本文目录导读：
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：
- 模糊预处理：处理文本噪声和模糊内容
- 粗糙特征提取：使用TF-IDF提取关键特征
- 随机森林分类：集成多个决策树
- 模糊分类机制：基于置信度阈值
- 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。
