Skrub

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* **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.

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* **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.

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* **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.