【Python-ML】SKlearn库L1正则化特征选择

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我是靠谱客的博主热心高山，最近开发中收集的这篇文章主要介绍【Python-ML】SKlearn库L1正则化特征选择，觉得挺不错的，现在分享给大家，希望可以做个参考。

概述

# -*- coding: utf-8 -*-
'''
Created on 2018年1月17日
@author: Jason.F
@summary: Scikit-Learn库逻辑斯蒂L1正则化-特征选择
'''
import pandas as pd
import numpy as np
from sklearn.cross_validation import train_test_split
from sklearn.preprocessing import MinMaxScaler
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LogisticRegression
import matplotlib.pyplot as plt
#导入数据
df_wine = pd.read_csv('https://archive.ics.uci.edu/ml/machine-learning-databases/wine/wine.data',header=None)
df_wine.columns=['Class label','Alcohol','Malic acid','Ash','Alcalinity of ash','Magnesium','Total phenols','Flavanoids','Nonflavanoid phenols','Proanthocyanins','Color intensity','Hue','OD280/OD315 of diluted wines','Proline']
print ('class labels:',np.unique(df_wine['Class label']))
#print (df_wine.head(5))
#分割训练集合测试集
X,y=df_wine.iloc[:,1:].values,df_wine.iloc[:,0].values
X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.3,random_state=0)
#特征值缩放
#归一化
mms=MinMaxScaler()
X_train_norm=mms.fit_transform(X_train)
X_test_norm=mms.fit_transform(X_test)
#标准化
stdsc=StandardScaler()
X_train_std=stdsc.fit_transform(X_train)
X_test_std=stdsc.fit_transform(X_test)
#L1正则化的逻辑斯蒂模型
lr=LogisticRegression(penalty='l1',C=0.1)#penalty='l2'
lr.fit(X_train_std,y_train)
print ('Training accuracy:',lr.score(X_train_std, y_train))
print ('Test accuracy:',lr.score(X_test_std, y_test))#比较训练集和测试集，观察是否出现过拟合
print (lr.intercept_)#查看截距，三个类别
print (lr.coef_)#查看权重系数，L1有稀疏化效果做特征选择
#正则化效果，减少约束参数值C，增加惩罚力度，各特征权重系数趋近于0
fig=plt.figure()
ax=plt.subplot(111)
colors=['blue','green','red','cyan','magenta','yellow','black','pink','lightgreen','lightblue','gray','indigo','orange']
weights,params=[],[]
for c in np.arange(-4,6,dtype=float):
lr=LogisticRegression(penalty='l1',C=10**c,random_state=0)
lr.fit(X_train_std,y_train)
weights.append(lr.coef_[0])#三个类别，选择第一个类别来观察
params.append(10**c)
weights=np.array(weights)
for column,color in zip(range(weights.shape[1]),colors):
plt.plot(params,weights[:,column],label=df_wine.columns[column+1],color=color)
plt.axhline(0,color='black',linestyle='--',linewidth=3)
plt.xlim([10**(-5),10**5])
plt.ylabel('weight coefficient')
plt.xlabel('C')
plt.xscale('log')
plt.legend(loc='upper left')
ax.legend(loc='upper center',bbox_to_anchor=(1.38,1.03),ncol=1,fancybox=True)
plt.show()

结果：

('class labels:', array([1, 2, 3], dtype=int64))
('Training accuracy:', 0.9838709677419355)
('Test accuracy:', 0.98148148148148151)
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