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ML之shap：基于adult人口普查收入二分类预测数据集(预测年收入是否超过50k)利用Shap值对XGBoost模型实现可解释性案例之详细攻略

2022-07-06 06:33:00 【一个处女座的程序猿】

基于adult人口普查收入二分类预测数据集(预测年收入是否超过50k)利用Shap值对XGBoost模型实现可解释性案例

# (2)、单转双特征全样本局部独立图散点图可视化

# (3)、双特征全样本散点图可视化

# 4.3、模型特征筛选

# (1)、基于聚类的shap特征筛选可视化

5、模型预测的可解释性(可主要分析误分类的样本)

# 5.1、力图可视化分析：可视化单个或多个样本内各个特征贡献度并对比模型预测值——探究误分类样本

(1)、单个样本力图可视化—对比预测

(2)、多个样本力图可视化

# 5.2、决策图可视化分析：模型如何做出决策

# (1)、单个样本决策图可视化

# (2)、多个样本决策图可视化

基于adult人口普查收入二分类预测数据集(预测年收入是否超过50k)利用Shap值对XGBoost模型实现可解释性案例

1、定义数据集

dtypes_len: 15

age

workclass

fnlwgt

education

education_num

marital_status

occupation

relationship

race

sex

capital_gain

capital_loss

hours_per_week

native_country

salary

State-gov

77516

Bachelors

Never-married

Adm-clerical

Not-in-family

White

Male

2174

United-States

<=50K

Self-emp-not-inc

83311

Bachelors

Married-civ-spouse

Exec-managerial

Husband

White

Male

United-States

<=50K

Private

215646

HS-grad

Divorced

Handlers-cleaners

Not-in-family

White

Male

United-States

<=50K

Private

234721

11th

Married-civ-spouse

Handlers-cleaners

Husband

Black

Male

United-States

<=50K

Private

338409

Bachelors

Married-civ-spouse

Prof-specialty

Wife

Black

Female

Cuba

<=50K

Private

284582

Masters

Married-civ-spouse

Exec-managerial

Wife

White

Female

United-States

<=50K

Private

160187

9th

Married-spouse-absent

Other-service

Not-in-family

Black

Female

Jamaica

<=50K

Self-emp-not-inc

209642

HS-grad

Married-civ-spouse

Exec-managerial

Husband

White

Male

United-States

>50K

Private

45781

Masters

Never-married

Prof-specialty

Not-in-family

White

Female

14084

United-States

>50K

Private

159449

Bachelors

Married-civ-spouse

Exec-managerial

Husband

White

Male

5178

United-States

>50K

2、数据集预处理

# 2.1、入模特征初步筛选

df.columns
14

# 2.2、目标特征二值化

# 2.3、类别型特征编码数字化

filt_dtypes_len： 13 [('age', 'float32'), ('workclass', 'category'), ('fnlwgt', 'float32'), ('education_Num', 'float32'), ('marital_status', 'category'), ('occupation', 'category'), ('relationship', 'category'), ('race', 'category'), ('sex', 'category'), ('capital_gain', 'float32'), ('capital_loss', 'float32'), ('hours_per_week', 'float32'), ('native_country', 'category')]

# 2.4、分离特征与标签

df_adult_display

	age	workclass	education_num	marital_status	occupation	relationship	race	sex	capital_gain	capital_loss	hours_per_week	native_country	salary
0	39	State-gov	13	Never-married	Adm-clerical	Not-in-family	White	Male	2174	0	40	United-States	0
1	50	Self-emp-not-inc	13	Married-civ-spouse	Exec-managerial	Husband	White	Male	0	0	13	United-States	0
2	38	Private	9	Divorced	Handlers-cleaners	Not-in-family	White	Male	0	0	40	United-States	0
3	53	Private	7	Married-civ-spouse	Handlers-cleaners	Husband	Black	Male	0	0	40	United-States	0
4	28	Private	13	Married-civ-spouse	Prof-specialty	Wife	Black	Female	0	0	40	Cuba	0
5	37	Private	14	Married-civ-spouse	Exec-managerial	Wife	White	Female	0	0	40	United-States	0
6	49	Private	5	Married-spouse-absent	Other-service	Not-in-family	Black	Female	0	0	16	Jamaica	0
7	52	Self-emp-not-inc	9	Married-civ-spouse	Exec-managerial	Husband	White	Male	0	0	45	United-States	1
8	31	Private	14	Never-married	Prof-specialty	Not-in-family	White	Female	14084	0	50	United-States	1
9	42	Private	13	Married-civ-spouse	Exec-managerial	Husband	White	Male	5178	0	40	United-States	1

df_adult

	age	workclass	education_num	marital_status	occupation	relationship	race	sex	capital_gain	capital_loss	hours_per_week	native_country	salary
0	39	7	13	4	1	1	4	1	2174	0	40	39	0
1	50	6	13	2	4	0	4	1	0	0	13	39	0
2	38	4	9	0	6	1	4	1	0	0	40	39	0
3	53	4	7	2	6	0	2	1	0	0	40	39	0
4	28	4	13	2	10	5	2	0	0	0	40	5	0
5	37	4	14	2	4	5	4	0	0	0	40	39	0
6	49	4	5	3	8	1	2	0	0	0	16	23	0
7	52	6	9	2	4	0	4	1	0	0	45	39	1
8	31	4	14	4	10	1	4	0	14084	0	50	39	1
9	42	4	13	2	4	0	4	1	5178	0	40	39	1

