Welcome to imbalanced-ensemble documentation!

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Date: Jun 16, 2024 Version: 0.2.2 Github: imbalanced-ensemble

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

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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}
}

API Demo:

>>> from imbens.ensemble import SelfPacedEnsembleClassifier
>>> from imbens.datasets import generate_imbalance_data
>>> from imbens.utils import evaluate_print
>>> from imbens.visualizer import ImbalancedEnsembleVisualizer
>>>
>>> X_train, X_test, y_train, y_test = generate_imbalance_data(
...     n_samples=200, weights=[.9,.1], test_size=.5)
>>>
>>> clf = SelfPacedEnsembleClassifier()                    # initialize ensemble
>>> clf.fit(X_train, y_train)
>>>
>>> y_test_pred = clf.predict(X_test)                      # predict labels
>>> y_test_proba = clf.predict_proba(X_test)               # predict probabilities
>>>
>>> evaluate_print(y_test, y_test_pred, "SPE")             # performance evaluation
SPE balanced Acc: 0.972 | macro Fscore: 0.886 | macro Gmean: 0.972
>>>
>>> visualizer = ImbalancedEnsembleVisualizer()            # initialize visualizer
>>> visualizer.fit({'SPE': clf})
>>>
>>> visualizer.performance_lineplot()                      # performance visualization
>>> visualizer.confusion_matrix_heatmap()                  # prediction visualization

Getting Started

API

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