probly: Uncertainty Representation and Quantification for Machine Learning

🛠️ Install

probly is intended to work with Python 3.12 and above. Installation can be done via pip and or uv:

pip install probly

uv add probly

⭐ Quickstart

probly makes it very easy to make models uncertainty-aware and perform several downstream tasks:

import probly
import torch.nn.functional as F

net = ...  # get neural network
model = probly.representation.Dropout(net)  # make neural network a Dropout model
train(model)  # train model as usual

data = ...  # get data
preds = model.predict_representation(data)  # predict an uncertainty representation
eu = probly.quantification.classification.mutual_information(preds)  # compute model's epistemic uncertainty

data_ood = ...  # get out of distribution data
preds_ood = model.predict_representation(data_ood)
eu_ood = probly.quantification.classification.mutual_information(preds_ood)
auroc = probly.evaluation.tasks.out_of_distribution_detection(eu,
                                                              eu_ood)  # compute the AUROC score for out of distribution detection

📜 License

This project is licensed under the MIT License.

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Built with ❤️ by the probly team.