python邏輯回歸 自動建模

#-*- coding: utf-8 -*-
#邏輯回歸 自動建模
import numpy as np
import pandas as pd
from sklearn.linear_model import LogisticRegression as LR
from sklearn.linear_model import RandomizedLogisticRegression as RLR
#參數初始化
filename = '../data/bankloan.xls'
data = pd.read_excel(filename)
x = data.iloc[:,:8].as_matrix()#使用pandas讀取文件  就可以不用管label column標簽
y = data.iloc[:,8].as_matrix()

rlr = RLR() #建立隨機邏輯回歸模型，進行特征選擇和變量篩選
rlr.fit(x, y) #訓練模型
egeList=rlr.get_support() #獲取篩選后的特征
egeList=np.append(egeList,False)#往numpy數組中 添加一個False元素  使用np.append(array,ele)方法
print("rlr.get_support():")
print(egeList)
print(u'隨機邏輯回歸模型特征選擇結束！！！')
print(u'有效特征為：%s' % ','.join(data.columns[egeList]))
x = data[data.columns[egeList]].as_matrix() #篩選好特征值

lr = LR() #建立邏輯回歸模型
lr.fit(x, y) #用篩選后的特征進行訓練
print(u'邏輯回歸訓練模型結束！！！')
print(u'模型的平均正確率：%s' % lr.score(x, y)) #給出模型的平均正確率，本例為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正在學習/python data dig/chapter5/demo/code/5-1_logistic_regression.py"
Running E:/A正在學習/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正在學習\\python data dig', 'E:/A正在學習/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]
隨機邏輯回歸模型特征選擇結束！！！
有效特征為：工齡,地址,負債率,信用卡負債
邏輯回歸訓練模型結束！！！
模型的平均正確率：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