Do you mean being able to wrap the created model in a scikit-learn Pipeline? This isn't something we've thought about and we haven't explicitly built support for it, though we could.
As of now, I think you could relatively easily wrap the plexe model, which has a `predict()` method, in a scikit-learn Estimator. You could then plug it into a Pipeline.
What do you have in mind? How would you want to use this with scikit-learn pipelines?
I think what I'm after is being able to put these in pipeline.
I.e. if I already have some data cleaning/normalisation, some dimensional reduction and then some fitting, being able to drop the Agent in place with an appropriate description and task.
Cleaning: Feed it a data frame and have it figure out what needs imputing etc.
The rest: Could either be separate tasks or one big task for the Agent..
impresburger|10 months ago
As of now, I think you could relatively easily wrap the plexe model, which has a `predict()` method, in a scikit-learn Estimator. You could then plug it into a Pipeline.
What do you have in mind? How would you want to use this with scikit-learn pipelines?
drlobster|10 months ago
I.e. if I already have some data cleaning/normalisation, some dimensional reduction and then some fitting, being able to drop the Agent in place with an appropriate description and task.
Cleaning: Feed it a data frame and have it figure out what needs imputing etc.
The rest: Could either be separate tasks or one big task for the Agent..