用KernelExplainer和LinearExplainer建立线性模型瀑布模型

Question

我正在研究二进制分类，并试图用SHAP框架解释我的模型。

我使用的是logistic回归算法。我想用KernelExplainer和LinearExplainer来解释这个模型。

所以，我尝试了下面来自So here的代码

from sklearn.ensemble import RandomForestClassifier
from sklearn.datasets import load_breast_cancer
from shap import TreeExplainer, Explanation
from shap.plots import waterfall

X, y = load_breast_cancer(return_X_y=True, as_frame=True)

idx = 9
model = LogisticRegression().fit(X, y)
background = shap.maskers.Independent(X, max_samples=100)
explainer = KernelExplainer(model,background)
sv = explainer(X.iloc[[5]])   # pass the row of interest as df
exp = Explanation(
    sv.values[:, :, 1],         # class to explain
    sv.base_values[:, 1],
    data=X.iloc[[idx]].values,  # pass the row of interest as df
    feature_names=X.columns,
)
waterfall(exp[0])  

这引发了一个错误，如下所示

AssertionError:作为数据对象传递的未知类型：

如何使用logistic regression和SHAP LinearExplainer解释SHAP KernelExplainer模型？

Answer 1

计算-从以下几个方面可以做到：

from sklearn.linear_model import LogisticRegression
from sklearn.datasets import load_breast_cancer

from shap import LinearExplainer, KernelExplainer, Explanation
from shap.plots import waterfall
from shap.maskers import Independent

X, y = load_breast_cancer(return_X_y=True, as_frame=True)

idx = 9
model = LogisticRegression().fit(X, y)

explainer = KernelExplainer(model.predict, X)
sv = explainer.shap_values(X.loc[[5]])   # pass the row of interest as df

exp = Explanation(sv,explainer.expected_value, data=X.loc[[idx]].values, feature_names=X.columns)
waterfall(exp[0])

​

​

注意：KernelExplainer不支持掩码，在本例中，loc或iloc都会返回相同的内容。

background = Independent(X, max_samples=100)
explainer = LinearExplainer(model,background)
sv = explainer(X.loc[[5]])   # pass the row of interest by index
waterfall(sv[0])

​

​

请注意，LinearExplainer的结果可以提供给瀑布“原样”。