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Ensemble PyTorch

A unified ensemble framework for pytorch_ to easily improve the performance and robustness of your deep learning model. Ensemble-PyTorch is part of the pytorch ecosystem <https://pytorch.org/ecosystem/>__, which requires the project to be well maintained.

• Document <https://ensemble-pytorch.readthedocs.io/>__
• Experiment <https://ensemble-pytorch.readthedocs.io/en/stable/experiment.html>__

Installation

.. code:: bash

pip install torchensemble

Example

.. code:: python

from torchensemble import VotingClassifier  # voting is a classic ensemble strategy

# Load data
train_loader = DataLoader(...)
test_loader = DataLoader(...)

# Define the ensemble
ensemble = VotingClassifier(
    estimator=base_estimator,               # estimator is your pytorch model
    n_estimators=10,                        # number of base estimators
)

# Set the optimizer
ensemble.set_optimizer(
    "Adam",                                 # type of parameter optimizer
    lr=learning_rate,                       # learning rate of parameter optimizer
    weight_decay=weight_decay,              # weight decay of parameter optimizer
)

# Set the learning rate scheduler
ensemble.set_scheduler(
    "CosineAnnealingLR",                    # type of learning rate scheduler
    T_max=epochs,                           # additional arguments on the scheduler
)

# Train the ensemble
ensemble.fit(
    train_loader,
    epochs=epochs,                          # number of training epochs
)

# Evaluate the ensemble
acc = ensemble.evaluate(test_loader)         # testing accuracy

Supported Ensemble

+------------------------------+------------+---------------------------+-----------------------------+ | Ensemble Name | Type | Source Code | Problem | +==============================+============+===========================+=============================+ | Fusion | Mixed | fusion.py | Classification / Regression | +------------------------------+------------+---------------------------+-----------------------------+ | Voting [1]_ | Parallel | voting.py | Classification / Regression | +------------------------------+------------+---------------------------+-----------------------------+ | Neural Forest | Parallel | voting.py | Classification / Regression | +------------------------------+------------+---------------------------+-----------------------------+ | Bagging [2]_ | Parallel | bagging.py | Classification / Regression | +------------------------------+------------+---------------------------+-----------------------------+ | Gradient Boosting [3]_ | Sequential | gradient_boosting.py | Classification / Regression | +------------------------------+------------+---------------------------+-----------------------------+ | Snapshot Ensemble [4]_ | Sequential | snapshot_ensemble.py | Classification / Regression | +------------------------------+------------+---------------------------+-----------------------------+ | Adversarial Training [5]_ | Parallel | adversarial_training.py | Classification / Regression | +------------------------------+------------+---------------------------+-----------------------------+ | Fast Geometric Ensemble [6]_ | Sequential | fast_geometric.py | Classification / Regression | +------------------------------+------------+---------------------------+-----------------------------+ | Soft Gradient Boosting [7]_ | Parallel | soft_gradient_boosting.py | Classification / Regression | +------------------------------+------------+---------------------------+-----------------------------+

Dependencies

• scikit-learn>=0.23.0
• torch>=1.4.0
• torchvision>=0.2.2

Reference

.. [1] Zhou, Zhi-Hua. Ensemble Methods: Foundations and Algorithms. CRC press, 2012.

.. [2] Breiman, Leo. Bagging Predictors. Machine Learning (1996): 123-140.

.. [3] Friedman, Jerome H. Greedy Function Approximation: A Gradient Boosting Machine. Annals of Statistics (2001): 1189-1232.

.. [4] Huang, Gao, et al. Snapshot Ensembles: Train 1, Get M For Free. ICLR, 2017.

.. [5] Lakshminarayanan, Balaji, et al. Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles. NIPS, 2017.

.. [6] Garipov, Timur, et al. Loss Surfaces, Mode Connectivity, and Fast Ensembling of DNNs. NeurIPS, 2018.

.. [7] Feng, Ji, et al. Soft Gradient Boosting Machine. ArXiv, 2020.

.. _pytorch: https://pytorch.org/

.. _pypi: https://pypi.org/project/torchensemble/

Thanks to all our contributors

|contributors|

.. |contributors| image:: https://contributors-img.web.app/image?repo=TorchEnsemble-Community/Ensemble-Pytorch .. _contributors: https://github.com/TorchEnsemble-Community/Ensemble-Pytorch/graphs/contributors