ALIEN
Active Learning In data ExploratioN (ALIEN) is a library for active learning, making it easy to use active learning with popular machine learning frameworks, such as Pytorch, Keras, Deepchem, LightGBM, CatBoost and others. In most cases, you can plug your existing model and dataset into ALIEN’s wrapper classes and immediately get useful functions, such as finding the (epistemic) uncertainty of a prediction, or selecting batches of new points to be labelled next (eg., by measuring them in a lab).
We have some handy installation instructions: Installation
The two main submodules for the end-user of ALIEN are
which contains wrapper classes which give your models the tools to compute uncertainties and get embeddings, and
containing SampleSelector subclasses which implement a number of different batch
selection strategies. (ALIEN is designed to make it maximally easy for
you to implement new selection strategies.)
The other submodules are alien.data, containing wrapper classes for various
data formats (you may or may not have to use these), alien.sample_generation,
containing classes to help with generating sample pools for the selectors, and
alien.benchmarks, containing functions for running “retrospective experiments”
and benchmarking selector performance.