Comments (4)
I mean 3 or 4 -- ie having a separate transformer for select. indeed it can do more than just subset columns
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I agree that it is something that would be useful, and to reply to your points :
I like 2. more than 1. because I think it makes sense to have different dropping strategies for different transformers. That being said, if they share a common, widely used drop case it would make sense to have this argument in TableVectorizer too. This way you would set up the dropping strategy for all of them at once
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It could possibly account for other strategies than dropna, for instance practitioners often drop 1) very sparse columns, or features that are present only for a small number of ids, 2) correlations (among features, between feature and target), 3) outliers
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I like the idea of verbs over nouns, but I would choose nouns to avoid a contrast with scikit-learn
wdyt ?
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@TheooJ I think Gaël comment is about removing columns based on user-defined lists. You'd need a transformer to perform feature selection.
Maybe we could combine 1. and 4. : having a drop
parameter on the TableVectorizer
and allowing renaming or simple column manipulation operations in kwargs. In addition, we could also introduce the ColSelector
for usage out of TableVectorizer
.
Let's assume a slightly different identity from scikit-learn with verbs rather than nouns ;)
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I think I prefer option 3, "add a Drop transformer". The vectorizer has quite a few parameters already and I believe a slightly longer pipeline with simpler steps is easier to understand than a pipeline where some steps do a lot of things. Also, I'm not sure but there could be situations where a user wants control over where the drop happens, eg to drop a lot of columns as soon as possible to save memory, or to use a column for a join and drop it afterwards for prediction
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Related Issues (20)
- One of the hyperlinks in the examples section of the Documentation not working HOT 1
- Add array-to-dummies preprocessor HOT 2
- FEAT enable pandas output in `TableVectorizer` as a parameter HOT 5
- Example 07_multiple_key_join takes a lot of time
- FEAT Use `pandas.merge` in `fuzzy_join` when `matching_score=1` HOT 1
- FEAT Develop the `AggJoiner` and `AggTarget` HOT 8
- Interactive examples for skrub HOT 4
- BUG example 03 breaks with `exact_until='microsecond'`
- FEAT Getting only the time since epoch from the DateTimeEncoder
- BUG DateTimeEncoder fails when `extract_until` is "year" or "month" HOT 2
- BUG DateTimeEncoder fails when a column mixes formats HOT 3
- DOC visual inconsistencies with the last version of the PyData Theme
- Adding a transformer to sessionize a table HOT 5
- Jupyterlite kernel fails to launch HOT 4
- DOC Remove file-level flake8 noqa HOT 1
- Use `Joiner` in `fuzzy_join` rather than the opposite
- `match_score` used in example 4 is too low to reject any matches
- Joiner's match score threshold varies with each call to transform HOT 1
- fuzzy_join's match_score starts at 0.5 not 0.0 HOT 4
- division by 0 in fuzzy_join when there are only perfect matches HOT 4
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