# 2.5、数据集整体切分

df_len： 32561 ，train_test_index： 30933
X.shape,y.shape： (30933, 12) (30933,)
X_test.shape,y_test.shape： (1628, 12) (1628,)

#3、模型训练与推理

# 3.1、数据集切分

# 3.2、模型建立并训练

# 3.3、模型预测

	age	workclass	education_num	marital_status	occupation	relationship	race	sex	capital_gain	capital_loss	hours_per_week	native_country	y_val_predi	y_val
11311	29	4	9	4	1	3	2	0	0	0	60	39	0	0
12519	33	4	10	4	3	1	2	1	8614	0	40	39	1	1
29225	27	4	13	4	10	1	4	1	0	0	45	39	0	0
5428	22	4	9	2	7	0	4	1	0	0	40	39	0	0
2400	32	7	10	4	1	1	2	0	0	0	40	39	0	0
4319	45	4	10	2	4	0	4	1	0	0	40	39	1	0
26564	43	4	9	2	6	0	4	1	0	0	40	39	0	0
4721	60	0	13	2	0	0	4	1	0	0	8	39	0	1
19518	29	6	9	2	12	0	4	1	0	0	35	39	0	0
25013	33	4	5	2	6	0	4	1	0	0	40	39	0	0

#4、模型特征重要性解释可视化

#4.1、全局特征重要性可视化

# T1、基于模型本身输出特征重要性

XGBR_importance_dict： [('age', 130), ('capital_gain', 125), ('education_num', 86), ('capital_loss', 75), ('hours_per_week', 63), ('relationship', 59), ('marital_status', 52), ('occupation', 52), ('workclass', 20), ('sex', 13), ('native_country', 10), ('race', 6)]

# T2、利用Shap值解释XGBR模型

利用shap自带的函数实现特征贡献性可视化——特征重要性排序与上边类似，但并不相同

# (1)、创建Explainer并计算SHAP值

# T2.1、输出shap.Explanation对象

# T2，2、输出numpy.array数组

shap2exp.values.shape (30933, 12) 
 [[ 0.31074238 -0.16607898  0.5617416  ... -0.04660619 -0.09465054
   0.00530914]
 [ 0.34912622 -0.16633348  0.65308005 ... -0.06718991 -0.9804511
   0.00515459]
 [ 0.21971266  0.02263742 -0.299867   ... -0.0583196  -0.09738331
   0.00415599]
 ...
 [-0.48140627  0.07019287 -0.30844492 ... -0.04253047 -0.10924102
   0.00649792]
 [ 0.39729887 -0.2313431  -0.45257783 ... -0.06502013  0.27416423
   0.00587647]
 [ 0.27594262  0.03170239  0.78293955 ... -0.06743324  0.31613
   0.00530914]]
shap2array.shape (30933, 12) 
 [[ 0.31074238 -0.16607898  0.5617416  ... -0.04660619 -0.09465054
   0.00530914]
 [ 0.34912622 -0.16633348  0.65308005 ... -0.06718991 -0.9804511
   0.00515459]
 [ 0.21971266  0.02263742 -0.299867   ... -0.0583196  -0.09738331
   0.00415599]
 ...
 [-0.48140627  0.07019287 -0.30844492 ... -0.04253047 -0.10924102
   0.00649792]
 [ 0.39729887 -0.2313431  -0.45257783 ... -0.06502013  0.27416423
   0.00587647]
 [ 0.27594262  0.03170239  0.78293955 ... -0.06743324  0.31613
   0.00530914]]
shap2exp.values与shap2array，两个矩阵否相等： True

# (2)、全样本各特征shap值条形图可视化

# shap值高阶交互可视化

# (3)、全样本各特征shap值蜂群图可视化

# (4)、全局特征重要性排序散点图可视化

#4.2、局部特征重要性可视化

# (1)、单样本全特征条形图可视化

前测试样本：0

.values =
array([ 0.31074238, -0.16607898,  0.5617416 , -0.58709425, -0.08897061,
       -0.6133537 ,  0.01539118,  0.04758333, -0.3988452 , -0.04660619,
       -0.09465054,  0.00530914], dtype=float32)
.base_values =
-1.3270257
.data =
array([3.900e+01, 7.000e+00, 1.300e+01, 4.000e+00, 1.000e+00, 1.000e+00,
       4.000e+00, 1.000e+00, 2.174e+03, 0.000e+00, 4.000e+01, 3.900e+01])

