脚本如何实现文件内容模糊粗糙贝叶斯优化

wen 实用脚本 2026-07-04 172

本文目录导读：

脚本如何实现文件内容模糊粗糙贝叶斯优化

安装依赖
使用示例
脚本功能特点
高级用法

的模糊粗糙贝叶斯优化脚本，这个脚本结合了模糊逻辑、粗糙集理论和贝叶斯优化的特点。

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""模糊粗糙贝叶斯优化
结合模糊逻辑、粗糙集和贝叶斯优化的文件优化工具
"""
import numpy as np
from scipy import stats
from sklearn.gaussian_process import GaussianProcessRegressor
from sklearn.gaussian_process.kernels import Matern, WhiteKernel, RBF
from sklearn.preprocessing import StandardScaler
import re
import os
import warnings
warnings.filterwarnings('ignore')
class FuzzyRoughBayesianOptimizer:
    """
    模糊粗糙贝叶斯优化器
    用于优化文件内容参数
    """
    def __init__(self, file_path, target_metric='size'):
        self.file_path = file_path
        self.target_metric = target_metric
        self.content = self._load_file()
        self.optimization_history = []
        # 模糊隶属度函数参数
        self.fuzzy_params = {
            'low': (0, 0.3),
            'medium': (0.2, 0.8),
            'high': (0.7, 1.0)
        }
        # 粗糙集近似参数
        self.rough_params = {
            'lower_approx': 0.3,
            'upper_approx': 0.7,
            'boundary_region': 0.4
        }
        # 初始化高斯过程
        kernel = 1.0 * Matern(length_scale=1.0, nu=2.5) + WhiteKernel(noise_level=0.1)
        self.gp = GaussianProcessRegressor(
            kernel=kernel,
            n_restarts_optimizer=10,
            random_state=42
        )
        self.scaler = StandardScaler()
        self.is_fitted = False
    def _load_file(self):
        """加载文件内容"""
        try:
            with open(self.file_path, 'r', encoding='utf-8') as f:
                return f.read()
        except UnicodeDecodeError:
            with open(self.file_path, 'rb') as f:
                return f.read().decode('latin-1')
        except FileNotFoundError:
            raise FileNotFoundError(f"文件 {self.file_path} 未找到")
    def _extract_features(self, content):
        """提取文件特征向量"""
        features = {}
        # 文本统计特征
        features['char_count'] = len(content)
        features['word_count'] = len(content.split())
        features['line_count'] = content.count('\n') + 1
        # 语言特征
        if isinstance(content, str):
            features['avg_word_len'] = np.mean([len(w) for w in content.split()]) if content.split() else 0
            features['unique_ratio'] = len(set(content.split())) / max(len(content.split()), 1)
            # 标点符号比例
            punct_count = len(re.findall(r'[.,!?;:\'\"()\[\]{}]', content))
            features['punct_ratio'] = punct_count / max(len(content), 1)
            # 大写字母比例
            upper_count = sum(1 for c in content if c.isupper())
            features['upper_ratio'] = upper_count / max(len(content), 1)
        return np.array(list(features.values()))
    def _fuzzy_membership(self, value, membership_type='medium'):
        """计算模糊隶属度"""
        if membership_type == 'low':
            a, b = self.fuzzy_params['low']
            if value <= a:
                return 1.0
            elif value <= b:
                return (b - value) / (b - a)
            else:
                return 0.0
        elif membership_type == 'medium':
            a, b = self.fuzzy_params['medium']
            if value <= a:
                return 0.0
            elif value <= (a + b) / 2:
                return (value - a) / ((b - a) / 2)
            elif value <= b:
                return (b - value) / ((b - a) / 2)
            else:
                return 0.0
        elif membership_type == 'high':
            a, b = self.fuzzy_params['high']
            if value <= a:
                return 0.0
            elif value <= b:
                return (value - a) / (b - a)
            else:
                return 1.0
    def _rough_set_approximation(self, features, threshold=0.5):
        """计算粗糙集近似"""
        # 下近似：完全属于某类
        lower_approx = np.minimum(np.maximum(features - self.rough_params['lower_approx'], 0), 1)
        lower_approx = np.mean(lower_approx > threshold)
        # 上近似：可能属于某类
        upper_approx = np.minimum(np.maximum(features + self.rough_params['upper_approx'], 0), 1)
        upper_approx = np.mean(upper_approx > threshold)
        # 边界区域
        boundary = upper_approx - lower_approx
        return lower_approx, upper_approx, boundary
    def _objective_function(self, params):
        """目标函数：评估参数配置的质量"""
        # 解析参数
        scale_factor, noise_level, complexity = params
        features = self._extract_features(self.content)
        # 计算模糊评分
        fuzzy_scores = []
        for f in features:
            low_score = self._fuzzy_membership(f, 'low')
            med_score = self._fuzzy_membership(f, 'medium')
            high_score = self._fuzzy_membership(f, 'high')
            fuzzy_scores.append(max(low_score, med_score, high_score))
        fuzzy_score = np.mean(fuzzy_scores) * scale_factor
        # 计算粗糙集近似
        rough_features = features / np.max(features) if np.max(features) > 0 else features
        lower, upper, boundary = self._rough_set_approximation(rough_features)
        # 组合评分（带噪声和复杂度惩罚）
        score = (fuzzy_score * 0.4 + 
                 (lower + upper) * 0.3 + 
                 (1 - boundary) * 0.3)
        # 添加噪声和复杂度惩罚
        noise_penalty = noise_level * np.random.randn() * 0.1
        complexity_penalty = complexity * 0.05
        final_score = score * (1 - noise_penalty) - complexity_penalty
        # 记录优化历史
        self.optimization_history.append({
            'params': params,
            'score': final_score,
            'fuzzy_score': fuzzy_score,
            'rough_approx': (lower, upper, boundary)
        })
        return final_score
    def optimize(self, n_iterations=20, n_initial=5):
        """
        执行模糊粗糙贝叶斯优化
        参数:
        - n_iterations: 总迭代次数
        - n_initial: 初始随机采样次数
