Limiting tree depth

[adamhaber-atidot]

I'm trying to use gplearn to automate feature engineering.

I have two questions:

1. Is it possible to limit the depth of the programs (not only the initial depth)? I'm looking for "interpretable" features, which would correspond to shallower trees...
2. Is it possible to pass a list of feature names such that the output of the best programs would be more informative?

[trevorstephens]

Interesting idea for # 1. Shall think on it.

On # 2... Can you give more details on the use-case? You don't want to use some features? Why include them in X then?

[trevorstephens]

Or you just want to see the output more readable? Sounds like #73 perhaps?

[adamhaber-atidot]

Yes, definitely related!

What I had in mind is printing add(feature_name_1,feature_name_17) instead of add(X1,X17); for example, by passing an optional features_names list to the print method.

[eggachecat]

@trevorstephens

A reference may be helpful ...

https://github.com/DEAP/deap/blob/f6accf730555c5bbc1c50ac310250ad707353080/deap/gp.py#L891-L914

It seems like that they just replace the one violates the max depth constraint with a new random one