Changelog

Version 0.4.3

  • Deployed: Friday, May 15, 2020

  • Contributors: Soledad Galli, Christopher Samiullah

Major Changes:
  • New Transformer: the ‘SklearnTransformerWrapper` allows you to use most Scikit-learn transformers just on a subset of features. Works with the SimpleImputer, the OrdinalEncoder and most scalers.

Minor Changes:
  • Added functionality: the ‘EqualFrequencyDiscretiser` and EqualWidthDiscretiser now have the ability to return interval boundaries as well as integers, to identify the bins. To return boundareis set the parameter return_boundaries=True.

  • Improved docs: added contibuting section, where you can find information on how to participate in the development of Feature-engine’s code base, and more.

Version 0.4.0

  • Deployed: Monday, April 04, 2020

  • Contributors: Soledad Galli, Christopher Samiullah

Major Changes:
  • Deprecated: the FrequentCategoryImputer was integrated into the class CategoricalVariableImputer. To perform frequent category imputation now use: CategoricalVariableImputer(imputation_method='frequent')

  • Renamed: the AddNaNBinaryImputer is now called AddMissingIndicator.

  • New: the OutlierTrimmer was introduced into the package and allows you to remove outliers from the dataset

Minor Changes:
  • Improved: the EndTailImputer now has the additional option to place outliers at a factor of the maximum value.

  • Improved: the FrequentCategoryImputer has now the functionality to return numerical variables cast as object, in case you want to operate with them as if they were categorical. Set return_object=True.

  • Improved: the RareLabelEncoder now allows the user to define the name for the label that will replace rare categories.

  • Improved: All feature engine transformers (except missing data imputers) check that the data sets do not contain missing values.

  • Improved: the LogTransformer will raise an error if a variable has zero or negative values.

  • Improved: the ReciprocalTransformer now works with variables of type integer.

  • Improved: the ReciprocalTransformer will raise an error if the variable contains the value zero.

  • Improved: the BoxCoxTransformer will raise an error if the variable contains negative values.

  • Improved: the OutlierCapper now finds and removes outliers based of percentiles.

  • Improved: Feature-engine is now compatible with latest releases of Pandas and Scikit-learn.

Version 0.3.0

  • Deployed: Monday, August 05, 2019

  • Contributors: Soledad Galli.

Major Changes:
  • New: the RandomSampleImputer now has the option to set one seed for batch imputation or set a seed observation per observations based on 1 or more additional numerical variables for that observation, which can be combined with multiplication or addition.

  • New: the YeoJohnsonTransfomer has been included to perform Yeo-Johnson transformation of numerical variables.

  • Renamed: the ExponentialTransformer is now called PowerTransformer.

  • Improved: the DecisionTreeDiscretiser now allows to provide a grid of parameters to tune the decision trees which is done with a GridSearchCV under the hood.

  • New: Extended documentation for all Feature-engine’s transformers.

  • New: Quickstart guide to jump on straight onto how to use Feature-engine.

  • New: Changelog to track what is new in Feature-engine.

  • Updated: new Jupyter notebooks with examples on how to use Feature-engine’s transformers.

Minor Changes:
  • Unified: dictionary attributes in transformers, which contain the transformation mappings, now end with _, for example binner_dict_.