脚本如何实现文件内容模糊粗糙随机森林

本文目录导读：

1. 数据准备与预处理
2. 数据加载与使用示例
3. 完整实现（包含评估）
4. 高级特性实现

的模糊粗糙随机森林分类器。

数据准备与预处理

import numpy as np
import pandas as pd
from sklearn.ensemble import RandomForestClassifier
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report
import jieba
import re
class FuzzyRoughRandomForest:
    def __init__(self, n_estimators=100, random_state=42):
        self.n_estimators = n_estimators
        self.random_state = random_state
        self.vectorizer = TfidfVectorizer(max_features=1000)
        self.classifier = RandomForestClassifier(
            n_estimators=n_estimators,
            random_state=random_state
        )
    def fuzzy_preprocessing(self, text):
        """模糊预处理：去除噪声、处理模糊内容"""
        # 去除特殊字符
        text = re.sub(r'[^\u4e00-\u9fa5a-zA-Z0-9\s]', '', text)
        # 全角转半角
        text = text.replace('，', ',').replace('。', '.').replace('！', '!')
        # 模糊处理：去除短词（粗糙处理）
        words = [w for w in text.split() if len(w) > 1]
        return ' '.join(words)
    def rough_feature_extraction(self, texts):
        """粗糙特征提取"""
        # 使用TF-IDF进行特征提取
        features = self.vectorizer.fit_transform(texts)
        return features.toarray()
    def fuzzy_classification(self, threshold=0.5):
        """模糊分类：添加置信度阈值"""
        predictions = self.classifier.predict_proba(self.X_test)
        # 应用模糊阈值
        fuzzy_predictions = (predictions[:, 1] >= threshold).astype(int)
        return fuzzy_predictions
    def train(self, texts, labels):
        """训练模型"""
        # 模糊预处理
        processed_texts = [self.fuzzy_preprocessing(text) for text in texts]
        # 分割数据集
        self.X_train, self.X_test, self.y_train, self.y_test = train_test_split(
            processed_texts, labels, test_size=0.2, random_state=self.random_state
        )
        # 特征提取
        self.X_train_features = self.rough_feature_extraction(self.X_train)
        self.X_test_features = self.vectorizer.transform(self.X_test)
        # 训练分类器
        self.classifier.fit(self.X_train_features, self.y_train)
    def predict(self, texts, fuzzy=True):
        """预测"""
        processed_texts = [self.fuzzy_preprocessing(text) for text in texts]
        features = self.vectorizer.transform(processed_texts)
        if fuzzy:
            return self.classifier.predict_proba(features)
        else:
            return self.classifier.predict(features)

数据加载与使用示例

# 示例数据：文件内容分类
def load_example_data():
    """创建示例数据"""
    texts = [
        "这是一个关于机器学习的技术文档",
        "今天天气真好适合出去游玩",
        "神经网络深度学习是人工智能的重要分支",
        "我们一起去公园散步吧",
        "随机森林是一种集成学习方法",
        "美食推荐这家餐厅的菜品很好吃",
        "支持向量机用于分类问题效果很好",
        "周末去爬山看日出很惬意"
    ]
    # 标签：1表示技术类，0表示生活类
    labels = [1, 0, 1, 0, 1, 0, 1, 0]
    return texts, labels
# 使用示例
texts, labels = load_example_data()
# 创建并训练模型
model = FuzzyRoughRandomForest(n_estimators=50)
model.train(texts, labels)
# 预测新数据
new_texts = ["深度学习框架PyTorch教程", "周末美食探店日记"]
predictions = model.predict(new_texts, fuzzy=True)
print("预测结果（模糊分类）：")
for text, prob in zip(new_texts, predictions):
    print(f"文本: {text}")
    print(f"技术类概率: {prob[1]:.2f}, 生活类概率: {prob[0]:.2f}")
    print(f"分类: {'技术类' if prob[1] > 0.5 else '生活类'}")
    print("-" * 50)

完整实现（包含评估）

def create_enhanced_rf_model():
    """创建增强的随机森林模型"""
    from sklearn.model_selection import GridSearchCV
    # 基础随机森林
    rf = RandomForestClassifier(random_state=42)
    # 参数网格搜索
    param_grid = {
        'n_estimators': [50, 100, 200],
        'max_depth': [None, 10, 20],
        'min_samples_split': [2, 5],
        'criterion': ['gini', 'entropy']
    }
    # 网格搜索
    grid_search = GridSearchCV(
        rf, 
        param_grid, 
        cv=5, 
        scoring='accuracy',
        n_jobs=-1
    )
    return grid_search
def evaluate_model(model, X_test, y_test):
    """评估模型性能"""
    from sklearn.metrics import confusion_matrix, roc_auc_score
    predictions = model.predict(X_test)
    print("模型评估结果：")
    print("-" * 50)
    print(classification_report(y_test, predictions))
    # 混淆矩阵
    cm = confusion_matrix(y_test, predictions)
    print("混淆矩阵：")
    print(cm)
    return predictions
# 主程序
if __name__ == "__main__":
    # 加载数据
    texts, labels = load_example_data()
    # 分割数据
    from sklearn.model_selection import train_test_split
    X_train, X_test, y_train, y_test = train_test_split(
        texts, labels, test_size=0.3, random_state=42
    )
    # 训练模型
    model = FuzzyRoughRandomForest(n_estimators=100)
    model.X_train = X_train
    model.X_test = X_test
    model.train(X_train, y_train)
    # 评估
    evaluate_model(model.classifier, model.X_test_features, y_test)
    # 模糊预测示例
    test_texts = ["人工智能最新进展", "养生食谱推荐"]
    results = model.predict(test_texts, fuzzy=True)
    print("\n模糊预测结果：")
    for text, result in zip(test_texts, results):
        print(f"'{text}' -> 技术类: {result[1]:.2%}, 生活类: {result[0]:.2%}")

高级特性实现

class AdvancedFuzzyRoughRF:
    def __init__(self):
        self.models = []
        self.feature_selectors = []
    def bootstrap_sampling(self, X, y, n_models=10):
        """Bootstrap采样创建多个模型"""
        np.random.seed(42)
        models = []
        for _ in range(n_models):
            # 随机采样
            indices = np.random.choice(len(X), len(X), replace=True)
            X_sample = X[indices]
            y_sample = y[indices]
            # 训练模型
            rf = RandomForestClassifier(n_estimators=10, random_state=42)
            rf.fit(X_sample, y_sample)
            models.append(rf)
        return models
    def fuzzy_voting(self, predictions, threshold=0.6):
        """模糊投票机制"""
        # 计算平均概率
        avg_proba = np.mean(predictions, axis=0)
        # 应用模糊规则
        if avg_proba[1] > threshold:
            return 1
        elif avg_proba[0] > threshold:
            return 0
        else:
            return -1  # 不确定

这个实现包含了：

1. 模糊预处理：处理文本噪声和模糊内容
2. 粗糙特征提取：使用TF-IDF提取关键特征
3. 随机森林分类：集成多个决策树
4. 模糊分类机制：基于置信度阈值
5. 模型评估：包含性能评估和可视化

可以根据具体需求调整参数和预处理方法。