(A)ctive (L)earning (Bench)marking tool: This is a benchmarking tool for evaluating active learning strategies for machine learning.
The tool takes an input dataset, machine learning model, and active learning strategy and outputs information to be used in evaluating how well the strategy does with that model and dataset. By running the tool multiple times with different inputs, the tool allows comparisons across different active learning strategies and also allows comparisons across different models and across different datasets. Researchers can use the tool to test proposed active learning strategies in the context of a specific model and dataset; or multiple models and datasets can be used to get a broader picture of each strategy's effectiveness in multiple contexts. As an alternative use case, multiple runs of the tool with different models and datasets can be compared, evaluating these models and datasets for their compatibility with a given active learning strategy.
The top-level code creates and configures handlers for the dataset, machine learning
model, and active learning strategy. Then it invokes the active learning strategy
handler to evaluate the strategy on the dataset using the model.
Download the source code using
git clone https://github.com/DigitalSlideArchive/ALBench.git
or a similar command. Then install it with pip using the name of the directory that you downloaded to:
pip install ./ALBench
If you wish to use the al_bench.model or al_bench.strategy subpackage you will also need to install tensorflow and torch. If you wish to use batchbald_redux you will need that too:
pip install 'tensorflow<3.0' 'torch<2.0' batchbald_redux
(Torch can be hard to install. See its installation instructions for help.)
Import the top-level package and each subpackage you wish to use
import al_bench as alb
import al_bench.dataset, al_bench.model, al_bench.strategy, al_bench.factory
# Use alb.dataset.*, alb.model.*, etc.
See SimpleExample.ipynb for a simple example of the dataset, model, and strategy subpackages. See test/test_0040_factory.py for an example use of the factory subpackage.