脚本如何实现文件内容模糊粗糙梯度提升

本文目录导读：

1. Python 实现
2. 文件处理脚本
3. 使用示例

模糊粗糙梯度提升的方法。

Python 实现

基础实现

import numpy as np
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.feature_extraction.text import TfidfVectorizer
import re
import nltk
from nltk.corpus import stopwords
class FuzzyRoughGBM:
    def __init__(self, n_estimators=100, learning_rate=0.1, max_depth=3):
        self.n_estimators = n_estimators
        self.learning_rate = learning_rate
        self.max_depth = max_depth
        self.model = GradientBoostingClassifier(
            n_estimators=n_estimators,
            learning_rate=learning_rate,
            max_depth=max_depth
        )
        self.vectorizer = TfidfVectorizer(max_features=5000)
    def fuzzy_preprocessing(self, text):
        """模糊预处理：处理文本模糊特征"""
        # 小写化
        text = text.lower()
        # 去除特殊字符
        text = re.sub(r'[^\w\s]', ' ', text)
        # 模糊词替换（如拼写错误容忍）
        text = re.sub(r'(\w)\1{2,}', r'\1', text)  # 连续重复字符简化
        return text
    def rough_feature_extraction(self, texts):
        """粗糙特征提取"""
        # 基础统计特征
        features = []
        for text in texts:
            # 粗略特征
            words = text.split()
            length = len(words)
            # 模糊类别特征
            if length < 10:
                cat = 'short'
            elif length < 50:
                cat = 'medium'
            else:
                cat = 'long'
            features.append({
                'word_count': length,
                'char_count': len(text),
                'category': cat,
                'avg_word_len': np.mean([len(w) for w in words]) if words else 0
            })
        return features
    def gradient_boosting_with_fuzzy(self, X_train, y_train, X_test):
        """模糊梯度提升训练"""
        # 模糊文本处理
        X_train_fuzzy = [self.fuzzy_preprocessing(x) for x in X_train]
        X_test_fuzzy = [self.fuzzy_preprocessing(x) for x in X_test]
        # 粗糙特征提取
        train_rough = self.rough_feature_extraction(X_train_fuzzy)
        test_rough = self.rough_feature_extraction(X_test_fuzzy)
        # TF-IDF向量化
        X_train_tfidf = self.vectorizer.fit_transform(X_train_fuzzy)
        X_test_tfidf = self.vectorizer.transform(X_test_fuzzy)
        # 合并特征
        X_train_combined = self._combine_features(X_train_tfidf, train_rough)
        X_test_combined = self._combine_features(X_test_tfidf, test_rough)
        # 训练模型
        self.model.fit(X_train_combined, y_train)
        predictions = self.model.predict(X_test_combined)
        return predictions
    def _combine_features(self, tfidf_features, rough_features):
        """合并TF-IDF特征和粗糙特征"""
        rough_array = np.array([list(f.values())[:3] for f in rough_features])
        from scipy.sparse import hstack, csr_matrix
        return hstack([tfidf_features, csr_matrix(rough_array)])

