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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, which requires the project to be well maintained.

• Document
• Experiment

Installation

pip install torchensemble

Example

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
Voting1	Parallel	voting.py	Classification / Regression
Neural Forest	Parallel	voting.py	Classification / Regression
Bagging2	Parallel	bagging.py	Classification / Regression
Gradient Boosting3	Sequential	gradient_boosting.py	Classification / Regression
Snapshot Ensemble4	Sequential	snapshot_ensemble.py	Classification / Regression
Adversarial Training5	Parallel	adversarial_training.py	Classification / Regression
Fast Geometric Ensemble6	Sequential	fast_geometric.py	Classification / Regression
Soft Gradient Boosting7	Parallel	soft_gradient_boosting.py	Classification / Regression

Dependencies

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

Reference

Thanks to all our contributors

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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.↩