Local sentiment analysis upload.
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戒酒的李白
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# -*- coding: utf-8 -*-
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"""
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Qwen3模型基础类，统一接口
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"""
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import os
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import pickle
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from abc import ABC, abstractmethod
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from typing import List, Tuple, Dict, Any
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import pandas as pd
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from sklearn.metrics import accuracy_score, f1_score, classification_report
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from sklearn.model_selection import train_test_split
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class BaseQwenModel(ABC):
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"""Qwen3情感分析模型基类"""
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def __init__(self, model_name: str):
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self.model_name = model_name
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self.model = None
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self.is_trained = False
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@abstractmethod
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def train(self, train_data: List[Tuple[str, int]], **kwargs) -> None:
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"""训练模型"""
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pass
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@abstractmethod
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def predict(self, texts: List[str]) -> List[int]:
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"""预测文本情感"""
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pass
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def predict_single(self, text: str) -> Tuple[int, float]:
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"""预测单条文本的情感
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Args:
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text: 待预测文本
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Returns:
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(predicted_label, confidence)
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"""
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predictions = self.predict([text])
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return predictions[0], 0.0 # 默认置信度为0
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def evaluate(self, test_data: List[Tuple[str, int]]) -> Dict[str, float]:
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"""评估模型性能"""
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if not self.is_trained:
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raise ValueError(f"模型 {self.model_name} 尚未训练，请先调用train方法")
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texts = [item[0] for item in test_data]
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labels = [item[1] for item in test_data]
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predictions = self.predict(texts)
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accuracy = accuracy_score(labels, predictions)
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f1 = f1_score(labels, predictions, average='weighted')
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print(f"\n{self.model_name} 模型评估结果:")
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print(f"准确率: {accuracy:.4f}")
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print(f"F1分数: {f1:.4f}")
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print("\n详细报告:")
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print(classification_report(labels, predictions))
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return {
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'accuracy': accuracy,
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'f1_score': f1,
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'classification_report': classification_report(labels, predictions)
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}
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@abstractmethod
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def save_model(self, model_path: str = None) -> None:
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"""保存模型到文件"""
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pass
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@abstractmethod
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def load_model(self, model_path: str) -> None:
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"""从文件加载模型"""
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pass
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@staticmethod
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def load_data(train_path: str = None, test_path: str = None, csv_path: str = 'dataset/weibo_senti_100k.csv') -> Tuple[List[Tuple[str, int]], List[Tuple[str, int]]]:
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"""加载训练和测试数据
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Args:
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train_path: 训练数据txt文件路径（可选）
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test_path: 测试数据txt文件路径（可选）
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csv_path: CSV数据文件路径（默认使用）
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"""
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# 优先尝试使用CSV文件
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if os.path.exists(csv_path):
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print(f"从CSV文件加载数据: {csv_path}")
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df = pd.read_csv(csv_path)
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# 检查数据格式
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if 'review' in df.columns and 'label' in df.columns:
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# 将DataFrame转换为元组列表
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data = [(row['review'], row['label']) for _, row in df.iterrows()]
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# 分割训练和测试数据，固定测试集为5000条
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total_samples = len(data)
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if total_samples > 5000:
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test_size = 5000
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train_data, test_data = train_test_split(
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data,
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test_size=test_size,
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random_state=42,
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stratify=[label for _, label in data]
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)
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else:
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# 如果总数据不足5000条，使用20%作为测试集
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train_data, test_data = train_test_split(
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data,
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test_size=0.2,
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random_state=42,
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stratify=[label for _, label in data]
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)
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print(f"训练数据量: {len(train_data)}")
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print(f"测试数据量: {len(test_data)}")
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return train_data, test_data
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else:
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print(f"CSV文件格式不正确，缺少'review'或'label'列")
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# 如果CSV不存在，尝试使用txt文件
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elif train_path and test_path and os.path.exists(train_path) and os.path.exists(test_path):
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def load_corpus(path):
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data = []
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with open(path, "r", encoding="utf8") as f:
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for line in f:
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parts = line.strip().split("\t")
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if len(parts) >= 2:
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content = parts[0]
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sentiment = int(parts[1])
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data.append((content, sentiment))
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return data
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print("从txt文件加载训练数据...")
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train_data = load_corpus(train_path)
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print(f"训练数据量: {len(train_data)}")
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print("从txt文件加载测试数据...")
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test_data = load_corpus(test_path)
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print(f"测试数据量: {len(test_data)}")
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return train_data, test_data
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else:
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# 如果都没有，提供样例数据创建指导
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print("未找到数据文件!")
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print("请确保以下文件之一存在:")
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print(f"1. CSV文件: {csv_path}")
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print(f"2. txt文件: {train_path} 和 {test_path}")
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print("\n数据格式要求:")
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print("CSV文件: 包含'review'和'label'列")
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print("txt文件: 每行格式为'文本内容\\t标签'")
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# 创建样例数据
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sample_data = [
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("今天天气真好，心情很棒!", 1),
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("这部电影太无聊了", 0),
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("非常喜欢这个产品", 1),
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("服务态度很差", 0),
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("质量不错，值得推荐", 1)
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]
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print("使用样例数据进行演示...")
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train_data = sample_data * 20 # 扩充样例数据
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test_data = sample_data * 5
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return train_data, test_data
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