feature-engine 1.8.2

Feature engineering and selection package with Scikit-learn's fit transform functionality

Feature-engine

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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

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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
• Scaling
• 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 Scaling methods

• MeanNormalizationScaler

Variable Creation:

• MathFeatures
• RelativeFeatures
• CyclicalFeatures
• DecisionTreeFeatures()

Feature Selection:

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

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.

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