Version 1.5.X

Version 1.5.2

Deployed: 21th November 2022

Contributors

• Gleb Levitski

• Alfonso Tobar

• pxn39

• Soledad Galli

In this release, we expand the functionality of existing classes and the documentation.

New functionality

• The StringSimilarityEncoder can now create similarity variables based on keywords entered by the user (Gleb Levitski)

• The Winsorizer and OutlierTrimmer now automatically adjust the value of the fold parameter based on the capping_method (pxn39)

Bug fixes

• Type checks errors raised by newer versions (Gleb Levitski)

Documentation

• Add example code snippets to the categorical encoding API docs (Alfonso Tobar)

• Add example code snippets to the imputation module API docs (Alfonso Tobar)

• Add example code snippets to the discretisation module API docs (Alfonso Tobar)

• Add example code snippets to the creation module API docs (Alfonso Tobar)

• Add example code snippets to the datetime module API docs (Alfonso Tobar)

• Update the user guide docs for the forecasting feature transformers (Soledad Galli)

• Update the user guide docs for datetime features and cyclical features (Soledad Galli)

• Fix badges in README (Gleb Levitski)

Version 1.5.0

Deployed: 17th October 2022

Contributors

• Gleb Levitski

• David Cortes

• Alfonso Tobar

• Morgan Sell

• Soledad Galli

In this release, we fix a bug that made the get_feature_names_out not compatible with the Scikit-learn pipeline.

In addition, thanks to Gleb Levitski, we’ve got a new encoder to replace categories by string similarity variables. Gleb Levitski also made a number of code enhancements to various transformers across the library, making a lot of new functionality available.

Finally, we’d like to thank Alfonso Tobar, David Cortes and Morgan Sell for creating new transformers, fixing bugs and expanding the functionality of Feature-engine.

Thank you so much to all contributors and to those of you who created issues flagging bugs or requesting new functionality.

New transformers

• StringSimilarityEncoder: encodes categorical variables based on string similarity (Gleb Levitski)

• MatchCategories: matches the categories in train and test set when of type pandas categorical (David Cortes)

• SelectByInformationValue: selects features based on the information value (Morgan Sell and Soledad Galli)

New functionality

• The MeanEncoder can now implement smoothing during the encoding to handle high cardinality (Gleb Levitski)

• The MeanEncoder can now encode unseen categories (Gleb Levitski)

• The OrdinalEncoder can now encode unseen categories (Soledad Galli)

• The CountFrequencyEncoder can now encode unseen categories (David Cortes)

• All outlier transformers can now detect outliers based on the MAD rule (Gleb Levitski)

• Add automatic calculation of PSI threshold in DropHighPSIFeatures (Gleb Levitski)

• All feature selection transformers now have the method get_support() (Soledad Galli)

Bug fixes

• get_feature_names_out is now compatible with the Scikit-learn pipeline in all transformers (Soledad Galli)

• The inverse_transform method in encoders now correctly handles unseen categories or raises not implemented errors (Soledad Galli)

• Fixes output of SklearnTransformerWrapper for OneHotEncoder and PolynomialFeatures (Alfonso Tobar)

Documentation

• Add more resources to documentation (Soledad Galli)

• User guide for StringSimilarityEncoder (Gleb Levitski)

• New Jupyter notebook for StringSimilarityEncoder (Gleb Levitski)

• User guide for SelectByInformationValue (Morgan Sell and Soledad Galli)

Deprecations

• Parameter errors in encoders is now replaced by unseen (Soledad Galli)

• The classes MathematicalCombination, CombineWithFeatureReference and CyclicalTransformer are removed (Soledad Galli)

• We are deprecating PRatioEncoder in version 1.5 and it will be removed in version 1.6 (Soledad Galli)

Code improvements

• Adds code coverage test (Soledad Galli)

• Changes logic of encoding unseen categories to work with inverse_transform (Soledad Galli)

• Increases code coverage for encoders (Soledad Galli)

• Removes CategoricalInitExpandedMixin (Soledad Galli)

• Removes checks for encoding dictionaries in all encoders (Soledad Galli)

• Refactors creation module (Soledad Galli)

• Refactors docstring module (Soledad Galli)

• Refactors variable handling module (Soledad Galli)

• Refactors numerical dictionary checks (Soledad Galli)

• Refactors base transformers module (Soledad Galli)

• Makes dataframe checks more performant (Soledad Galli)

• Replaces pd.concat by pd.group in all target based encoders (Soledad Galli)