load MLFlow model and plot feature importance with feature names

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If I save a xgboost model in mlflow with mlflow.xgboost.log_model(model, "model") and load it with model = mlflow.xgboost.load_model("models:/model_uri") and want to plot the feature importance with xgboost.plot_importance(model) the problem is that the features are not shown with names (see plot). If I plot the feature without saving in mlflow the origin feature names are shown. Do I have to store the model in another way? enter image description here

mlflow xgboost
1 Answers

Usually, if you use a pipeline It can happen for example on FeatureUnion from sklearn.

You can try to get the feature index from the model or the last step of the pipeline and use it to retrieve the feature names from the dataset.

If you are using a pipeline you can try to get the feature the step before this problem appears or edit the step, also be aware if you are using feature selection different situations can happen.

You can use autologging to autosave the plot, but the same problem happen if it is pipeline.

You could save the model like an artifact, if you think to do it, my suggestion is to use the Dill package.

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