CHANGES.rst

orphan:

.. currentmodule:: dirty_cat

Release 0.4.1

Major changes

• :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>`

Minor changes

• Improvement of date column detection and date format inference in :class:`TableVectorizer`. The

format inference now finds a format which works for all non-missing values of the column, instead of relying on pandas behavior. If no such format exists, the column is not casted to a date column. :pr:`543` by :user:`Leo Grinsztajn <LeoGrin>`

Release 0.4.0

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 auxilliary 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>`

Release 0.3.0

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>`

Release 0.2.2

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>`

Release 0.2.1

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.

Release 0.2.0

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>`

Release 0.2.0a1

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>`

Release 0.1.1

Major changes

Bug-fixes

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

Release 0.1.0

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>`

Release 0.0.7

• 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.

Release 0.0.6

• 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.

Release 0.0.5

• 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.