I want to get the LightGBM built-in variable importance values from a model that was generated by AutoML. That's not logged in the metrics by default - can I change a setting so that it will be logged?
More fundamentally: what I'd really like is to modify the LightGBM notebook generated by AutoML so that it logs the model to MLflow with flavor.loader_module equal to mlflow.lightgbm. By default, it logs with that parameter equal to mlflow.sklearn.
As a result, if I want to load that model back in for new predictions, I have to use model = mlflow.pyfunc.load_model(), and that loads the model in a generic format that doesn't include useful LightGBM things like model.feature_importances.
I want to be able to load the model back in via model = mlflow.lightgbm.load_model(), but currently that generates an error because the model wasn't saved in the right format for that.
Now, I know model = mlflow.lightgbm.load_model() succeeds on a different model that I originally saved in LightGBM flavor via mlflow.lightgbm.log_model(). But the AutoML notebook doesn't use load_model(), so I have to look further for a way to force LightGBM flavor.
In that vein, I did find a command pyfunc.add_to_model(mlflow_model, loader_module="mlflow.sklearn") in the notebook. Sadly, changing it to loader_module="mlflow.lightgbm" had no discernable effect on the problem. The model saved to MLflow still had flavor.loader_module equal to mlflow.sklearn.
