归因分析笔记6：SHAP代码笔记.pdfVIP

归因分析笔记6 ：SHAP代码笔记 ⽬录 Python包: 该包的⽂档: SHAP （SHapley Additive exPlanations）是⼀种归因⽅法attribution method, ⼀种描述特征影响模型平均⾏为的全局解释⽅法. 基于解 释单个预测的局部解释⽅法Shapley 值, 通过组合Shapley 值得到. SHAP包的介绍参考: SHAP多分类参考: 安装 pip install shap or conda install -c conda-forge shap activate Liver pip install shap 使⽤⽰例 Kernel SHAP的实现, 核 SHAP 是⼀种与模型⽆关的⽅法，⽤于估计任何模型的 SHAP 值。因为它不对模型类型做出假设，所以 KernelExplainer ⽐其他特定于模型类型的算法慢。 该例⼦解释iris数据集上的多分类 SVM 完整的notebook代码(解释scikit-learn中的6种模型): import sklearn import shap from sklearn.model_selection import train_test_split # print the JS visualization code to the notebook shap.initj s() # train a SVM classifier X_train,X_test,Y_train,Y_test = train_test_split(*shap.datasets.iris(), test_size=0.2, random_state=0) svm = sklearn.svm.SVC(kernel=rbf, probability=True) svm.fit(X_train, Y_train) # ⽤ Kernel SHAP解释测试集的预测 explainer = shap.KernelExplainer(svm.predict_proba, X_train, link=logit) shap_values = explainer.shap_values(X_test, nsamples= 100) 其中explainer.shap_values(X_test, nsamples= 100)代表解释每个预测(单个测试样本)时重新评估模型的次数(见下⽅) 分别解释4个特征贡献, 从平均输出0.3206推向0.0 1 shap_values() 进⼊shap_values函数仔细看下: 作⽤: 估计⼀组采样的Shap值 输⼊参数 X : numpy.array or pandas.DataFrame or any scipy.sparse 矩阵 ⽤于解释模型输出的样本矩阵 nsamples : auto or int Number of times to re-evaluate the model when explaining each prediction. More samples lead to lower variance estimates of the SHAP values. The auto setting uses `nsamples = 2 * X.shape[1] + 2048`. 解释每个预测(单个测试样本)时重新评估模型的次数。样本越多，Shap值的⽅差估计越低。设为auto代表使⽤ `nSamples=2*X.Shape[1]+2048`。 返回值 array or list For models with a single output this returns a matrix of SHAP values (# samples x # features). Each row sums to the difference between the model output for that sample and the expected value of the model output (which is stored as expected_value attribute of the explainer). For models with vector outputs this returns a list of such matrice