Feature-engine

Package
Meta
Documentation
Testing

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

Feature-engine features in the following resources

• Feature Engineering for Machine Learning, Online Course

• Feature Selection for Machine Learning, Online Course

• Feature Engineering for Time Series Forecasting, Online Course

• Forecasting with Machine Learning, Online Course

• Python Feature Engineering Cookbook

• Feature Selection in Machine Learning Book

Blogs about Feature-engine

• Feature-engine: A new open-source Python package for feature engineering

• Practical Code Implementations of Feature Engineering for Machine Learning with Python

Documentation

• Documentation

Pst! How did you find us?

We want to share Feature-engine with more people. It'd help us loads if you tell us how you discovered us.

Then we'd know what we are doing right and which channels to use to share the love.

Please share your story by answering 1 quick question at this link . 😃

Current Feature-engine's transformers include functionality for:

• Missing Data Imputation
• Categorical Encoding
• Discretisation
• Outlier Capping or Removal
• Variable Transformation
• Variable Creation
• Variable Selection
• Datetime Features
• Time Series
• Preprocessing
• Scikit-learn Wrappers

Imputation Methods

• MeanMedianImputer
• ArbitraryNumberImputer
• RandomSampleImputer
• EndTailImputer
• CategoricalImputer
• AddMissingIndicator
• DropMissingData

Encoding Methods

• OneHotEncoder
• OrdinalEncoder
• CountFrequencyEncoder
• MeanEncoder
• WoEEncoder
• RareLabelEncoder
• DecisionTreeEncoder
• StringSimilarityEncoder

Discretisation methods

• EqualFrequencyDiscretiser
• EqualWidthDiscretiser
• GeometricWidthDiscretiser
• DecisionTreeDiscretiser
• ArbitraryDiscreriser

Outlier Handling methods

• Winsorizer
• ArbitraryOutlierCapper
• OutlierTrimmer

Variable Transformation methods

• LogTransformer
• LogCpTransformer
• ReciprocalTransformer
• ArcsinTransformer
• PowerTransformer
• BoxCoxTransformer
• YeoJohnsonTransformer

Variable Creation:

• MathFeatures
• RelativeFeatures
• CyclicalFeatures
• DecisionTreeFeatures()

Feature Selection:

• DropFeatures
• DropConstantFeatures
• DropDuplicateFeatures
• DropCorrelatedFeatures
• SmartCorrelationSelection
• ShuffleFeaturesSelector
• SelectBySingleFeaturePerformance
• SelectByTargetMeanPerformance
• RecursiveFeatureElimination
• RecursiveFeatureAddition
• DropHighPSIFeatures
• SelectByInformationValue
• ProbeFeatureSelection

Datetime

• DatetimeFeatures
• DatetimeSubtraction

Time Series

• LagFeatures
• WindowFeatures
• ExpandingWindowFeatures

Pipelines

• Pipeline
• make_pipeline

Preprocessing

• MatchCategories
• MatchVariables

Wrappers:

• SklearnTransformerWrapper

Installation

From PyPI using pip:

pip install feature_engine

From Anaconda:

conda install -c conda-forge feature_engine

Or simply clone it:

git clone https://github.com/feature-engine/feature_engine.git

Example Usage

>>> import pandas as pd
>>> from feature_engine.encoding import RareLabelEncoder

>>> 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 = RareLabelEncoder(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

Find more examples in our Jupyter Notebook Gallery or in the documentation.

Contribute

Details about how to contribute can be found in the Contribute Page

Briefly:

• Fork the repo
• Clone your fork into your local computer:

git clone https://github.com/<YOURUSERNAME>/feature_engine.git

• navigate into the repo folder

cd feature_engine

• Install Feature-engine as a developer:

pip install -e .

• Optional: Create and activate a virtual environment with any tool of choice
• Install Feature-engine dependencies:

pip install -r requirements.txt

and

pip install -r test_requirements.txt

• Create a feature branch with a meaningful name for your feature:

git checkout -b myfeaturebranch

• Develop your feature, tests and documentation
• Make sure the tests pass
• Make a PR

Thank you!!

Documentation

Feature-engine documentation is built using Sphinx and is hosted on Read the Docs.

To build the documentation make sure you have the dependencies installed: from the root directory:

pip install -r docs/requirements.txt

Now you can build the docs using:

sphinx-build -b html docs build

License

The content of this repository is licensed under a BSD 3-Clause license.

Sponsor us

Sponsor us and support further our mission to democratize machine learning and programming tools through open-source software.