pandas逻辑回归建模
基于样本数据（年龄、教育、工龄、地址、收入、负债率、信用卡负债、其他负债、违约），挖掘显著影响违约的因子。步骤如下：
import pandas as pd
In [2]:
filename = ‘/Users/frontc/book/ppdam/bankloan.xls’
In [3]:
data = pd.read_excel(filename)
In [29]:
data
Out[29]:
| 年龄 | 教育 | 工龄 | 地址 | 收入 | 负债率 | 信用卡负债 | 其他负债 | 违约 | |
|---|---|---|---|---|---|---|---|---|---|
| 0 | 41 | 3 | 17 | 12 | 176 | 9.3 | 11.359392 | 5.008608 | 1 |
| 1 | 27 | 1 | 10 | 6 | 31 | 17.3 | 1.362202 | 4.000798 | 0 |
| 2 | 40 | 1 | 15 | 14 | 55 | 5.5 | 0.856075 | 2.168925 | 0 |
| 3 | 41 | 1 | 15 | 14 | 120 | 2.9 | 2.658720 | 0.821280 | 0 |
| 4 | 24 | 2 | 2 | 0 | 28 | 17.3 | 1.787436 | 3.056564 | 1 |
| 5 | 41 | 2 | 5 | 5 | 25 | 10.2 | 0.392700 | 2.157300 | 0 |
| 6 | 39 | 1 | 20 | 9 | 67 | 30.6 | 3.833874 | 16.668126 | 0 |
| 7 | 43 | 1 | 12 | 11 | 38 | 3.6 | 0.128592 | 1.239408 | 0 |
| 8 | 24 | 1 | 3 | 4 | 19 | 24.4 | 1.358348 | 3.277652 | 1 |
| 9 | 36 | 1 | 0 | 13 | 25 | 19.7 | 2.777700 | 2.147300 | 0 |
| 10 | 27 | 1 | 0 | 1 | 16 | 1.7 | 0.182512 | 0.089488 | 0 |
| 11 | 25 | 1 | 4 | 0 | 23 | 5.2 | 0.252356 | 0.943644 | 0 |
| 12 | 52 | 1 | 24 | 14 | 64 | 10.0 | 3.929600 | 2.470400 | 0 |
| 13 | 37 | 1 | 6 | 9 | 29 | 16.3 | 1.715901 | 3.011099 | 0 |
| 14 | 48 | 1 | 22 | 15 | 100 | 9.1 | 3.703700 | 5.396300 | 0 |
| 15 | 36 | 2 | 9 | 6 | 49 | 8.6 | 0.817516 | 3.396484 | 1 |
| 16 | 36 | 2 | 13 | 6 | 41 | 16.4 | 2.918216 | 3.805784 | 1 |
| 17 | 43 | 1 | 23 | 19 | 72 | 7.6 | 1.181952 | 4.290048 | 0 |
| 18 | 39 | 1 | 6 | 9 | 61 | 5.7 | 0.563274 | 2.913726 | 0 |
| 19 | 41 | 3 | 0 | 21 | 26 | 1.7 | 0.099008 | 0.342992 | 0 |
| 20 | 39 | 1 | 22 | 3 | 52 | 3.2 | 1.154816 | 0.509184 | 0 |
| 21 | 47 | 1 | 17 | 21 | 43 | 5.6 | 0.587552 | 1.820448 | 0 |
| 22 | 28 | 1 | 3 | 6 | 26 | 10.0 | 0.431600 | 2.168400 | 0 |
| 23 | 29 | 1 | 8 | 6 | 27 | 9.8 | 0.402192 | 2.243808 | 0 |
| 24 | 21 | 2 | 1 | 2 | 16 | 18.0 | 0.241920 | 2.638080 | 1 |
| 25 | 25 | 4 | 0 | 2 | 32 | 17.6 | 2.140160 | 3.491840 | 0 |
| 26 | 45 | 2 | 9 | 26 | 69 | 6.7 | 0.707319 | 3.915681 | 0 |
| 27 | 43 | 1 | 25 | 21 | 64 | 16.7 | 0.951232 | 9.736768 | 0 |
| 28 | 33 | 2 | 12 | 8 | 58 | 18.4 | 3.084208 | 7.587792 | 0 |
| 29 | 26 | 3 | 2 | 1 | 37 | 14.2 | 0.204906 | 5.049094 | 0 |
| … | … | … | … | … | … | … | … | … | … |
| 670 | 23 | 2 | 3 | 4 | 24 | 6.3 | 0.551880 | 0.960120 | 0 |
| 671 | 27 | 1 | 0 | 7 | 18 | 12.8 | 0.582912 | 1.721088 | 0 |
| 672 | 34 | 1 | 6 | 1 | 20 | 1.2 | 0.042480 | 0.197520 | 0 |
| 673 | 35 | 1 | 0 | 5 | 34 | 11.1 | 1.369962 | 2.404038 | 1 |
| 674 | 24 | 2 | 4 | 4 | 20 | 3.7 | 0.324120 | 0.415880 | 0 |
| 675 | 48 | 1 | 30 | 8 | 101 | 6.4 | 1.874560 | 4.589440 | 0 |
| 676 | 26 | 2 | 8 | 1 | 40 | 11.8 | 0.443680 | 4.276320 | 0 |
| 677 | 40 | 1 | 6 | 9 | 36 | 2.1 | 0.390852 | 0.365148 | 1 |
| 678 | 34 | 1 | 9 | 8 | 48 | 9.3 | 0.419616 | 4.044384 | 0 |
| 679 | 35 | 1 | 17 | 4 | 42 | 3.0 | 0.093240 | 1.166760 | 0 |
