如何解决高斯过程回归量的 SHAP 值为零
我正在尝试使用 SHAP 库获取高斯过程回归 (GPR) 模型的 SHAP 值。但是,所有 SHAP 值都为零。我正在使用 official documentation 中的示例。我只将模型更改为探地雷达。
import sklearn
from sklearn.model_selection import train_test_split
import numpy as np
import shap
import time
from sklearn.gaussian_process import GaussianProcessRegressor
from sklearn.gaussian_process.kernels import Matern,WhiteKernel,ConstantKernel
shap.initjs()
X,y = shap.datasets.diabetes()
X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.2,random_state=0)
# rather than use the whole training set to estimate expected values,we summarize with
# a set of weighted kmeans,each weighted by the number of points they represent.
X_train_summary = shap.kmeans(X_train,10)
kernel = Matern(length_scale=2,nu=3/2) + WhiteKernel(noise_level=1)
gp = GaussianProcessRegressor(kernel)
gp.fit(X_train,y_train)
# explain all the predictions in the test set
explainer = shap.KernelExplainer(gp.predict,X_train_summary)
shap_values = explainer.shap_values(X_test)
shap.summary_plot(shap_values,X_test)
运行上面的代码给出如下图:
当我使用神经网络或线性回归时,上面的代码可以正常工作。
如果您有任何想法如何解决此问题,请告诉我。
解决方法
您的模型无法预测任何内容:
plt.scatter(y_test,gp.predict(X_test));
正确训练您的模型,如下所示:
plt.scatter(y_test,gp.predict(X_test));
你可以走了:
explainer = shap.KernelExplainer(gp.predict,X_train_summary)
shap_values = explainer.shap_values(X_test)
shap.summary_plot(shap_values,X_test)
完全可重现的示例:
import sklearn
from sklearn.model_selection import train_test_split
import numpy as np
import shap
import time
from sklearn.gaussian_process import GaussianProcessRegressor
from sklearn.gaussian_process.kernels import WhiteKernel,DotProduct
X,y = shap.datasets.diabetes()
X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.2,random_state=0)
X_train_summary = shap.kmeans(X_train,10)
kernel = DotProduct() + WhiteKernel()
gp = GaussianProcessRegressor(kernel)
gp.fit(X_train,y_train)
explainer = shap.KernelExplainer(gp.predict,X_test)
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