IMBENS: Class-imbalanced Ensemble Learning in Python

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⏳Quick Start with our 5-minute Guide & Detailed Examples

IMBENS (imported as imbens) is a Python library for quick implementation, modification, evaluation, and visualization of ensemble learning from class-imbalanced data. Currently, IMBENS includes over 15 ensemble imbalanced learning algorithms (SMOTEBoost, SMOTEBagging, RUSBoost, EasyEnsemble, SelfPacedEnsemble, etc) and 19 over-/under-sampling methods (SMOTE, ADASYN, TomekLinks, etc) from imbalance-learn.

🌈 IMBENS Highlights

• 🧑‍💻 Ease-of-use: Unified, easy-to-use APIs with documentation and examples.
• 🚀 Performance: Optimized performance with parallelization using joblib.
• 📊 Benchmarking: Running & comparing multiple models with our visualizer.
• 📺 Monitoring: Powerful, customizable, interactive training logging.
• 🪐 Versatility: Full compatibility with scikit-learn and imbalanced-learn.
• 📈 Functionality: Extending existing techniques from binary to multi-class setting.

✂️ Use IMBENS for class-imbalanced classification with <5 lines of code:

# Train an SPE classifier
from imbens.ensemble import SelfPacedEnsembleClassifier
clf = SelfPacedEnsembleClassifier(random_state=42)
clf.fit(X_train, y_train)

# Predict with an SPE classifier
y_pred = clf.predict(X_test)

🤗 Citing IMBENS

🍻 We appreciate your citation if you find our work helpful! The BibTeX entry:

@article{liu2023imbens,
  title={IMBENS: Ensemble Class-imbalanced Learning in Python},
  author={Liu, Zhining and Kang, Jian and Tong, Hanghang and Chang, Yi},
  journal={arXiv preprint arXiv:2111.12776},
  year={2023}
}

👯‍♂️ Contribute to IMBENS

Join us and become a contributor! Please refer to the contributing guidelines.

📚 Table of Contents

• Installation
• List of implemented methods
• 5-min Quick Start with IMBENS
  • A minimal working example
  • Visualize ensemble classifiers
  • Customizing training log
• About imbalanced learning
• Acknowledgements
• References
• Related Projects
• Contributors ✨

Installation

It is recommended to use pip for installation.
Please make sure the latest version is installed to avoid potential problems:

$ pip install imbalanced-ensemble            # normal install
$ pip install --upgrade imbalanced-ensemble  # update if needed

Or you can install imbalanced-ensemble by clone this repository:

$ git clone https://github.com/ZhiningLiu1998/imbalanced-ensemble.git
$ cd imbalanced-ensemble
$ pip install .

imbalanced-ensemble requires following dependencies:

• Python (>=3.6)
• numpy (>=1.16.0)
• pandas (>=1.1.3)
• scipy (>=1.9.1)
• joblib (>=0.11)
• scikit-learn (>=1.2.0)
• matplotlib (>=3.3.2)
• seaborn (>=0.11.0)
• tqdm (>=4.50.2)

List of implemented methods

Currently (v0.1.3, 2021/06), 16 ensemble imbalanced learning methods were implemented:
(Click to jump to the document page)

• Resampling-based
  • Under-sampling + Ensemble
    1. SelfPacedEnsembleClassifier [1] (in Github)
    2. BalanceCascadeClassifier [2]
    3. BalancedRandomForestClassifier [3] (imblearn version)
    4. EasyEnsembleClassifier [2] (imblearn version)
    5. RUSBoostClassifier [4] (imblearn version)
    6. UnderBaggingClassifier [5] (imblearn version)
  • Over-sampling + Ensemble
    1. OverBoostClassifier
    2. SMOTEBoostClassifier [6]
    3. KmeansSMOTEBoostClassifier
    4. **OverBaggingClassifier [5] ([imblearn