增强版实现

import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score, classification_report
import warnings
warnings.filterwarnings('ignore')
class EnhancedFuzzyRoughGBM:
    def __init__(self, fuzzy_level=0.5, rough_granularity='medium'):
        self.fuzzy_level = fuzzy_level
        self.rough_granularity = rough_granularity
        self.models = []
        self.feature_importance = {}
    def fuzzy_tokenization(self, text, threshold=0.3):
        """模糊分词：对相似词进行聚合"""
        # 简单模糊匹配实现
        from difflib import SequenceMatcher
        words = text.split()
        fuzzy_groups = []
        used = set()
        for i, w1 in enumerate(words):
            if i in used:
                continue
            group = [w1]
            used.add(i)
            for j, w2 in enumerate(words):
                if j in used:
                    continue
                similarity = SequenceMatcher(None, w1, w2).ratio()
                if similarity > threshold:
                    group.append(w2)
                    used.add(j)
            fuzzy_groups.append(group)
        return ['_'.join(g) for g in fuzzy_groups]
    def rough_set_approximation(self, features, labels):
        """粗糙集近似：计算下近似和上近似"""
        # 简单的粗糙集实现
        lower_approx = {}
        upper_approx = {}
        for class_label in set(labels):
            class_indices = [i for i, l in enumerate(labels) if l == class_label]
            # 下近似：确定属于该类的样本
            lower_approx[class_label] = class_indices
            # 上近似：可能属于该类的样本
            similar_indices = []
            for idx in class_indices:
                # 使用欧氏距离找相似样本
                distances = []
                for other_idx in range(len(features)):
                    if other_idx != idx:
                        dist = np.linalg.norm(np.array(features[idx]) - np.array(features[other_idx]))
                        distances.append((other_idx, dist))
                distances.sort(key=lambda x: x[1])
                similar_indices.extend([d[0] for d in distances[:3]])
            upper_approx[class_label] = list(set(similar_indices + class_indices))
        return lower_approx, upper_approx
    def gradient_boosting_with_roughness(self, X, y, n_stages=5):
        """带有粗糙集的梯度提升"""
        # 数据预处理
        X_processed = []
        for text in X:
            fuzzy_words = self.fuzzy_tokenization(text)
            # 转换为数值特征
            word_lengths = [len(w) for w in fuzzy_words]
            X_processed.append([
                len(word_lengths),  # 词数
                np.mean(word_lengths) if word_lengths else 0,  # 平均词长
                np.std(word_lengths) if word_lengths else 0,  # 词长标准差
                len(text)  # 总字符数
            ])
        X_processed = np.array(X_processed)
        # 多阶段梯度提升
        predictions = np.zeros(len(y))
        residuals = y.copy()
        for stage in range(n_stages):
            # 计算近似集合
            lower, upper = self.rough_set_approximation(X_processed, residuals)
            # 构建弱学习器
            from sklearn.tree import DecisionTreeRegressor
            weak_learner = DecisionTreeRegressor(max_depth=2 + stage)
            # 使用粗糙集指导训练
            for class_label in set(residuals):
                if class_label in lower:
                    # 下近似样本权重更高
                    weights = np.array([2.0 if i in lower[class_label] 
                                      else 1.0 for i in range(len(y))])
                    weak_learner.fit(X_processed, residuals, sample_weight=weights)
            # 更新预测
            stage_pred = weak_learner.predict(X_processed)
            predictions += self.learning_rate * stage_pred
            # 更新残差
            residuals = y - predictions
            self.models.append(weak_learner)
        return np.round(predictions)  # 二分类返回0或1

文件处理脚本

import os
import glob
from pathlib import Path
class FileFuzzyRoughProcessor:
    def __init__(self, data_dir='./data'):
        self.data_dir = Path(data_dir)
        self.data_dir.mkdir(exist_ok=True)
    def load_files_as_text(self, pattern='*.txt'):
        """加载文件为文本数据"""
        files = glob.glob(str(self.data_dir / pattern))
        texts = []
        labels = []
        for file_path in files:
            with open(file_path, 'r', encoding='utf-8', errors='ignore') as f:
                texts.append(f.read())
                # 从文件名或目录获取标签
                label = 1 if 'positive' in file_path else 0
                labels.append(label)
        return texts, labels
    def process_file_batch(self, file_patterns=['*.txt', '*.md', '*.csv']):
        """批量处理文件"""
        all_texts = []
        all_labels = []
        for pattern in file_patterns:
            texts, labels = self.load_files_as_text(pattern)
            all_texts.extend(texts)
            all_labels.extend(labels)
        return all_texts, all_labels
    def apply_fuzzy_rough_gbm(self, test_size=0.2):
        """应用模糊粗糙梯度提升"""
        # 加载数据
        X_raw, y = self.process_file_batch()
        # 创建模型
        gbm = FuzzyRoughGBM(n_estimators=50, learning_rate=0.1, max_depth=3)
        # 分割数据
        from sklearn.model_selection import train_test_split
        X_train, X_test, y_train, y_test = train_test_split(
            X_raw, y, test_size=test_size, random_state=42
        )
        # 训练和预测
        predictions = gbm.gradient_boosting_with_fuzzy(X_train, y_train, X_test)
        # 评估
        accuracy = accuracy_score(y_test, predictions)
        print(f"Accuracy: {accuracy:.4f}")
        print("\nClassification Report:")
        print(classification_report(y_test, predictions))
        return predictions, y_test

使用示例

# 使用示例
if __name__ == "__main__":
    # 创建处理器
    processor = FileFuzzyRoughProcessor("./documents")
    # 处理文件
    predictions, true_labels = processor.apply_fuzzy_rough_gbm(test_size=0.2)
    # 或者使用增强版本
    enhanced_gbm = EnhancedFuzzyRoughGBM(fuzzy_level=0.6, rough_granularity='fine')
    texts, labels = processor.process_file_batch()
    results = enhanced_gbm.gradient_boosting_with_roughness(texts, labels)

这个实现包含了：

1. 模糊处理：文本模糊化、拼写容忍、模糊分词
2. 粗糙集近似：计算上下近似、处理不确定性
3. 梯度提升：多阶段训练、残差学习
4. 文件处理：批量文件加载、自动标签提取

根据需要可以调整参数和实现细节。