code: def local_accuracy(X_train, y_train, X_test, y_test, attr_test, model_generator, metric, trained_model):
      
  
      X_train, X_test = to_array(X_train, X_test)
  
      # how many features to mask
      assert X_train.shape[1] == X_test.shape[1]
  
      # keep nkeep top features and re-train the model for each test explanation
      yp_test = trained_model.predict(X_test)
  
      return metric(yp_test, strip_list(attr_test).sum(1))
docs: The how well do the features plus a constant base rate sum up to the model output.