Scikit learn 高斯过程回归器的形状值为零

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，所有形状值均为零。我正在使用的例子中。我只是把模型改成了GPR

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)

运行上述代码可得到以下曲线图：

当我使用神经网络或线性回归时，上述代码工作正常，没有问题。

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如果您对如何解决此问题有任何想法，请告诉我。

您的模型无法预测任何情况：

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)

完全可复制的示例：

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在页面上来回跳跃有点难看：这里唯一的区别是内核吗？@Ben唯一的区别是模型没有经过训练。证据：我换了内核，它“起作用了”。模型的性能仍然不太惊人——我预计回归线为45⁰ -- 但接下来的问题是如何正确地训练高斯过程分类器。看看概要图，这是第一个想法。我的意思是，在你的代码中，唯一的变化是内核（一些注释消失了）？@Ben是的，没错

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_train_summary)
shap_values = explainer.shap_values(X_test)
shap.summary_plot(shap_values, X_test)