Feature Engine

Python 3.6 Python 3.7 Python 3.8 License CircleCI Documentation Status

Feature-engine is a Python library with multiple transformers to engineer features for use in machine learning models. Feature-engine's transformers follow Scikit-learn functionality with fit() and transform() methods to first learn the transforming paramenters from data and then transform the data.

Feature-engine features in the following resources:

Blogs about Feature-engine:

Documentation

Documentation: http://feature-engine.readthedocs.io
Home page: https://www.trainindata.com/feature-engine

Current Feature-engine's transformers include functionality for:

Missing data imputation
Categorical variable encoding
Outlier removal
Discretisation
Numerical Variable Transformation

Imputing Methods

MeanMedianImputer
RandomSampleImputer
EndTailImputer
AddNaNBinaryImputer
CategoricalVariableImputer
FrequentCategoryImputer
ArbitraryNumberImputer

Encoding Methods

CountFrequencyCategoricalEncoder
OrdinalCategoricalEncoder
MeanCategoricalEncoder
WoERatioCategoricalEncoder
OneHotCategoricalEncoder
RareLabelCategoricalEncoder

Outlier Handling methods

Winsorizer
ArbitraryOutlierCapper
OutlierTrimmer

Discretisation methods

EqualFrequencyDiscretiser
EqualWidthDiscretiser
DecisionTreeDiscretiser
UserInputDiscreriser

Variable Transformation methods

LogTransformer
ReciprocalTransformer
PowerTransformer
BoxCoxTransformer
YeoJohnsonTransformer

Scikit-learn Wrapper:

SklearnTransformerWrapper

Installing

pip install feature_engine

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

Usage

>>> from feature_engine.categorical_encoders import RareLabelCategoricalEncoder
>>> import pandas as pd

>>> data = {'var_A': ['A'] * 10 + ['B'] * 10 + ['C'] * 2 + ['D'] * 1}
>>> data = pd.DataFrame(data)
>>> data['var_A'].value_counts()

Out[1]:
A    10
B    10
C     2
D     1
Name: var_A, dtype: int64

>>> rare_encoder = RareLabelCategoricalEncoder(tol=0.10, n_categories=3)
>>> data_encoded = rare_encoder.fit_transform(data)
>>> data_encoded['var_A'].value_counts()

Out[2]:
A       10
B       10
Rare     3
Name: var_A, dtype: int64

See more usage examples in the jupyter notebooks in the example folder of this repository, or in the documentation: http://feature-engine.readthedocs.io

Contributing

Local Setup Steps

Clone the repo and cd into it
Run pip install tox
Run tox if the tests pass, your local setup is complete

Opening Pull Requests

PR's are welcome! Please make sure the CI tests pass on your branch.

License

BSD 3-Clause

Authors

Soledad Galli - Initial work - Feature Engineering for Machine Learning, Online Course.

References

Many 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 for Machine Learning, Online Course

For a summary of the methods check this presentation and this article

To stay alert of latest releases, sign up at trainindata

Metadata

Release files for feature-engine 0.5.2

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