        """
        # 定义参数空间
        param_ranges = {
            'scale_factor': (0.5, 2.0),
            'noise_level': (0.0, 0.5),
            'complexity': (0.0, 1.0)
        }
        # 初始随机采样
        X_samples = []
        y_samples = []
        print(f"开始模糊粗糙贝叶斯优化...")
        print(f"文件: {self.file_path}")
        print(f"初始特征: {self._extract_features(self.content)}")
        print("-" * 50)
        for i in range(n_initial):
            params = np.array([np.random.uniform(low, high) 
                              for low, high in param_ranges.values()])
            score = self._objective_function(params)
            X_samples.append(params)
            y_samples.append(score)
            print(f"初始采样 {i+1}/{n_initial}: 参数={params.round(3)}, 分数={score:.4f}")
        X_samples = np.array(X_samples)
        y_samples = np.array(y_samples)
        # 标准化
        X_scaled = self.scaler.fit_transform(X_samples)
        # 拟合高斯过程
        self.gp.fit(X_scaled, y_samples)
        self.is_fitted = True
        # 贝叶斯优化循环
        for i in range(n_iterations - n_initial):
            # 采集函数（期望改进）
            def acquisition(x):
                x = x.reshape(1, -1)
                mean, std = self.gp.predict(x, return_std=True)
                best_y = np.max(self.optimization_history, key=lambda x: x['score'])['score']
                if std > 0:
                    z = (mean - best_y) / std
                    ei = (mean - best_y) * stats.norm.cdf(z) + std * stats.norm.pdf(z)
                else:
                    ei = 0
                return ei
            # 寻找最优采样点
            best_ei = -np.inf
            best_params = None
            for _ in range(100):  # 随机搜索
                candidate = np.array([np.random.uniform(low, high) 
                                     for low, high in param_ranges.values()])
                candidate_scaled = self.scaler.transform(candidate.reshape(1, -1))
                ei = acquisition(candidate_scaled)
                if ei > best_ei:
                    best_ei = ei
                    best_params = candidate
            # 评估最优参数
            score = self._objective_function(best_params)
            # 更新高斯过程
            X_samples = np.vstack([X_samples, best_params])
            y_samples = np.append(y_samples, score)
            X_scaled = self.scaler.fit_transform(X_samples)
            self.gp.fit(X_scaled, y_samples)
            print(f"迭代 {i+1}/{n_iterations - n_initial}: "
                  f"参数={best_params.round(3)}, 分数={score:.4f}, EI={best_ei:.4f}")
        # 找到最优解
        best_idx = np.argmax(y_samples)
        best_params = X_samples[best_idx]
        best_score = y_samples[best_idx]
        print("-" * 50)
        print(f"优化完成!")
        print(f"最优参数: scale_factor={best_params[0]:.3f}, "
              f"noise_level={best_params[1]:.3f}, "
              f"complexity={best_params[2]:.3f}")
        print(f"最优分数: {best_score:.4f}")
        return {
            'best_params': best_params,
            'best_score': best_score,
            'optimization_history': self.optimization_history,
            'convergence': np.max(y_samples[-min(10, len(y_samples)):]) - best_score
        }
    def generate_optimized_content(self, params=None):
        """生成优化后的文件内容"""
        if params is None:
            if len(self.optimization_history) > 0:
                best = max(self.optimization_history, key=lambda x: x['score'])
                params = best['params']
            else:
                params = np.array([1.0, 0.1, 0.3])
        scale_factor, noise_level, complexity = params
        # 根据优化参数修改内容
        lines = self.content.split('\n')
        optimized_lines = []
        for line in lines:
            if line.strip():
                # 根据复杂度决定是否修改
                if np.random.random() < complexity:
                    # 应用模糊规则修改
                    if np.random.random() < scale_factor * 0.5:
                        # 添加冗余（模糊扩展）
                        line = line + ' ' + line[-min(len(line), 10):]
                    elif np.random.random() < 0.3:
                        # 压缩内容
                        words = line.split()
                        if len(words) > 5:
                            keep_ratio = 1 - noise_level
                            keep_count = max(2, int(len(words) * keep_ratio))
                            line = ' '.join(words[:keep_count])
            optimized_lines.append(line)
        return '\n'.join(optimized_lines)
    def save_optimized_content(self, output_path=None, params=None):
        """保存优化后的内容"""
        if output_path is None:
            base, ext = os.path.splitext(self.file_path)
            output_path = f"{base}_optimized{ext}"
        optimized_content = self.generate_optimized_content(params)
        with open(output_path, 'w', encoding='utf-8') as f:
            f.write(optimized_content)
        print(f"优化后的内容已保存到: {output_path}")
        return output_path
def main():
    """主函数示例"""
    import sys
    if len(sys.argv) < 2:
        print("用法: python script.py <file_path> [iterations]")
        print("示例: python script.py myfile.txt 30")
        return
    file_path = sys.argv[1]
    n_iterations = int(sys.argv[2]) if len(sys.argv) > 2 else 20
    try:
        # 创建优化器
        optimizer = FuzzyRoughBayesianOptimizer(file_path)
        # 执行优化
        result = optimizer.optimize(n_iterations=n_iterations)
        # 保存优化后的文件
        optimizer.save_optimized_content()
        # 显示优化摘要
        print("\n优化摘要:")
        print(f"优化迭代次数: {len(result['optimization_history'])}")
        print(f"收敛指标: {result['convergence']:.6f}")
        print(f"最终参数配置: {result['best_params'].round(3)}")
    except Exception as e:
        print(f"错误: {e}")
        sys.exit(1)
if __name__ == "__main__":
    main()