| 680 | 30 | 1 | 7 | 2 | 33 | 25.4 | 1.165098 | 7.216902 | 1 |
| 681 | 20 | 1 | 4 | 0 | 14 | 9.7 | 0.200984 | 1.157016 | 1 |
| 682 | 36 | 4 | 1 | 17 | 30 | 11.5 | 0.324300 | 3.125700 | 0 |
| 683 | 21 | 1 | 1 | 1 | 16 | 6.3 | 0.141120 | 0.866880 | 0 |
| 684 | 34 | 1 | 18 | 10 | 53 | 10.5 | 0.840315 | 4.724685 | 0 |
| 685 | 35 | 1 | 7 | 5 | 39 | 16.1 | 1.701609 | 4.577391 | 1 |
| 686 | 35 | 3 | 1 | 4 | 20 | 7.9 | 0.853200 | 0.726800 | 0 |
| 687 | 34 | 1 | 10 | 1 | 33 | 10.3 | 2.501664 | 0.897336 | 1 |
| 688 | 33 | 1 | 12 | 12 | 68 | 10.8 | 1.365984 | 5.978016 | 0 |
| 689 | 30 | 1 | 4 | 2 | 18 | 10.7 | 0.227268 | 1.698732 | 0 |
| 690 | 24 | 2 | 0 | 5 | 16 | 7.3 | 0.024528 | 1.143472 | 0 |
| 691 | 47 | 1 | 31 | 8 | 253 | 7.2 | 9.308376 | 8.907624 | 0 |
| 692 | 53 | 1 | 0 | 26 | 27 | 28.9 | 2.754459 | 5.048541 | 1 |
| 693 | 22 | 3 | 0 | 2 | 20 | 4.7 | 0.219020 | 0.720980 | 0 |
| 694 | 48 | 2 | 6 | 1 | 66 | 12.1 | 2.315940 | 5.670060 | 0 |
| 695 | 36 | 2 | 6 | 15 | 27 | 4.6 | 0.262062 | 0.979938 | 1 |
| 696 | 29 | 2 | 6 | 4 | 21 | 11.5 | 0.369495 | 2.045505 | 0 |
| 697 | 33 | 1 | 15 | 3 | 32 | 7.6 | 0.491264 | 1.940736 | 0 |
| 698 | 45 | 1 | 19 | 22 | 77 | 8.4 | 2.302608 | 4.165392 | 0 |
| 699 | 37 | 1 | 12 | 14 | 44 | 14.7 | 2.994684 | 3.473316 | 0 |
700 rows × 9 columns
In [17]:
x = data.iloc[:,:8].as_matrix() # iloc是取特定的行和列，逗号前为行，逗号后为列。前闭后开。这个是自变量
In [18]:
y = data.iloc[:,8].as_matrix() # 取的是index=8，即第九列。这个是因变量
In [19]:
from sklearn.linear_model import LogisticRegression as LR # 逻辑回归
from sklearn.linear_model import RandomizedLogisticRegression as RLR # 随机逻辑回归
In [20]:
rlr = RLR() # 初始化随机逻辑回归模型
In [21]:
rlr.fit(x,y) # 训练模型
Out[21]:
RandomizedLogisticRegression(C=1, fit_intercept=True,
memory=Memory(cachedir=None), n_jobs=1, n_resampling=200,
normalize=True, pre_dispatch=’3*n_jobs’, random_state=None,
sample_fraction=0.75, scaling=0.5, selection_threshold=0.25,
tol=0.001, verbose=False)
In [22]:
rlr.get_support() # 获取特征的筛选结果
Out[22]:
array([False, False, True, True, False, True, True, False], dtype=bool)
In [23]:
rlr.scores_ # 查看各特征的分数
Out[23]:
array([ 0.095, 0.09 , 1. , 0.4 , 0. , 0.985, 0.55 , 0.02 ])
In [24]:
x = data[data.columns[rlr.get_support()]].as_matrix() # 筛选好特征
/Users/frontc/anaconda/lib/python2.7/site-packages/pandas/indexes/base.py:1275: VisibleDeprecationWarning: boolean index did not match indexed array along dimension 0; dimension is 9 but corresponding boolean dimension is 8
result = getitem(key)
In [26]:
lr = LR() # 建立逻辑回归模型
In [27]:
lr.fit(x,y) # 用筛选后的特征数据来训练模型
Out[27]:
LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,
intercept_scaling=1, max_iter=100, multi_class=’ovr’, n_jobs=1,
penalty=’l2’, random_state=None, solver=’liblinear’, tol=0.0001,
verbose=0, warm_start=False)
In [28]:
lr.score(x,y) # 查看模型的平均正确率
Out[28]:
0.81428571428571428