self.device)
+            inputs = self.tokenizer(text, return_tensors="pt", padding="max_length", truncation=True, max_length=80)
             with torch.no_grad():
             with torch.no_grad():
                 outputs = self.model(**inputs)
                 outputs = self.model(**inputs)
-            embeddings.append(outputs.last_hidden_state[:, 0, :].cpu().numpy())  # [batch_size, hidden_size]
+            embeddings.append(outputs.last_hidden_state.cpu().numpy())  # [batch_size, sequence_length, hidden_size]
         return np.vstack(embeddings)
         return np.vstack(embeddings)
 
 
-    def chinese_tokenize(self, text):
-        """使用jieba对中文文本进行分词"""
-        return " ".join(jieba.cut(text))
-
-    def train_ctm(self, texts):
-        """训练CTM模型"""
-        try:
-            # 分词并准备BOW文本
-            bow_texts = [self.chinese_tokenize(text) for text in texts]
-            training_dataset = self.tp.fit(text_for_contextual=texts, text_for_bow=bow_texts)
-
-            # 训练CTM
-            self.ctm_model = CombinedTM(bow_size=len(self.tp.vocab), contextual_size=768, 
-                                        n_components=self.n_components, num_epochs=self.num_epochs)
-            self.ctm_model.fit(training_dataset)
-            print("CTM模型训练完成")
-        except Exception as e:
-            print(f"训练CTM模型时发生错误: {e}")
-
-    def predict(self, texts):
-        """使用训练好的CTM模型预测新文本的主题分布"""
-        if not self.ctm_model:
-            raise ValueError("模型尚未训练或加载，无法进行预测")
-        
-        try:
-            bow_texts = [self.chinese_tokenize(text) for text in texts]
-            testing_dataset = self.tp.transform(text_for_contextual=texts, text_for_bow=bow_texts)
-            topic_distributions = self.ctm_model.get_doc_topic_distribution(testing_dataset)
-            return topic_distributions
-        except Exception as e:
-            print(f"预测主题时发生错误: {e}")
-            return None
-
-    def save_model(self, path):
-        """保存训练后的CTM模型"""
-        if self.ctm_model:
-            self.ctm_model.save(path)
-            print(f"CTM模型已保存至: {path}")
-        else:
-            print("未找到已训练的CTM模型，无法保存")
-
-    def load_model(self, path):
-        """加载已保存的CTM模型"""
-        if os.path.exists(path):
-            self.ctm_model = CombinedTM.load(path)
-            print(f"CTM模型已加载自: {path}")
-        else:
-            print(f"无法加载模型，路径不存在: {path}")
+    def save_model(self, ctm):
+        """保存CTM模型、词袋和BoW的vectorizer"""
+        os.makedirs(self.model_save_path, exist_ok=True)
+        with open(f"{self.model_save_path}/ctm_model.pkl", 'wb') as f:
+            pickle.dump(ctm, f)
+        with open(f"{self.model_save_path}/vocab.pkl", 'wb') as f:
+            pickle.dump(self.tp.vocab, f)
+        with open(f"{self.model_save_path}/vectorizer.pkl", 'wb') as f:  # 保存BoW的vectorizer
+            pickle.dump(self.tp.vectorizer, f)
+        print(f"CTM模型和词袋保存到: {self.model_save_path}")
+
+    def load_model(self):
+        """加载CTM模型、词袋和BoW的vectorizer"""
+        with open(f"{self.model_save_path}/ctm_model.pkl", 'rb') as f:
+            ctm = pickle.load(f)
+        with open(f"{self.model_save_path}/vocab.pkl", 'rb') as f:
+            self.tp.vocab = pickle.load(f)
+        with open(f"{self.model_save_path}/vectorizer.pkl", 'rb') as f:  # 加载BoW的vectorizer
+            self.tp.vectorizer = pickle.load(f)
+        print(f"CTM模型、词袋和vectorizer加载成功")
+        return ctm
+
+    def train(self, csv_file):
+        """训练BERT + CTM模型并保存最终的特征向量和标签"""
+        # 读取CSV文件中的文本和标签
+        data = pd.read_csv(csv_file)
+        texts = data['TEXT'].tolist()
+        labels = data['label'].tolist()
+
+        # Step 1: 获取BERT的嵌入向量
+        print("Extracting BERT embeddings...")
+        bert_embeddings = self.get_bert_embeddings(texts)  # [batch_size, sequence_length, hidden_size]
+
+        # Step 2: 准备CTM数据
+        print("Preparing data for CTM using training set...")
+        bow_texts = [self.chinese_tokenize(text) for text in texts]
+        training_dataset = self.tp.fit(text_for_contextual=texts, text_for_bow=bow_texts)
+
+        # Step 3: 替换BERT嵌入
+        training_dataset._X = bert_embeddings[:, 0, :]  # 只使用第一个token的向量用于CTM
+
+        # Step 4: 训练CTM模型
+        print("Training CTM model...")
+        ctm = CombinedTM(bow_size=len(self.tp.vocab), contextual_size=768, n_components=self.n_components, num_epochs=self.num_epochs)
+        ctm.fit(train_dataset=training_dataset, verbose=True)
+
+        # Step 5: 保存CTM模型和词袋
+        self.save_model(ctm)
+
+        # Step 6: 获取CTM的特征向量
+        print("Generating CTM features...")
+        ctm_features = ctm.get_doc_topic_distribution(training_dataset)  # [batch_size, n_components]
+
+        # Step 7: 将CTM特征扩展为与BERT的sequence长度一致
+        sequence_length = bert_embeddings.shape[1]
+        ctm_features_expanded = np.repeat(ctm_features[:, np.newaxis, :], sequence_length, axis=1)  # [batch_size, sequence_length, n_components]
+
+        # Step 8: 拼接BERT嵌入和CTM特征
+        final_embeddings = np.concatenate([bert_embeddings, ctm_features_expanded], axis=-1)  # [batch_size, sequence_length, hidden_size + n_components]
+
+        return bert_embeddings
 
 
 if __name__ == "__main__":
 if __name__ == "__main__":
-    # 设定BERT和CTM模型的路径
-    bert_model_path = './bert_model'
-    ctm_tokenizer_path = './sentence_bert_model'
-    
-    # 初始化模型
-    model = BERT_CTM_Model(bert_model_path, ctm_tokenizer_path)
-
-    # 示例文本
-    texts = ["这是第一个文本", "这是第二个文本"]
-    
-    # 训练CTM模型
-    model.train_ctm(texts)
-
-    # 保存CTM模型
-    model.save_model('./trained_ctm_model')
-
-    # 加载CTM模型
-    model.load_model('./trained_ctm_model')
-
-    # 预测新文本的主题分布
-    new_texts = ["这是一个新的文本", "另外一个新文本"]
-    topic_distributions = model.predict(new_texts)
-
-    # 输出预测结果
-    if topic_distributions is not None:
-        for idx, distribution in enumerate(topic_distributions):
-            print(f"文本 {idx+1} 的主题分布: {distribution}")
+    # 创建BERT_CTM_Model实例
+    model = BERT_CTM_Model(
+        bert_model_path='./bert_model',  # BERT模型的路径
+        ctm_tokenizer_path='./sentence_bert_model',  # CTM分词器的路径
+        n_components=12,  # 主题数量
+        num_epochs=50,  # 训练轮次
+        model_save_path='./ctm_model',  # 保存路径
+    )
+
+    # 传入CSV文件路径进行训练
+    model.train("./train.csv")