There are 2 repositories under positive-unlabeled-learning topic.
Python codes for weakly-supervised learning
Simple sklearn based python implementation of Positive-Unlabeled (PU) classification using bagging based ensembles
Positive and Unlabeled Materials Machine Learning (pumml) is a code that uses semi-supervised machine learning to classify materials from only positive and unlabeled examples.
An example repo for how PU Bagging and TSA works.
A curated list of resources dedicated to Positive Unlabeled(PU) learning ML methods.
A collection of notebooks that implement algorithms introduced in "Learning from positive and unlabeled data: a survey"
Python framework for interpretable protein prediction
🍊 PAUSE (Positive and Annealed Unlabeled Sentence Embedding), accepted by EMNLP'2021 🌴
uPU, nnPU and PN learning with Extra Trees classifier.
NeurIPS'20 Paper: "Learning from Positive and Unlabeled Data with Arbitrary Positive Shift"
Predicting protein functions using positive-unlabeled ranking with ontology-based priors
PyTorch Implementation of Asymetric Cross Entropy Loss (Loss Correction for PU Learning)
Domain Adaptation with Dynamic Open-Set Targets