Skrub

==========================

Major changes
-------------
* :func:`fuzzy_join` and :class:`FeatureAugmenter` can now join on numerical columns based on the euclidean distance.
:pr:`530` by :user:`Jovan Stojanovic <jovan-stojanovic>`

* :func:`fuzzy_join` and :class:`FeatureAugmenter` can perform many-to-many joins on lists of numerical or string key columns.
:pr:`530` by :user:`Jovan Stojanovic <jovan-stojanovic>`

* :func:`GapEncoder.transform` will not continue fitting of the instance anymore.
It makes functions that depend on it (:func:`~GapEncoder.get_feature_names_out`,
:func:`~GapEncoder.score`, etc.) deterministic once fitted.
:pr:`548` by :user:`Lilian Boulard <LilianBoulard>`

* :func:`fuzzy_join` and :class:`FeatureAugmenter` now perform joins on missing values as in `pandas.merge`
but raises a warning. :pr:`522` and :pr:`529` by :user:`Jovan Stojanovic <jovan-stojanovic>`

* Added :func:`get_ken_table_aliases` and :func:`get_ken_types` for exploring
KEN embeddings. :pr:`539` by :user:`Lilian Boulard <LilianBoulard>`.


Minor changes
-------------
* Improvement of date column detection and date format inference in :class:`TableVectorizer`. The
format inference now tries to find a format which works for all non-missing values of the column, and only
tries pandas default inference if it fails.
:pr:`543` by :user:`Leo Grinsztajn <LeoGrin>`
:pr:`587` by :user:`Leo Grinsztajn <LeoGrin>`

=========================

Major changes
-------------
* `SuperVectorizer` is renamed as :class:`TableVectorizer`, a warning is raised when using the old name.
:pr:`484` by :user:`Jovan Stojanovic <jovan-stojanovic>`

* New experimental feature: joining tables using :func:`fuzzy_join` by approximate key matching. Matches are based
on string similarities and the nearest neighbors matches are found for each category.
:pr:`291` by :user:`Jovan Stojanovic <jovan-stojanovic>` and :user:`Leo Grinsztajn <LeoGrin>`

* New experimental feature: :class:`FeatureAugmenter`, a transformer
that augments with :func:`fuzzy_join` the number of features in a main table by using information from auxiliary tables.
:pr:`409` by :user:`Jovan Stojanovic <jovan-stojanovic>`

* Unnecessary API has been made private: everything (files, functions, classes)
starting with an underscore shouldn't be imported in your code. :pr:`331` by :user:`Lilian Boulard <LilianBoulard>`

* The :class:`MinHashEncoder` now supports a `n_jobs` parameter to parallelize
the hashes computation. :pr:`267` by :user:`Leo Grinsztajn <LeoGrin>` and :user:`Lilian Boulard <LilianBoulard>`.

* New experimental feature: deduplicating misspelled categories using :func:`deduplicate` by clustering string distances.
This function works best when there are significantly more duplicates than underlying categories.
:pr:`339` by :user:`Moritz Boos <mjboos>`.

Minor changes
-------------
* Add example `Wikipedia embeddings to enrich the data`. :pr:`487` by :user:`Jovan Stojanovic <jovan-stojanovic>`

* **datasets.fetching**: contains a new function :func:`get_ken_embeddings` that can be used to download Wikipedia
embeddings and filter them by type.

* **datasets.fetching**: contains a new function :func:`fetch_world_bank_indicator` that can be used to download indicators
from the World Bank Open Data platform.
:pr:`291` by :user:`Jovan Stojanovic <jovan-stojanovic>`

* Removed example `Fitting scalable, non-linear models on data with dirty categories`. :pr:`386` by :user:`Jovan Stojanovic <jovan-stojanovic>`

* :class:`MinHashEncoder`'s :func:`minhash` method is no longer public. :pr:`379` by :user:`Jovan Stojanovic <jovan-stojanovic>`

* Fetching functions now have an additional argument ``directory``,
which can be used to specify where to save and load from datasets.
:pr:`432` by :user:`Lilian Boulard <LilianBoulard>`

* Fetching functions now have an additional argument ``directory``,
which can be used to specify where to save and load from datasets.
:pr:`432` and :pr:`453` by :user:`Lilian Boulard <LilianBoulard>`

* The :class:`TableVectorizer`'s default `OneHotEncoder` for low cardinality categorical variables now defaults
to `handle_unknown="ignore"` instead of `handle_unknown="error"` (for sklearn >= 1.0.0).
This means that categories seen only at test time will be encoded by a vector of zeroes instead of raising an error. :pr:`473` by :user:`Leo Grinsztajn <LeoGrin>`

Bug fixes
---------

* The :class:`MinHashEncoder` now considers `None` and empty strings as missing values, rather
than raising an error. :pr:`378` by :user:`Gael Varoquaux <GaelVaroquaux>`

==========================

Major changes
-------------

* New encoder: :class:`DatetimeEncoder` can transform a datetime column into several numerical columns
(year, month, day, hour, minute, second, ...). It is now the default transformer used
in the :class:`TableVectorizer` for datetime columns. :pr:`239` by :user:`Leo Grinsztajn <LeoGrin>`

* The :class:`TableVectorizer` has seen some major improvements and bug fixes:

- Fixes the automatic casting logic in ``transform``.
- To avoid dimensionality explosion when a feature has two unique values, the default encoder (:class:`~sklearn.preprocessing.OneHotEncoder`) now drops one of the two vectors (see parameter `drop="if_binary"`).
- ``fit_transform`` and ``transform`` can now return unencoded features, like the :class:`~sklearn.compose.ColumnTransformer`'s behavior. Previously, a ``RuntimeError`` was raised.

