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Common superclass for transformers #61

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lars-reimann opened this issue Mar 22, 2023 · 2 comments · Fixed by #108
Closed

Common superclass for transformers #61

lars-reimann opened this issue Mar 22, 2023 · 2 comments · Fixed by #108
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cleanup 🧹 Refactorings and other tasks that improve the code released Included in a release

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@lars-reimann
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lars-reimann commented Mar 22, 2023

Is your feature request related to a problem?

Transformers currently contain a lot of copied & pasted documentation. We also don't have a type to express that we expect/return any transformer.

Desired solution

A common superclass (abstract base class) for transformers:

  • TableTransformer (fit, transform, fit_transform)
  • InvertibleTableTransformer (adds an inverse_transform method)

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@lars-reimann lars-reimann added the enhancement 💡 New feature or request label Mar 22, 2023
@github-project-automation github-project-automation bot moved this to Backlog in Library Mar 22, 2023
@lars-reimann lars-reimann added cleanup 🧹 Refactorings and other tasks that improve the code and removed enhancement 💡 New feature or request labels Mar 23, 2023
@lars-reimann lars-reimann self-assigned this Mar 24, 2023
@lars-reimann lars-reimann moved this from Backlog to Todo in Library Mar 24, 2023
@lars-reimann lars-reimann removed their assignment Mar 24, 2023
@lars-reimann lars-reimann moved this from Todo to Backlog in Library Mar 24, 2023
@lars-reimann
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The fit methods of the different existing transformers currently don't have a common interface. Some can be applied to only one column, some can only be applied to all columns, some can be applied to a list of columns. This must be normalized first.

@lars-reimann lars-reimann self-assigned this Mar 27, 2023
@lars-reimann lars-reimann moved this from Backlog to Todo in Library Mar 27, 2023
lars-reimann added a commit that referenced this issue Mar 28, 2023
### Summary of Changes

The `OrdinalEncoder` was a bit of an outlier compared to the other
`Transformer` classes:

* It could only be applied to a single column instead of a list of
columns. Because of this, it was not possible to implement #61.
* Nothing was "learned" since the user had to specify the value order
explicitly. The `fit` step was completely unnecessary.

Therefore, I've removed the class `OrdinalEncoder`. Instead the
`transform_column` method on a `Table` can be used. If eventually find
this to be too cumbersome, we can implement a new method
`transform_column_into_ordered_labels` on `Table`.

---------

Co-authored-by: lars-reimann <lars-reimann@users.noreply.github.com>
@lars-reimann lars-reimann moved this from Todo to In Progress in Library Mar 28, 2023
lars-reimann added a commit that referenced this issue Mar 28, 2023
Closes #61.
Closes #90.

### Summary of Changes

* Common superclasses `TableTransformer` and
`InvertibleTableTransformer`
* Common interface for `fit`, `transform`, `fit_transform`,
`inverse_transform`
* Return new transformer when calling `fit`
* More thorough tests

---------

Co-authored-by: lars-reimann <lars-reimann@users.noreply.github.com>
@github-project-automation github-project-automation bot moved this from In Progress to ✔️ Done in Library Mar 28, 2023
lars-reimann pushed a commit that referenced this issue Mar 29, 2023
## [0.7.0](v0.6.0...v0.7.0) (2023-03-29)

### Features

* `sort_rows` of a `Table` ([#104](#104)) ([20aaf5e](20aaf5e)), closes [#14](#14)
* add `_file` suffix to methods interacting with files ([#103](#103)) ([ec011e4](ec011e4))
* improve transformers for tabular data ([#108](#108)) ([b18a06d](b18a06d)), closes [#61](#61) [#90](#90)
* remove `OrdinalEncoder` ([#107](#107)) ([b92bba5](b92bba5)), closes [#61](#61)
* specify features and target when creating a `TaggedTable` ([#114](#114)) ([95e1fc7](95e1fc7)), closes [#27](#27)
* swap `name` and `data` parameters of `Column` ([#105](#105)) ([c2f8da5](c2f8da5))
@lars-reimann
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🎉 This issue has been resolved in version 0.7.0 🎉

The release is available on:

Your semantic-release bot 📦🚀

@lars-reimann lars-reimann added the released Included in a release label Mar 29, 2023
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