Skrub

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

Version 0.2.0a1 is a pre-release.
To try it, you have to install it manually using::

pip install --pre dirty_cat==0.2.0a1

or from the GitHub repository::

pip install git+https://github.com/dirty-cat/dirty_cat.git

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

* Bump minimum dependencies:

- Python (>= 3.6)
- NumPy (>= 1.16)
- SciPy (>= 1.2)
- scikit-learn (>= 0.20.0)

* :class:`TableVectorizer`: Added automatic transform through the
:class:`TableVectorizer` class. It transforms
columns automatically based on their type. It provides a replacement
for scikit-learn's :class:`~sklearn.compose.ColumnTransformer` simpler to use on heterogeneous
pandas DataFrame. :pr:`167` by :user:`Lilian Boulard <LilianBoulard>`

* **Backward incompatible change to** :class:`GapEncoder`: The :class:`GapEncoder` now only
supports two-dimensional inputs of shape (n_samples, n_features).
Internally, features are encoded by independent :class:`GapEncoder` models,
and are then concatenated into a single matrix.
:pr:`185` by :user:`Lilian Boulard <LilianBoulard>` and :user:`Alexis Cvetkov <alexis-cvetkov>`.


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

* Fix `get_feature_names` for scikit-learn > 0.21. :pr:`216` by :user:`Alexis Cvetkov <alexis-cvetkov>`

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

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

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

* RuntimeWarnings due to overflow in :class:`GapEncoder`. :pr:`161` by :user:`Alexis Cvetkov <alexis-cvetkov>`

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

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

* :class:`GapEncoder`: Added online Gamma-Poisson factorization through the
:class:`GapEncoder` class. This method discovers latent categories formed
via combinations of substrings, and encodes string data as combinations of
these categories. To be used if interpretability is important. :pr:`153` by :user:`Alexis Cvetkov <alexis-cvetkov>`

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

* Multiprocessing exception in notebook. :pr:`154` by :user:`Lilian Boulard <LilianBoulard>`

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

* **MinHashEncoder**: Added ``minhash_encoder.py`` and ``fast_hast.py`` files
that implement minhash encoding through the :class:`MinHashEncoder` class.
This method allows for fast and scalable encoding of string categorical
variables.

* **datasets.fetch_employee_salaries**: change the origin of download for employee_salaries.

- The function now return a bunch with a dataframe under the field "data",
and not the path to the csv file.
- The field "description" has been renamed to "DESCR".

* **SimilarityEncoder**: Fixed a bug when using the Jaro-Winkler distance as a
similarity metric. Our implementation now accurately reproduces the behaviour
of the ``python-Levenshtein`` implementation.

* **SimilarityEncoder**: Added a `handle_missing` attribute to allow encoding
with missing values.

* **TargetEncoder**: Added a `handle_missing` attribute to allow encoding
with missing values.

* **MinHashEncoder**: Added a `handle_missing` attribute to allow encoding
with missing values.

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

* **SimilarityEncoder**: Accelerate ``SimilarityEncoder.transform``, by:

- computing the vocabulary count vectors in ``fit`` instead of ``transform``
- computing the similarities in parallel using ``joblib``. This option can be
turned on/off via the ``n_jobs`` attribute of the :class:`SimilarityEncoder`.

* **SimilarityEncoder**: Fix a bug that was preventing a :class:`SimilarityEncoder`
to be created when ``categories`` was a list.

* **SimilarityEncoder**: Set the dtype passed to the ngram similarity
to float32, which reduces memory consumption during encoding.

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

* **SimilarityEncoder**: Change the default ngram range to (2, 4) which
performs better empirically.

* **SimilarityEncoder**: Added a `most_frequent` strategy to define
prototype categories for large-scale learning.

* **SimilarityEncoder**: Added a `k-means` strategy to define prototype
categories for large-scale learning.

* **SimilarityEncoder**: Added the possibility to use hashing ngrams for
stateless fitting with the ngram similarity.

* **SimilarityEncoder**: Performance improvements in the ngram similarity.

* **SimilarityEncoder**: Expose a `get_feature_names` method.