#-*- coding: utf-8 -*-
#逻辑回归 自动建模
import numpy as np
import pandas as pd
from sklearn.linear_model import LogisticRegression as LR
from sklearn.linear_model import RandomizedLogisticRegression as RLR
#参数初始化
filename = '../data/bankloan.xls'
data = pd.read_excel(filename)
x = data.iloc[:,:8].as_matrix()#使用pandas读取文件 就可以不用管label column标签
y = data.iloc[:,8].as_matrix()
rlr = RLR() #建立随机逻辑回归模型，进行特征选择和变量筛选
rlr.fit(x, y) #训练模型
egeList=rlr.get_support() #获取筛选后的特征
egeList=np.append(egeList,False)#往numpy数组中 添加一个False元素 使用np.append(array,ele)方法
print("rlr.get_support():")
print(egeList)
print(u'随机逻辑回归模型特征选择结束！！！')
print(u'有效特征为：%s' % ','.join(data.columns[egeList]))
x = data[data.columns[egeList]].as_matrix() #筛选好特征值
lr = LR() #建立逻辑回归模型
lr.fit(x, y) #用筛选后的特征进行训练
print(u'逻辑回归训练模型结束！！！')
print(u'模型的平均正确率：%s' % lr.score(x, y)) #给出模型的平均正确率，本例为81.4%
D:\Download\python3\python3.exe "D:\Program Files\JetBrains\PyCharm 2017.3.3\helpers\pydev\pydev_run_in_console.py" 56033 56034 "E:/A正在学习/python data dig/chapter5/demo/code/5-1_logistic_regression.py"
Running E:/A正在学习/python data dig/chapter5/demo/code/5-1_logistic_regression.py
import sys; print('Python %s on %s' % (sys.version, sys.platform))
sys.path.extend(['E:\\A正在学习\\python data dig', 'E:/A正在学习/python data dig/chapter5/demo/code'])
C:\Users\Snow\AppData\Roaming\Python\Python35\site-packages\sklearn\utils\deprecation.py:58: DeprecationWarning: Class RandomizedLogisticRegression is deprecated; The class RandomizedLogisticRegression is deprecated in 0.19 and will be removed in 0.21.
warnings.warn(msg, category=DeprecationWarning)
rlr.get_support():
[False False True True False True True False False]
随机逻辑回归模型特征选择结束！！！
有效特征为：工龄,地址,负债率,信用卡负债
逻辑回归训练模型结束！！！
模型的平均正确率：0.8142857142857143
PyDev console: starting.
Python 3.5.4 (v3.5.4:3f56838, Aug 8 2017, 02:17:05) [MSC v.1900 64 bit (AMD64)] on win32
