new transformer: feature selection using mrmr

[solegalli]

As per description here:

https://medium.com/towards-data-science/mrmr-explained-exactly-how-you-wished-someone-explained-to-you-9cf4ed27458b

and references therein.

[Manuelhrokr]

Hi Soledad,

I went through the suggested TDS article. I see there is an existing library implemented by the author: https://github.com/smazzanti/mrmr. How would the implementation for the Feature-engine library would be under this circumstances? I would like to contribute by adding this transformer if possible. Thanks.

[MetroCat69]

is this still relevant?

[solegalli]

Hey @MetroCat69

Yes, it'd be great to add this implementation to feature engine. Would you like to give it a go?

[MetroCat69]

sure

[MetroCat69]

how should we name it especially not be confused with SmartCorrelatedSelection?

[solegalli]

MRMR or MRMRSelector or MRMRSelection (I think or transformers are called Selection instead of Selector.

[MetroCat69]

one problem that arose is that there is a difference in the algorithms depending if the variables are discrete or continuous. In scikitlearn at least with MI they solve it by splitting into 2 classes:
eg:
https://scikit-learn.org/stable/modules/generated/sklearn.feature_selection.mutual_info_classif.html.
and
https://scikit-learn.org/stable/modules/generated/sklearn.feature_selection.mutual_info_regression.html#sklearn.feature_selection.mutual_info_regression
should we do the same?
are there any better options?

[solegalli]

why mutual information? I thought MRMR was based on f score and correlation.

[MetroCat69]

No, it is more complicated than that.

1. In the paper he is based on, they have several variations of the mRMR algorithm [1]. Since I can't find how they trained the random forest classifier (which hyperparameters they used), and also as far as I understand, RDC is not numerically stable and not supported natively in SciPy/Scikit-learn, I suggest we either use just the FCD variant or the following variants: MID, MIQ, FCD, FCQ [2].

2. F statistics shouldn't be used for classification problems, and F tests can't be used for regression problems. See Wikipedia or, actually, they even say it in the Scikit-learn docs [3].

So, we either need to create two classes (similar to what Scikit-learn does), or we need to detect if the target variable is categorical or numerical. Or any other suggestions ?Ψ(￣∀￣)Ψ

4. Another thing I don't know is this: Should we automatically filter the method only for numerical variables using the find_numerical_variables method, or should we use the method on all features always?"

References:
[1] https://arxiv.org/pdf/1908.05376.pdf
[2] https://github.com/feature-engine/feature_engine/assets/78600473/ee484ed9-f372-46e1-9382-39493e28d0c6
[3] https://scikit-learn.org/stable/modules/feature_selection.html

[MetroCat69]

?

[solegalli]

I see, thank you for pointing that out.

For the transformers that can take classification and regression, we have an extra parameter in the init called regression which defaults to True, if I remember correctly. The user needs to enter the type of problem they want to solve. Then, if it is regression we'd use mutual_info_regression otherwise classif. I'd use sklearn functionality, and let them take care of continuous/discrete with those functions.

here you have an example of a transformer that takes regression or classification and all the checks that we do in the fit, plus the docstrings, plus the checks in the init: https://github.com/feature-engine/feature_engine/blob/main/feature_engine/discretisation/decision_tree.py