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Feature Engine

Feature Engine is a python library that contains several transformers to engineer features for use in machine learning models. The transformers follow scikit-learn like functionality. They first learn the imputing or encoding methods from the training set, and subsequently transform the dataset. Currently the transformers include functionality for:

  • Missing value imputation
  • Categorical variable encoding
  • Outlier removal
  • Discretisation
  • Numerical Variable Transformation

Important Links

Documentation: http://feature-engine.readthedocs.io

Imputing Methods

  • MeanMedianImputer
  • RandomSampleImputer
  • EndTailImputer
  • AddNaNBinaryImputer
  • CategoricalVariableImputer
  • FrequentCategoryImputer
  • ArbitraryNumberImputer

Encoding Methods

  • CountFrequencyCategoricalEncoder
  • OrdinalCategoricalEncoder
  • MeanCategoricalEncoder
  • WoERatioCategoricalEncoder
  • OneHotCategoricalEncoder
  • RareLabelCategoricalEncoder

Outlier Handling methods

  • Windsorizer
  • ArbitraryOutlierCapper

Discretisation methods

  • EqualFrequencyDiscretiser
  • EqualWidthDiscretiser
  • DecisionTreeDiscretiser

Variable Transformation methods

  • LogTransformer
  • ReciprocalTransformer
  • ExponentialTransformer
  • BoxCoxTransformer

Installing

pip install feature_engine

or

git clone https://github.com/solegalli/feature_engine.git

Usage

from feature_engine.categorical_encoders import RareLabelEncoder

rare_encoder = RareLabelEncoder(tol = 0.05, n_categories=5)
rare_encoder.fit(data, variables = ['Cabin', 'Age'])
data_encoded = rare_encoder.transform(data)

See more usage examples in the jupyter notebooks in the example section

Examples

You can find jupyter notebooks in the examples folder, with directions on how to use this package and its multiple transformers.

License

BSD 3-Clause

Authors

References

Most of the engineering and encoding functionality is inspired by this series of articles from the 2009 KDD competition

To learn more about the rationale, functionality, pros and cos of each imputer, encoder and transformer, refer to the Feature Engineering Online Course

For a summary of the methods check this presentation