特征筛选LASSO回归封装好的代码、数据集和结果

 Gitee仓库地址：特征筛选LASSO回归封装好的代码、数据集和结果

README

LassoFeatureSelector_main

这个是主函数文件，在实例化LassoFeatureSelector类时，需要传入下面这些参数：

1. input_train_data_path：输入训练集的路径
2. input_test_data_path：输入测试集的路径
3. output_train_path：输出训练集的路径
4. output_test_path：输出测试集的路径
5. Upper_limit_alpha：正则化搜索范围上限
6. Lower_limit_alpha：正则化搜索范围下限
7. iterations：LASSO回归迭代次数
8. cv：选择最佳正则化系数的交叉验证次数

实例化后调用总运行函数即可：lasso_selector.run_all()

LassoFeatureSelector

这个是封装好的类，主要实现以下几个功能：

1. 计算特征筛选前后的方差膨胀因子，输出并导出
2. 绘制岭迹图并导出
3. 以MSE为损失函数进行LASSO回归
4. k折交叉验证进行最佳正则化系数的搜索
5. 导出特征筛选后的训练集和测试集
6. 无论输入的文件格式是xlsx文件还是csv文件，类都能读取

数据集

数据集来自网络入侵检测领域的经典数据集：NSLKDD

预处理好的数据集和导出的训练集测试集可以在百度网盘下载：

链接：https://pan.baidu.com/s/125SniuPOWFkrB4fONtIPQw?pwd=fgin 提取码：fgin

原始数据集见官网下载：

ISCX NSL-KDD dataset 2009

导出的文件

1. LASSO系数矩阵.xlsx
2. LASSO回归岭迹图.png
3. 原始训练集的方差膨胀因子.xlsx
4. LASSO回归后训练集的方差膨胀因子.xlsx
5. NSLKDD_train_LASSO.xlsx
6. NSLKDD_test_LASSO.xlsx

LASSO回归参数说明

若需要调整LASSO回归的参数，需要到LassoFeatureSelector文件的lasso_regression函数中进行修改

lassoreg = Lasso(alpha=alpha, max_iter=self.iterations, fit_intercept=True,precompute=False, copy_X=False,tol=0.0001, warm_start=False,positive=False,selection='cyclic')

