我还没有找到很多关于PyTorch的SHAP值的例子。我使用了两种技术来生成SHAP值，但是，它们的结果似乎并不一致。

使用PyTorch的SHAP KernelExplainer

import torch
from torch.autograd import Variable
import shap
import numpy
import pandas

torch.set_grad_enabled(False)

# Get features
train_features_df = ... # pandas dataframe
test_features_df = ... # pandas dataframe

# Define function to wrap model to transform data to tensor
f = lambda x: model_list[0]( Variable( torch.from_numpy(x) ) ).detach().numpy()

# Convert my pandas dataframe to numpy
data = test_features_df.to_numpy(dtype=np.float32)

# The explainer doesn't like tensors, hence the f function
explainer = shap.KernelExplainer(f, data)

# Get the shap values from my test data
shap_values = explainer.shap_values(data)

# Enable the plots in jupyter
shap.initjs()

feature_names = test_features_df.columns
# Plots
#shap.force_plot(explainer.expected_value, shap_values[0], feature_names)
#shap.dependence_plot("b1_price_avg", shap_values[0], data, feature_names)
shap.summary_plot(shap_values[0], data, feature_names)

使用PyTorch SHAP DeepExplainer

# It wants gradients enabled, and uses the training set
torch.set_grad_enabled(True)
e = shap.DeepExplainer(model, Variable( torch.from_numpy( train_features_df.to_numpy(dtype=np.float32) ) ) )

# Get the shap values from my test data (this explainer likes tensors)
shap_values = e.shap_values( Variable( torch.from_numpy(data) ) )

# Plots
#shap.force_plot(explainer.expected_value, shap_values, feature_names)
#shap.dependence_plot("b1_price_avg", shap_values, data, feature_names)
shap.summary_plot(shap_values, data, feature_names)

对比结果

从汇总图中可以看出，在相同的测试数据下，相同PyTorch模型的特性值明显不同。
例如，特征b1_addresses_avg在KernelExplainer中的值为倒数第一，但在DeepExplainer中的值为倒数第三。
我不知道接下来该怎么办。