Local sentiment analysis upload.

SentimentAnalysisModel/WeiboSentiment_MachineLearning/bayes_train.py

@@ -0,0 +1,155 @@
+# -*- coding: utf-8 -*-
+"""
+朴素贝叶斯情感分析模型训练脚本
+"""
+import argparse
+import pandas as pd
+from typing import List, Tuple
+from sklearn.feature_extraction.text import CountVectorizer
+from sklearn.naive_bayes import MultinomialNB
+from sklearn.metrics import accuracy_score, f1_score
+
+from base_model import BaseModel
+from utils import stopwords
+
+
+class BayesModel(BaseModel):
+    """朴素贝叶斯情感分析模型"""
+    
+    def __init__(self):
+        super().__init__("Bayes")
+        
+    def train(self, train_data: List[Tuple[str, int]], **kwargs) -> None:
+        """训练朴素贝叶斯模型
+        
+        Args:
+            train_data: 训练数据，格式为[(text, label), ...]
+            **kwargs: 其他参数
+        """
+        print(f"开始训练 {self.model_name} 模型...")
+        
+        # 准备数据
+        df_train = pd.DataFrame(train_data, columns=["words", "label"])
+        
+        # 特征编码（词袋模型）
+        print("构建词袋模型...")
+        self.vectorizer = CountVectorizer(
+            token_pattern=r'\[?\w+\]?', 
+            stop_words=stopwords
+        )
+        
+        X_train = self.vectorizer.fit_transform(df_train["words"])
+        y_train = df_train["label"]
+        
+        print(f"特征维度: {X_train.shape[1]}")
+        
+        # 训练模型
+        print("训练朴素贝叶斯分类器...")
+        self.model = MultinomialNB()
+        self.model.fit(X_train, y_train)
+        
+        self.is_trained = True
+        print(f"{self.model_name} 模型训练完成！")
+        
+    def predict(self, texts: List[str]) -> List[int]:
+        """预测文本情感
+        
+        Args:
+            texts: 待预测文本列表
+            
+        Returns:
+            预测结果列表
+        """
+        if not self.is_trained:
+            raise ValueError(f"模型 {self.model_name} 尚未训练，请先调用train方法")
+            
+        # 特征转换
+        X = self.vectorizer.transform(texts)
+        
+        # 预测
+        predictions = self.model.predict(X)
+        
+        return predictions.tolist()
+    
+    def predict_single(self, text: str) -> Tuple[int, float]:
+        """预测单条文本的情感
+        
+        Args:
+            text: 待预测文本
+            
+        Returns:
+            (predicted_label, confidence)
+        """
+        if not self.is_trained:
+            raise ValueError(f"模型 {self.model_name} 尚未训练，请先调用train方法")
+            
+        # 特征转换
+        X = self.vectorizer.transform([text])
+        
+        # 预测
+        prediction = self.model.predict(X)[0]
+        probabilities = self.model.predict_proba(X)[0]
+        confidence = max(probabilities)
+        
+        return int(prediction), float(confidence)
+
+
+def main():
+    """主函数"""
+    parser = argparse.ArgumentParser(description='朴素贝叶斯情感分析模型训练')
+    parser.add_argument('--train_path', type=str, default='./data/weibo2018/train.txt',
+                        help='训练数据路径')
+    parser.add_argument('--test_path', type=str, default='./data/weibo2018/test.txt',
+                        help='测试数据路径')
+    parser.add_argument('--model_path', type=str, default='./model/bayes_model.pkl',
+                        help='模型保存路径')
+    parser.add_argument('--eval_only', action='store_true',
+                        help='仅评估已有模型，不进行训练')
+    
+    args = parser.parse_args()
+    
+    # 创建模型
+    model = BayesModel()
+    
+    if args.eval_only:
+        # 仅评估模式
+        print("评估模式：加载已有模型进行评估")
+        model.load_model(args.model_path)
+        
+        # 加载测试数据
+        _, test_data = BaseModel.load_data(args.train_path, args.test_path)
+        
+        # 评估模型
+        model.evaluate(test_data)
+    else:
+        # 训练模式
+        # 加载数据
+        train_data, test_data = BaseModel.load_data(args.train_path, args.test_path)
+        
+        # 训练模型
+        model.train(train_data)
+        
+        # 评估模型
+        model.evaluate(test_data)
+        
+        # 保存模型
+        model.save_model(args.model_path)
+        
+        # 示例预测
+        print("\n示例预测:")
+        test_texts = [
+            "今天天气真好，心情很棒",
+            "这部电影太无聊了，浪费时间",
+            "哈哈哈，太有趣了"
+        ]
+        
+        for text in test_texts:
+            pred, conf = model.predict_single(text)
+            sentiment = "正面" if pred == 1 else "负面"
+            print(f"文本: {text}")
+            print(f"预测: {sentiment} (置信度: {conf:.4f})")
+            print()
+
+
+if __name__ == "__main__":
+    main()