alpha=alpha: 正则化强度，控制稀疏性

fit_intercept=True: 拟合截距

precompute=False: 是否预计算 Gram 矩阵，通常设置为 False

copy_X=True: 对输入数据进行复制

max_iter=self.iterations: 最大迭代次数，控制算法运行的最大迭代次数

tol=0.0001: 收敛的容忍度，指定算法收敛的阈值

warm_start=False: 如果为 True，则使用前一个调用的解决方案以适应的权重

positive=False: 如果为 True，则要求系数为正

selection='cyclic': 指定系数更新的策略。'cyclic' 表示按循环顺序逐个更新系数

封装好的类

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from matplotlib import rcParams
from sklearn.exceptions import ConvergenceWarning
from sklearn.linear_model import Lasso, LassoCV
from sklearn.metrics import mean_squared_error
from statsmodels.stats.outliers_influence import variance_inflation_factor
import warnings
import ospd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)# 设置matplotlib绘图的中文字体
rcParams['font.sans-serif'] = ['Microsoft YaHei']
rcParams['axes.unicode_minus'] = False# 忽略特定类型的警告
warnings.filterwarnings("ignore", category=ConvergenceWarning)class LassoFeatureSelector:def __init__(self, input_train_data_path, input_test_data_path, output_train_path, output_test_path, upper_limit_alpha, lower_limit_alpha, iterations, cv):  # 初始化函数self.input_train_data_path = input_train_data_pathself.input_test_data_path = input_test_data_pathself.output_train_path = output_train_pathself.output_test_path = output_test_pathself.upper_limit_alpha = upper_limit_alphaself.lower_limit_alpha = lower_limit_alphaself.iterations = iterationsself.cv = cvdef load_data(self):  # 读取文件函数if os.path.splitext(self.input_train_data_path)[1] == '.xlsx':self.input_train_data = pd.read_excel(self.input_train_data_path)elif os.path.splitext(self.input_train_data_path)[1] == '.csv':self.input_train_data = pd.read_csv(self.input_train_data_path)if os.path.splitext(self.input_test_data_path)[1] == '.xlsx':self.input_test_data = pd.read_excel(self.input_test_data_path)elif os.path.splitext(self.input_test_data_path)[1] == '.csv':self.input_test_data = pd.read_csv(self.input_test_data_path)def lasso_regression(self, train, test, alpha):  # LASSO回归函数lassoreg = Lasso(alpha=alpha, max_iter=self.iterations, fit_intercept=True,precompute=False,copy_X=False,tol=0.0001, warm_start=False,positive=False,selection='cyclic')lassoreg.fit(train.iloc[:, 0:-1], train.iloc[:, -1])feature_count = np.sum(lassoreg.coef_ != 0)y_pred = lassoreg.predict(test.iloc[:, 0:-1])mse = mean_squared_error(test.iloc[:, -1], y_pred)ret = [alpha, mse]ret.append(feature_count)ret.extend(lassoreg.coef_)return retdef matrix_lasso(self):  # LASSO系数矩阵函数self.alpha_lasso = np.linspace(self.lower_limit_alpha, self.upper_limit_alpha, self.iterations)col = ["alpha", "mse", "feature_count"] + list(self.input_train_data.iloc[:, 0:-1])ind = ["alpha_%.4g" % self.alpha_lasso[i] for i in range(0, len(self.alpha_lasso))]self.coef_matrix_lasso = pd.DataFrame(index=ind, columns=col)input_train_1, input_train_2 = train_test_split(self.input_train_data, test_size=0.2, random_state=42)  # 在输入的训练集里面分割进行LASSO回归for i in range(len(self.alpha_lasso)):self.coef_matrix_lasso.iloc[i] = self.lasso_regression(input_train_1, input_train_2, self.alpha_lasso[i])self.coef_matrix_lasso.to_excel(r'LASSO系数矩阵.xlsx', index=True)def plot_lasso_path(self):  # 绘制岭迹图函数plt.figure(figsize=(14, 6.8))for i in np.arange(len(list(self.input_train_data.iloc[:, 0:-1]))):plt.plot(self.coef_matrix_lasso["alpha"],self.coef_matrix_lasso[list(self.input_train_data.iloc[:, 0:-1])[i]],color=plt.cm.Set1(i / len(list(self.input_train_data.iloc[:, 0:-1]))),label=list(self.input_train_data.iloc[:, 0:-1])[i])plt.legend(loc="upper right", ncol=2, prop={'size': 7})plt.xlabel("正则化系数", fontsize=14)plt.ylabel("回归系数", fontsize=14)plt.savefig(r'LASSO回归岭迹图', dpi=600)plt.show()def select_best_alpha(self):  # 选择最佳正则化系数函数alpha_choose = np.linspace(self.lower_limit_alpha, self.upper_limit_alpha, self.iterations)lasso_cv = LassoCV(alphas=alpha_choose, cv=self.cv, max_iter=self.iterations)lasso_cv.fit(self.input_train_data.iloc[:, 0:-1], self.input_train_data.iloc[:, -1])self.lasso_best_alpha = lasso_cv.alpha_print(f"选择的最佳正则化系数: {self.lasso_best_alpha}")def calculate_vif(self, data):  # 计算方差膨胀因子函数vif = pd.DataFrame()vif['特征'] = data.columnsvif['方差膨胀因子'] = [variance_inflation_factor(data.values, i) for i in range(data.shape[1])]return vifdef fit_lasso_model(self):  # 筛选特征函数self.lasso_model = Lasso(alpha=self.lasso_best_alpha, fit_intercept=True,max_iter=self.iterations, random_state=42, selection='cyclic')self.lasso_model.fit(self.input_train_data.iloc[:, 0:-1], self.input_train_data.iloc[:, -1])self.selected_features = self.input_train_data.iloc[:, 0:-1].columns[self.lasso_model.coef_ != 0]def save_vif(self):  # 输出并保存方差膨胀因子函数vif_before = self.calculate_vif(self.input_train_data.iloc[:, 0:-1])print("原始数据的方差膨胀因子:\n", vif_before)input_data_selected = self.input_train_data.iloc[:, 0:-1][self.selected_features]vif_after = self.calculate_vif(input_data_selected)print("筛选特征后的方差膨胀因子:\n", vif_after)vif_before.to_excel(r'原始训练集的方差膨胀因子.xlsx', index=False)vif_after.to_excel(r'LASSO回归后训练集的方差膨胀因子.xlsx', index=False)def save_selected_data(self):  # 导出LASSO筛选特征后的训练集和测试集selected_data_train = self.input_train_data[list(self.selected_features) + [self.input_train_data.columns[-1]]]selected_data_test = self.input_test_data[list(self.selected_features) + [self.input_test_data.columns[-1]]]if os.path.splitext(self.output_train_path)[1] == '.xlsx':selected_data_train.to_excel(self.output_train_path, index=False)elif os.path.splitext(self.output_train_path)[1] == '.csv':selected_data_train.to_csv(self.output_train_path, index=False)if os.path.splitext(self.output_test_path)[1] == '.xlsx':selected_data_test.to_excel(self.output_test_path, index=False)elif os.path.splitext(self.output_test_path)[1] == '.csv':selected_data_test.to_csv(self.output_test_path, index=False)def calculate_avg_vif(self):  # 计算平均方差膨胀因子函数vif_before = self.calculate_vif(self.input_train_data.iloc[:, 0:-1])avg_vif_before = vif_before['方差膨胀因子'].mean()input_data_selected = self.input_train_data.iloc[:, 0:-1][self.selected_features]vif_after = self.calculate_vif(input_data_selected)avg_vif_after = vif_after['方差膨胀因子'].mean()print(f"特征筛选前的平均方差膨胀因子: {avg_vif_before}")print(f"特征筛选后的平均方差膨胀因子: {avg_vif_after}")def run_all(self):self.load_data()self.matrix_lasso()self.plot_lasso_path()self.select_best_alpha()self.fit_lasso_model()self.save_vif()self.save_selected_data()self.calculate_avg_vif()

 调用的子函数

from LassoFeatureSelector import *'''初始化'''
input_train_data_path = r'D:\Gitee\NSLKDD_train.xlsx'  # 输入训练集的路径
input_test_data_path = r'D:\Gitee\NSLKDD_test.xlsx'  # 输入测试集的路径
output_train_path = r'D:\Gitee\NSLKDD_train_LASSO.xlsx'  # 输出训练集的路径
output_test_path = r'D:\Gitee\NSLKDD_test_LASSO.xlsx'  # 输出测试集的路径
Upper_limit_alpha = 0.001  # 正则化搜索范围上限
Lower_limit_alpha = 0.0012  # 正则化搜索范围下限
iterations = 2000  # LASSO回归迭代次数
cv = 10  # 选择最佳正则化系数的交叉验证次数'''调用函数'''
lasso_selector = LassoFeatureSelector(input_train_data_path, input_test_data_path, output_train_path, output_test_path, Upper_limit_alpha, Lower_limit_alpha, iterations, cv)
lasso_selector.run_all()