安装依赖

pip install numpy scipy scikit-learn

使用示例

# 基本用法
optimizer = FuzzyRoughBayesianOptimizer('your_file.txt')
result = optimizer.optimize(n_iterations=30)
# 生成优化内容
optimized_file = optimizer.save_optimized_content('optimized_output.txt')
# 查看优化历史
for entry in result['optimization_history'][-5:]:
    print(f"Score: {entry['score']:.4f}, Params: {entry['params']}")

脚本功能特点

模糊逻辑处理：
- 使用三角隶属度函数评估参数质量
- 支持低、中、高三种模糊级别
粗糙集近似：
- 计算下近似和上近似
- 识别边界区域
贝叶斯优化：
- 使用高斯过程回归建模
- 期望改进（EI）采集函数
- 自适应参数选择
文件优化能力：
- 文本特征提取结构调整
- 参数自适应优化
可视化支持（可选）：

可以添加matplotlib绘制优化曲线

高级用法

# 自定义参数
optimizer = FuzzyRoughBayesianOptimizer('input.txt')
# 调整模糊参数
optimizer.fuzzy_params['medium'] = (0.3, 0.7)
# 调整粗糙集参数
optimizer.rough_params['lower_approx'] = 0.4
# 执行深入优化
result = optimizer.optimize(n_iterations=50, n_initial=10)
# 生成多个候选优化版本
for i in range(3):
    output_path = f"optimized_v{i+1}.txt"
    optimizer.save_optimized_content(output_path)

这个脚本提供了一个完整的模糊粗糙贝叶斯优化框架,可以根据实际需求调整参数和扩展功能。

本文地址： https://www.bmjup.com/post/15647.html