:pr:`300` by :user:`Lilian Boulard <LilianBoulard>`

* **Backward-incompatible change in the TableVectorizer**:
To apply ``remainder`` to features (with the ``*_transformer`` parameters),
the value ``'remainder'`` must be passed, instead of ``None`` in previous versions.
``None`` now indicates that we want to use the default transformer. :pr:`303` by :user:`Lilian Boulard <LilianBoulard>`

* Support for Python 3.6 and 3.7 has been dropped. Python >= 3.8 is now required. :pr:`289` by :user:`Lilian Boulard <LilianBoulard>`

* Bumped minimum dependencies:

- scikit-learn>=0.23
- scipy>=1.4.0
- numpy>=1.17.3
- pandas>=1.2.0 :pr:`299` and :pr:`300` by :user:`Lilian Boulard <LilianBoulard>`

* Dropped support for Jaro, Jaro-Winkler and Levenshtein distances.

- The :class:`SimilarityEncoder` now exclusively uses ``ngram`` for similarities,
and the `similarity` parameter is deprecated. It will be removed in 0.5. :pr:`282` by :user:`Lilian Boulard <LilianBoulard>`

Notes
-----

* The ``transformers_`` attribute of the :class:`TableVectorizer` now contains column
names instead of column indices for the "remainder" columns. :pr:`266` by :user:`Leo Grinsztajn <LeoGrin>`

=========================

Bug fixes
---------

* Fixed a bug in the :class:`TableVectorizer` causing a :class:`FutureWarning`
when using the :func:`get_feature_names_out` method. :pr:`262` by :user:`Lilian Boulard <LilianBoulard>`

==========================

Major changes
-------------

* Improvements to the :class:`TableVectorizer`

- Type detection works better: handles dates, numerics columns encoded as strings, or numeric columns containing strings for missing values.

:pr:`238` by :user:`Leo Grinsztajn <LeoGrin>`

* :func:`get_feature_names` becomes :func:`get_feature_names_out`, following changes in the scikit-learn API.
:func:`get_feature_names` is deprecated in scikit-learn > 1.0. :pr:`241` by :user:`Gael Varoquaux <GaelVaroquaux>`

* Improvements to the :class:`MinHashEncoder`
- It is now possible to fit multiple columns simultaneously with the :class:`MinHashEncoder`.
Very useful when using for instance the :func:`~sklearn.compose.make_column_transformer` function,
on multiple columns.

:pr:`243` by :user:`Jovan Stojanovic <jovan-stojanovic>`


Bug-fixes
---------

* Fixed a bug that resulted in the :class:`GapEncoder` ignoring the analyzer argument. :pr:`242` by :user:`Jovan Stojanovic <jovan-stojanovic>`

* :class:`GapEncoder`'s `get_feature_names_out` now accepts all iterators, not just lists. :pr:`255` by :user:`Lilian Boulard <LilianBoulard>`

* Fixed :class:`DeprecationWarning` raised by the usage of `distutils.version.LooseVersion`. :pr:`261` by :user:`Lilian Boulard <LilianBoulard>`

Notes
-----

* Remove trailing imports in the :class:`MinHashEncoder`.

* Fix typos and update links for website.

* Documentation of the :class:`TableVectorizer` and the :class:`SimilarityEncoder` improved.

=========================

Also see pre-release 0.2.0a1 below for additional changes.

Major changes
-------------

* Bump minimum dependencies:

- scikit-learn (>=0.21.0) :pr:`202` by :user:`Lilian Boulard <LilianBoulard>`
- pandas (>=1.1.5) **! NEW REQUIREMENT !** :pr:`155` by :user:`Lilian Boulard <LilianBoulard>`

* **datasets.fetching** - backward-incompatible changes to the example
datasets fetchers:

- The backend has changed: we now exclusively fetch the datasets from OpenML.
End users should not see any difference regarding this.
- The frontend, however, changed a little: the fetching functions stay the same
but their return values were modified in favor of a more Pythonic interface.
Refer to the docstrings of functions `dirty_cat.datasets.fetch_*`
for more information.
- The example notebooks were updated to reflect these changes. :pr:`155` by :user:`Lilian Boulard <LilianBoulard>`

* **Backward incompatible change to** :class:`MinHashEncoder`: The :class:`MinHashEncoder` now
only supports two dimensional inputs of shape (N_samples, 1).
:pr:`185` by :user:`Lilian Boulard <LilianBoulard>` and :user:`Alexis Cvetkov <alexis-cvetkov>`.

* Update `handle_missing` parameters:

- :class:`GapEncoder`: the default value "zero_impute" becomes "empty_impute" (see doc).
- :class:`MinHashEncoder`: the default value "" becomes "zero_impute" (see doc).

:pr:`210` by :user:`Alexis Cvetkov <alexis-cvetkov>`.

* Add a method "get_feature_names_out" for the :class:`GapEncoder` and the :class:`TableVectorizer`,
since `get_feature_names` will be depreciated in scikit-learn 1.2. :pr:`216` by :user:`Alexis Cvetkov <alexis-cvetkov>`

Notes
-----

* Removed hard-coded CSV file `dirty_cat/data/FiveThirtyEight_Midwest_Survey.csv`.


* Improvements to the :class:`TableVectorizer`

- Missing values are not systematically imputed anymore
- Type casting and per-column imputation are now learnt during fitting
- Several bugfixes

:pr:`201` by :user:`Lilian Boulard <LilianBoulard>`