前测试样本：1

.values =
array([ 0.34912622, -0.16633348,  0.65308005,  0.3069151 ,  0.26878497,
        0.5229906 ,  0.01030679,  0.04531586, -0.15429462, -0.06718991,
       -0.9804511 ,  0.00515459], dtype=float32)
.base_values =
-1.3270257
.data =
array([50.,  6., 13.,  2.,  4.,  0.,  4.,  1.,  0.,  0., 13., 39.])

前测试样本：10

.values =
array([ 0.27578622,  0.02686635, -0.0699547 ,  0.2820353 ,  0.3097189 ,
        0.55229187, -0.03686382,  0.05135565, -0.1607191 , -0.06321771,
        0.38190693,  0.02023092], dtype=float32)
.base_values =
-1.3270257
.data =
array([37.,  4., 10.,  2.,  4.,  0.,  2.,  1.,  0.,  0., 80., 39.])

前测试样本：20

.values =
array([ 0.31008577,  0.00316932,  1.3133987 ,  0.16768128,  0.18239255,
        0.6863757 ,  0.00508371,  0.05159741, -0.15813455, -0.06736177,
        0.31327826,  0.01936885], dtype=float32)
.base_values =
-1.3270257
.data =
array([40.,  4., 16.,  2., 10.,  0.,  4.,  1.,  0.,  0., 60., 39.])

# (2)、单转双特征全样本局部独立图散点图可视化

# (3)、双特征全样本散点图可视化

# 4.3、模型特征筛选

# (1)、基于聚类的shap特征筛选可视化

5、模型预测的可解释性(可主要分析误分类的样本)

提供了预测的细节，侧重于解释单个预测是如何生成的。它可以帮助决策者信任模型，并且解释各个特征是如何影响模型单次的决策。

# 5.1、力图可视化分析：可视化单个或多个样本内各个特征贡献度并对比模型预测值——探究误分类样本

提供了单一模型预测的可解释性，可用于误差分析，找到对特定实例预测的解释。如样例0所示：
(1)、模型输出值：5.89；
(2)、基值：base value即explainer.expected_value，即模型输出与训练数据的平均值；
(3)、绘图箭头下方数字是此实例的特征值。如Age=39；
(4)、红色则表示该特征的贡献是正数(将预测推高的特征)，蓝色表示该特征的贡献是负数(将预测推低的特征)。长度表示影响力；箭头越长，特征对输出的影响(贡献)越大。通过 x 轴上刻度值可以看到影响的减少或增加量。

(1)、单个样本力图可视化—对比预测

输出当前测试样本：0

mode_exp_value： -1.3270257
<IPython.core.display.HTML object>
输出当前测试样本：0 
 age               29.0
workclass          4.0
education_num      9.0
marital_status     4.0
occupation         1.0
relationship       3.0
race               2.0
sex                0.0
capital_gain       0.0
capital_loss       0.0
hours_per_week    60.0
native_country    39.0
y_val_predi        0.0
y_val              0.0
Name: 11311, dtype: float64
输出当前测试样本的真实label： 0
输出当前测试样本的的预测概率： 0

输出当前测试样本：1

输出当前测试样本：1 
 age                 33.0
workclass            4.0
education_num       10.0
marital_status       4.0
occupation           3.0
relationship         1.0
race                 2.0
sex                  1.0
capital_gain      8614.0
capital_loss         0.0
hours_per_week      40.0
native_country      39.0
y_val_predi          1.0
y_val                1.0
Name: 12519, dtype: float64
输出当前测试样本的真实label： 1
输出当前测试样本的的预测概率： 1

输出当前测试样本：5

输出当前测试样本：5 
 age               45.0
workclass          4.0
education_num     10.0
marital_status     2.0
occupation         4.0
relationship       0.0
race               4.0
sex                1.0
capital_gain       0.0
capital_loss       0.0
hours_per_week    40.0
native_country    39.0
y_val_predi        1.0
y_val              0.0
Name: 4319, dtype: float64
输出当前测试样本的真实label： 0
输出当前测试样本的的预测概率： 1

输出当前测试样本：7

输出当前测试样本：7 
 age               60.0
workclass          0.0
education_num     13.0
marital_status     2.0
occupation         0.0
relationship       0.0
race               4.0
sex                1.0
capital_gain       0.0
capital_loss       0.0
hours_per_week     8.0
native_country    39.0
y_val_predi        0.0
y_val              1.0
Name: 4721, dtype: float64
输出当前测试样本的真实label： 1
输出当前测试样本的的预测概率： 0