8000 ENH Adds encoded_missing_value to OrdinalEncoder by thomasjpfan · Pull Request #21988 · scikit-learn/scikit-learn · GitHub
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ENH Adds encoded_missing_value to OrdinalEncoder #21988

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Merged
merged 9 commits into from
Mar 23, 2022
Merged

ENH Adds encoded_missing_value to OrdinalEncoder #21988

merged 9 commits into from
Mar 23, 2022

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thomasjpfan
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Reference Issues/PRs

Related to #21967

What does this implement/fix? Explain your changes.

This PR adds encoded_missing_value to OrdinalEncoder. Similar to unknown_value, encoded_missing_value is not allowed to be one of the encoding used by a known category.

CC @glemaitre

ogrisel
ogrisel approved these changes Dec 16, 2021
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Just a quick suggestion for a usability improvement in the error message both other than that, LGTM!

@ogrisel ogrisel mentioned this pull request Dec 16, 2021
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@glemaitre glemaitre self-requested a review December 23, 2021 10:55
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Maybe a short example in the docstring:

    By default, :class:`OrdinalEncoder` is lenient towards missing values by
    propagating them.

    >>> import numpy as np
    >>> X = [['Male', 1], ['Female', 3], ['Female', np.nan]]
    >>> enc.fit_transform(X)
    array([[ 1.,  0.],
           [ 0.,  1.],
           [ 0., nan]])

    You can use the parameter `encoded_missing_value` to encode missing values.

    >>> enc.set_params(encoded_missing_value=-1).fit_transform(X)
    array([[ 1.,  0.],
           [ 0.,  1.],
           [ 0., -1.]])

@glemaitre
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In the User Guide l.540 of preprocessing.rst, I think that we can add/modify with:

By default, :class:`OrdinalEncoder` will also passthrough missing values that are
indicated by `np.nan`.

    >>> enc = preprocessing.OrdinalEncoder()
    >>> X = [['male'], ['female'], [np.nan], ['female']]
    >>> enc.fit_transform(X)
    array([[ 1.],
           [ 0.],
           [nan],
           [ 0.]])

:class:`OrdinalEncoder` provides a parameter `encoded_missing_value` to encode
the missing values without the need to create a pipeline and using
:class:`~sklearn.impute.SimpleImputer`.

    >>> enc = preprocessing.OrdinalEncoder(encoded_missing_value=-1)
    >>> X = [['male'], ['female'], [np.nan], ['female']]
    >>> enc.fit_transform(X)
    array([[ 1.],
           [ 0.],
           [-1.],
           [ 0.]])

The above processing is equivalent to the following pipeline::

    >>> from sklearn.pipeline import Pipeline
    >>> from sklearn.impute import SimpleImputer
    >>> enc = Pipeline(steps=[
    ...     ("encoder", preprocessing.OrdinalEncoder()),
    ...     ("imputer", SimpleImputer(strategy="constant", fill_value=-1)),
    ... ])
    >>> enc.fit_transform(X)
    array([[ 1.],
           [ 0.],
           [-1.],
           [ 0.]])

@thomasjpfan thomasjpfan added this to the 1.1 milestone Mar 3, 2022
jeremiedbb
jeremiedbb approved these changes Mar 23, 2022
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LGTM

@jeremiedbb jeremiedbb merged commit 51fddb7 into scikit-learn:main Mar 23, 2022
glemaitre pushed a commit to glemaitre/scikit-learn that referenced this pull request Apr 6, 2022
@thomasjpfan @glemaitre
ENH Adds encoded_missing_value to OrdinalEncoder (scikit-learn#21988)
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This was referenced Nov 15, 2022
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@thomasjpfan thomasjpfan mentioned this pull request Feb 23, 2023
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@ogrisel ogrisel ogrisel approved these changes

@jeremiedbb jeremiedbb jeremiedbb approved these changes

@glemaitre glemaitre Awaiting requested review from glemaitre

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