Python package, methods and data for the paper Deep Batch Active Learning for Drug Discovery.

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, 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).

ALiEN is in Beta testing and is not a finished product! TODO: Change documentation link DOCUMENTATION HERE

$ git clone https://github.com/Sanofi-Public/Alien.git
$ cd Alien
$ pip install .

To install extras, use pip install .[<extra>] where <extra> may be one of

• torch,
• tensorflow,
• deepchem,
• deepchem-torch, or
• deepchem-tensorflow.

In the following example, we take certain objects as given:

• keras_model is a Keras regression model
• X and y are a set of initial labeled data, in a form consumable by keras_model
• unlabeled_pool is a set of samples to select from

Then the following code will wrap the model in ALIEN's MCDropoutRegressor class, fit the model to X and y, and then select a batch of 10 samples from unlabeled_pool for labelling using the COVDROP selection strategy (which is what we call the combination of MC dropout with CovarianceSelector):

from alien.models import MCDropoutRegressor
from alien.selecion import CovarianceSelector

alien_model = MCDropoutRegressor(keras_model)
alien_model.fit(X, y)

selector = CovarianceSelector(
    model = alien_model,
    samples = unlabeled_pool,
    batch_size = 10,
)

batch = selector.select()

Development of ALIEN happens on GitHub.

• If you encounter a bug, please fill out the issue form here.
• If you have a feature request, please fill the feature request form.