高斯过程的SHAP值为零。

Question

我试图使用SHAP库获得高斯过程回归(GPR)模型的SHAP值。然而，所有SHAP值都为零。我正在使用正式文件中的示例。我只是把模型改成了探地雷达。

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)

运行上述代码将给出以下情节：

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当我使用神经网络或线性回归，上述代码工作良好，没有问题。

如果你知道如何解决这个问题，请告诉我。

Answer 1

你的模型不能预测任何事情：

plt.scatter(y_test, gp.predict(X_test));

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适当地训练你的模型，如下所示：

plt.scatter(y_test, gp.predict(X_test));

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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)

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完全可复制的示例

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

Answer 2

试试下面的代码：

kernel = 1.0 * Matern(length_scale=1.0, nu=2.5) + \
WhiteKernel(noise_level=10**-1,noise_level_bounds=(10**-1, 10**1))

model = GaussianProcessRegressor(kernel=kernel, 
                           optimizer='fmin_l_bfgs_b',random_state=123)              


explainer = shap.Explainer(model.predict,X_train)
shap_values = explainer.shap_values(X_train)
shap.plots.bar(shap_values)                                  ## bar plot
shap.summary_plot(shap_values, X_train,show=False)           ## summary