How can I explain predictions of an imblearn pipeline?
Imblearn's samplers are effectively no-op (ie. identity) transformers during prediction. Therefore, it should be safe to delete them after the pipeline has been fitted.
Try the following workflow:

Construct an Imblearn pipeline, and fit it.
Extract the steps of the fitted Imblearn pipeline to a new Scikit-Learn pipeline.
Delete the SMOTE step.
Explain your predictions using standard Scikit-Learn pipeline explanation tools.
I have an imblearn (not sklearn) pipeline consisting of the following steps:

Column selector
Preprocessing pipeline (ColumnTransformer with OneHotEncoders and CountVectorizers on different columns)
imblearn's SMOTE
XGBClassifier

I have a tabular dataset and I'm trying to explain my predictions.
I managed to work out feature importance plots with some work, but can't get either
eli5 or lime to work.
Lime requires that I transform the data to the state of before the last transformation (because the transformers in the Pipeline like custom vectorizers create new columns).
In principle, I can slice my Pipeline like this: pipeline[:-1].predict(instance). However, I get the following error: {AttributeError}'SMOTE' object has no attribute 'predict'.
I also tried an eli5 explainer, since it supposedly works with Sklearn Pipelines.
However, after running eli5.sklearn.explain_prediction.explain_prediction_sklearn_not_supported(pipeline, instance_to_explain) I get the message that the classifier is not supported.
Will appreciate any ideas on how to proceed with this.