I am evaluating TensorFlow Decision Forests code as a replacement for existing code that usesTensorFlow’s BoostedTreesClassifier.
I am running TFDF v0.1.7 on Ubuntu Linux 20.04.2 LTS.
Using the example code in this tutorial as a starting point:

  
      

      TensorFlow
  

  
    

Build, train and evaluate models with TensorFlow Decision Forests 1



  

  
    
    
  

  


I have been able to train, evaluate, obtain a summary of the model, and save the model successfully using my own data.  I have been using tfdf.keras.RandomForestModel() as a starting point.
My problem is getting programmatic access to the different types of variable importances.  My understanding is that I should be able to do this by obtaining an inspector from the trained model.
   inspector = tfdf_model.make_inspector()
   variable_importances = inspector.variable_importances()

The  return value of variable_importances() is a dict as expected, but when I invoke keys(), the only key returned is NUM_AS_ROOT.  Judging from what I have seen in the output from summary(), I would have expected NUM_NODES, SUM_SCORE, and MEAN_MIN_DEPTH` to be present as well.
Is there something I need to specify to make those statistics accessible from variable_importances()?
Thanks!
–
P.J. Hinton