ks代码助解

亦凉 2021-12-05 05:53 238阅读 0赞

![1353331-20190707170051204-671543211.png][]

代码实现：

data_test_2 = {
        'gd':[1,1,1,1,1,1,0,0,0,0,0,0,0],'score':[1,2,0,2,2,7,4,5,4,0,4,18,np.nan]}
    data_test_2 = pd.DataFrame(data_test_2)
    
    def ks_calc_cross(data,var_col,y_col):
        '''
        功能: 计算KS值，输出对应分割点和累计分布
        输入值:-
        输出值:
        'ks': KS值，'crossdens': 好坏人累积概率分布以及其差值gap
        '''
        ks_df1 = pd.crosstab(data[var_col],data[y_col])
        print(ks_df1)
        print(ks_df1.cumsum(axis=0))
        print(ks_df1.sum())
        ks_df2 = ks_df1.cumsum(axis=0) / ks_df1.sum()
        print(ks_df2)
        ks_df2['gap'] = abs(ks_df2[0] - ks_df2[1])
        ks = ks_df2['gap'].max()
        return ks
    
    ks_value = ks_calc_cross(data_test_2, 'score', 'gd')
    print(ks_value)

gd     0  1
    score      
    0.0    1  1
    1.0    0  1
    2.0    0  3
    4.0    3  0
    5.0    1  0
    7.0    0  1
    18.0   1  0
    gd     0  1
    score      
    0.0    1  1
    1.0    1  2
    2.0    1  5
    4.0    4  5
    5.0    5  5
    7.0    5  6
    18.0   6  6
    gd
    0    6
    1    6
    dtype: int64
    gd            0         1
    score                    
    0.0    0.166667  0.166667
    1.0    0.166667  0.333333
    2.0    0.166667  0.833333
    4.0    0.666667  0.833333
    5.0    0.833333  0.833333
    7.0    0.833333  1.000000
    18.0   1.000000  1.000000
    0.666666666667
    
    庖丁解牛：

import pandas as pd
    import numpy as np
    
    data_test_2 = {
        'gd':[1,1,1,1,1,1,0,0,0,0,0,0,0],'score':[1,2,0,2,2,7,4,5,4,0,4,18,np.nan]}
    data_test_2 = pd.DataFrame(data_test_2)
    data_test_2

![1353331-20190707170238398-63839425.png][]

ks_df1 = pd.crosstab(data_test_2['score'],data_test_2['gd'])
    ks_df1

![1353331-20190707170302357-2026019151.png][]

ks_df1.cumsum(axis=0)

![1353331-20190707170601546-945541117.png][]

ks_df1.sum()

![1353331-20190707170623105-186260107.png][]

ks_df2 = ks_df1.cumsum(axis=0) / ks_df1.sum()
    ks_df2

![1353331-20190707170644708-1685268394.png][]

ks_df2['gap'] = abs(ks_df2[0] - ks_df2[1])
    ks_df2

![1353331-20190707170703374-2137248989.png][]

ks_df2['gap'].max()

![1353331-20190707170723490-685342914.png][]

转载于:https://www.cnblogs.com/wqbin/p/11146836.html

[1353331-20190707170051204-671543211.png]: /images/20211205/7f489ff37f8249c984f8b340981e49c7.png
[1353331-20190707170238398-63839425.png]: /images/20211205/7c91ee73a3f84c629aaccb4f409317c5.png
[1353331-20190707170302357-2026019151.png]: /images/20211205/6a51c87fb30f450b8ed2ee2fae32b990.png
[1353331-20190707170601546-945541117.png]: /images/20211205/f1c1c06485b64101b272ec06bd22de55.png
[1353331-20190707170623105-186260107.png]: /images/20211205/4cc56276aa2d4f4d876d07b8dcf08f28.png
[1353331-20190707170644708-1685268394.png]: /images/20211205/b87aad88cd304de68438a5eacf06d991.png
[1353331-20190707170703374-2137248989.png]: /images/20211205/532717be8cd948539d00357ab4324067.png
[1353331-20190707170723490-685342914.png]: /images/20211205/67ee5acafebc4df58efe9eeaf722409d.png