Plot SHAP values in waterfall and beeswarm plots-CodePudding

I am working on binary classification using random forest. However, am trying to use SHAP to explain the model predictions. However, I keep getting the below error. I am following the tutorial here

import shap
explainer = shap.Explainer(rf_boruta)  #pass my model
shap_values = explainer(ord_test_t) #pass my test dataset
sample_idx = 15
shap_vals = explainer.shap_values(ord_test_t.iloc[sample_idx:sample_idx 1])
print("Base Value : ", explainer.expected_value)
print()
print("Shap Values for Sample %d : "%sample_idx, shap_vals)
print("\n")
print("Prediction From Model                            : ", rf_boruta.predict(ord_test_t.iloc[15:16]))
print("Prediction From Adding SHAP Values to Base Value : ", explainer.expected_value   shap_vals.sum())

I get an error as shown below

>       8 print("\n")
>       9 print("Prediction From Model                            : ", rf_boruta.predict(ord_test_t.iloc[sample_idx:sample_idx 1]))
> ---> 10 print("Prediction From Adding SHAP Values to Base Value : ", explainer.expected_value   shap_vals.sum())
> 
> AttributeError: 'list' object has no attribute 'sum'

When I tried another tutorial here, I got another error

explainer = shap.TreeExplainer(rf_boruta,ord_test_t)
shap_values = explainer.shap_values(ord_test_t)
sample_ind = 0
shap.waterfall_plot(explainer.expected_value, shap_values[sample_ind],ord_test_t.iloc[sample_ind])


TypeError: waterfall() got multiple values for argument 'max_display'

Later, when I change it to default, I get another error as shown below

> ---> 46     base_values = shap_values.base_values
>      47 
>      48     features = shap_values.data
> 
> AttributeError: 'numpy.ndarray' object has no attribute 'base_values'

update - another code tried

row_to_show = 5
data_for_prediction = ord_test_t.iloc[row_to_show]  # use 1 row of data here. Could use multiple rows if desired
data_for_prediction_array = data_for_prediction.values.reshape(1, -1)
rf_boruta.predict_proba(data_for_prediction_array)
explainer = shap.TreeExplainer(rf_boruta)
# Calculate Shap values
shap_values = explainer.shap_values(data_for_prediction)
shap.waterfall_plot(explainer.expected_value,shap_values,data_for_prediction)

CodePudding user response：

The error callback tells you what the problem is:

10 print("Prediction From Adding SHAP Values to Base Value : ", explainer.expected_value   shap_vals.sum())

AttributeError: 'list' object has no attribute 'sum'

So instead of calling shap_vals.sum() on your list, get the sum in a way that is supported, like using the built-in sum function:

print("Prediction From Adding SHAP Values to Base Value : ", explainer.expected_value   sum(shap_vals))