如何做单个样本的SHAP的决策图和力图

如何做单个样本的SHAP的决策图和力图，类似这片文章中的做法

img

  • 这篇博客: SHAP的介绍和应用（附代码）中的 SHAP值的常规用法 部分也许能够解决你的问题, 你可以仔细阅读以下内容或跳转源博客中阅读:
  • !pip install shap
import warnings
warnings.filterwarnings("ignore")
import shap
shap.initjs()

    Collecting shap
  Downloading shap-0.40.0-cp37-cp37m-manylinux2010_x86_64.whl (564 kB)
[K     |████████████████████████████████| 564 kB 3.6 MB/s 
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Collecting slicer==0.0.7
  Downloading slicer-0.0.7-py3-none-any.whl (14 kB)
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Installing collected packages: slicer, shap
Successfully installed shap-0.40.0 slicer-0.0.7

    在这里插入图片描述

    #如果数据量大，这个运行的会非常慢
explainer = shap.TreeExplainer(clf)
shap_values = explainer.shap_values(train.drop(columns=['Survived'])) #获取shap value
np.array(shap_values).shape #看一下shap value的dim

    (2, 712, 9)

    shap值是一个三维的数据。一个样本有两个shap值。

    第一个维度控制的是选择哪个类别的shap值，第一个维度是0表示0（negative）类，第一个维度是1表示1（positive）类。

    后面两个维度就是每个样本和每个特征的shap值。

    (shap_values[0] == -1* shap_values[1]).all()

    True

    可以看到0类的shap值和1类的shap值是相反的