add categorical_feature like lightgbm

[rafmacalaba]

I don't know how to feature request XD.

It would be great if you can add the categorical_feature parameter in your Dataset just like in the lightgbm docs. Thanks!!

[aerdem4]

@rafmacalaba you can try setting categorical_feature in fit_params dictionary that you provide for LOFO. https://github.com/aerdem4/lofo-importance/blob/master/lofo/lofo_importance.py#L30

lofo_imp = LOFOImportance(dataset, cv=cv, scoring="roc_auc", fit_params={"categorical_feature": ["cat1", "cat2"]})

[rafmacalaba]

Ah!! make sense. I just figured it out just now, I added the categorical_feature parameter in my dict. Thanks :D btw, it would be good if you add that on your README.md :D Thank you so much Ahmet! Please resolve this.

[rafmacalaba]

Seems like it doesn't work :x: @aerdem4

~/anaconda3/lib/python3.7/site-packages/sklearn/model_selection/_validation.py in _fit_and_score(estimator, X, y, scorer, train, test, verbose, parameters, fit_params, return_train_score, return_parameters, return_n_test_samples, return_times, return_estimator, error_score)
    526             estimator.fit(X_train, **fit_params)
    527         else:
--> 528             estimator.fit(X_train, y_train, **fit_params)
    529 
    530     except Exception as e:

TypeError: fit() got an unexpected keyword argument 'objective'```

[rafmacalaba]

@aerdem4 is there a way I can download this specific version 6mos ago? https://www.kaggle.com/divrikwicky/lofo-importance

[aerdem4]

objective is not fit_params but model parameters, you need to set it while you create your lightgbm model.

If you still want to use an old version you can pip install lofo-importance==0.2.0

[rafmacalaba]

@aerdem4 I made it work with some workarounds, I think I found the culprit of my error. specifically in infer_defaults.py I am using the dtype category instead of the object that the one you're using in the script. Might be good to add this in the file.

Thank you!

[aerdem4]

Can you paste your code that reproduces the error?

[rafmacalaba]

@aerdem4 it's just the classic Could not convert to float - string which any models would be angry to have, basically my categorical features are in category dtype not object which was in your infer_defaults.py script. I made a PR for this and just check if it's good to go :D Thanks!

[aerdem4]

Solved by #25