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:

Feature Engineering for Machine Learning, Online Course. Python Feature Engineering Cookbook

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

Variable Transformation methods

LogTransformer
ReciprocalTransformer
PowerTransformer
BoxCoxTransformer
YeoJohnsonTransformer

Installing

pip install feature_engine

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 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

Release files for feature-engine 0.4.2

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for feature-engine 0.4.2
File	Size	Uploaded
feature_engine-0.4.2.tar.gz	21.6 kB	May 2, 2020	Details

Built distribution (wheel)

Table of built distributions (wheels) for feature-engine 0.4.2
File	Interpreter	ABI	Platform	Reset
feature_engine-0.4.2-py2.py3-none-any.whl 26.7 kB May 2, 2020	Python 2, Python 3	none	any	Details

Total release size: 48.2 kB

Release files / feature_engine-0.4.2.tar.gz

Download URL	feature_engine-0.4.2.tar.gz
Size	21.6 kB
Tags	Source
SHA-256 checksum How to use checksums	`fc7ba891e33f4bd9726470bd8d847c14c96d6024e8be8acaeb51389d08621452`
BLAKE2b-256 checksum How to use checksums	`dbc78fa51c27324a38c1fe9a0b06f452931e0331913c6b01bc68ac412497f62c`
Upload date	May 2, 2020
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/3.1.1 pkginfo/1.5.0.1 requests/2.23.0 setuptools/40.6.2 requests-toolbelt/0.9.1 tqdm/4.45.0 CPython/3.7.2`

Release files / feature_engine-0.4.2-py2.py3-none-any.whl

Download URL	feature_engine-0.4.2-py2.py3-none-any.whl
Size	26.7 kB
Tags	Python 2 Python 3
SHA-256 checksum How to use checksums	`2dddac2c5cf92317dc38c296877548a65a7941ae1aeb7a81f8f707b62256111a`
BLAKE2b-256 checksum How to use checksums	`bf084d56c3cd33a87650882fb0247d761e1f40687b8f9b4c11cbc431ff6f43c4`
Upload date	May 2, 2020
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/3.1.1 pkginfo/1.5.0.1 requests/2.23.0 setuptools/40.6.2 requests-toolbelt/0.9.1 tqdm/4.45.0 CPython/3.7.2`