我的 shap 值有问题，这是我的模型：
Model: "model_4"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
input_5 (InputLayer) [(None, 158)] 0
__________________________________________________________________________________________________
model_1 (Model) (None, 158) 57310 input_5[0][0]
__________________________________________________________________________________________________
subtract_4 (Subtract) (None, 158) 0 input_5[0][0]
model_1[5][0]
__________________________________________________________________________________________________
multiply_4 (Multiply) (None, 158) 0 subtract_4[0][0]
subtract_4[0][0]
__________________________________________________________________________________________________
lambda_4 (Lambda) (None,) 0 multiply_4[0][0]
__________________________________________________________________________________________________
reshape_3 (Reshape) (None, 1) 0 lambda_4[0][0]
==================================================================================================
Total params: 57,310
Trainable params: 57,310
Non-trainable params: 0
__________________________________________________________________________________________________
我打电话给：
scores = new_model.predict(X_test_scaled)
scores = scores.reshape(scores.shape[0],1)
toexplain = np.append(X_test_scaled, scores, axis = 1)
toexplain = pd.DataFrame(toexplain)
toexplain.sort_values(by = [158], ascending=False, inplace=True)
toexplain = toexplain.iloc[0:16]
toexplain.drop(columns = [158], axis = 1, inplace = True)
explainer=shap.DeepExplainer(new_model, df_sampled_X_train_scaled)
shap_values = explainer.shap_values(toexplain, check_additivity=False)
但是我的 shap 值看起来像这样（对于第一个实例）：
shap_values[0]
array([ nan, nan, nan, 0.08352888, nan,
nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan,
nan, nan, nan, nan, 0.03286453,
nan, nan, 0.2984612 , nan, nan,
nan, 0.01110088, -0.85235232, nan, nan,
nan, nan, nan, nan, -0.27935541,
nan, nan, nan, nan, nan,
nan, nan, -0.18422949, 0.01466912, nan,
nan, nan, -0.1688329 , 0.07462809, 0.03071906,
nan, -0.00554245, nan, nan, nan,
nan, 0.04587848, nan, nan, nan,
nan, 0.05448143, nan, nan, nan,
nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan,
nan, 0.00933742, nan, nan, nan,
nan, nan, nan, nan, nan,
nan, nan, 0.00919492, nan, nan,
nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan,
nan, nan, nan])
我相当确定我的 shap_values 中不应该有 nan 值，但我似乎找不到原始问题。此外，由 给出的预测值shap.force_plot与我的模型的预测不同，这就是我首先检查我的 shap_values 的原因。
有人知道我该如何解决吗？