Any way to send hyperparameter_hunter.Integer to custom feature_engineer function? about hyperparameter_hunter

[HunterMcGushion]

@dePuff, thank you for opening this issue!

I'm sorry, but I don't think I'm completely understanding what functionality you're looking for.

It sounds like you're familiar with HH's feature_engineer, so you may have already seen this Medium article. If you haven't seen it, would you mind checking that out and letting me know if that's at all what you're talking about?

My other thought is that you might be looking for the feature_selector kwarg of CVExperiment. Both feature_engineer and feature_selector are also kwargs in the forge_experiment method of all the OptPros for optimization, which is when you would be using Integer to define a search space.

I'm sorry if I'm completely missing your point. Would you mind providing a minimal code snippet of what you're trying to accomplish?

[puffofsmoke]

First of all. Thank you for your time and for your response.

Yes, I started with Medium article but probably missing something because never use libs like this one before.

For example, if I run something like code below then hyperparameter_hunter will looking for good learning rate by checking learning rates closest to good one.
I found this possibility to be amazing.

opt_1 = BayesianOptPro(iterations=100, random_state=32)
opt_1.forge_experiment(
    AdaBoostRegressor,
    model_init_params=dict(
        n_estimators=40,
        learning_rate=Real(0.01, 1.0),
        loss='linear',
    ),
)
opt_1.go()

From now I wanna use same approaches for feature selection but fail with it.
My expectation was to have something like rasbt.github.io/mlxtend/user_guide/feature_selection/SequentialFeatureSelector/

Let's forget for moment about question in the title because probably it's totally wrong idea how to get what I want.

Could you provide any snippet of code how to make any kind of feature selection with hyperparameter_hunter it will really help to understand the technics?

I tried feature_selector kwargs in this way:

possible_features = [['col_1', 'col_2', 'col_3', 'col_4', ], ['col_1', 'col_2', 'col_4', ]]
...
feature_selector=Categorical(possible_features)

And it did't work anyhow.

But here I had ideas of features combinations which should work.
I really want to know how to make hyperparameter_hunter found them if we don't have them.

Any sample of any technics please.

[HunterMcGushion]

Hahaha thank you so much for bringing this up, because I forgot to add that when I was working on the rest of Feature Engineering! I had always intended to add optimization of feature_selector, so thank you for reminding me it's not actually in there yet.

I'll get started on this and update you with any developments!