Random subsampling for feature selection #47 #650
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Hello @lareinaxy! Thanks for updating this PR. We checked the lines you've touched for PEP 8 issues, and found: There are currently no PEP 8 issues detected in this Pull Request. Cheers! 🍻 Comment last updated at 2019-12-21 22:28:28 UTC |
rasbt
requested changes
Dec 22, 2019
| @@ -382,7 +382,8 @@ def fit(self, X, y, custom_feature_names=None, groups=None, **fit_params): | |||
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| else: | |||
| select_in_range = False | |||
| k_to_select = self.k_features | |||
| k_to_select = int(len(X[1])**.5) | |||
| np.take(X, np.random.permutation(X.shape[1]), axis=1, out=X) | |||
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Hi Lareina, thanks for the PR. I don't quite understand how the value is chosen k_to_select = int(len(X[1])**.5). So this means that the random subset is always sqrt of the original number of features?
There are two problems with that.
- We want to add the random feature subset size as an optional thing
- I think we should let the user allow to choose the subset size
There could be a new parameter
use_random_feature_subset for the SequentialFeatureSelector class which either accepts a function like f = lambda x: int(np.sqrt(x) or None.
Let me know if you have questions.
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Description
Related issues or pull requests
Fixes #47
Pull Request Checklist
./docs/sources/CHANGELOG.mdfile (if applicable)./mlxtend/*/testsdirectories (if applicable)mlxtend/docs/sources/(if applicable)PYTHONPATH='.' pytest ./mlxtend -svand make sure that all unit tests pass (for small modifications, it might be sufficient to only run the specific test file, e.g.,PYTHONPATH='.' pytest ./mlxtend/classifier/tests/test_stacking_cv_classifier.py -sv)flake8 ./mlxtend