This repository contains code accompanying our paper "A Framework and Benchmark for Deep Batch Active Learning for Regression". It can be used for the following purposes:

• Apply various pool-based Batch Mode Deep Active Learning (BMDAL) algorithms for regression to custom neural networks (NNs) or kernel methods
• Use our NN for tabular regression through a simple scikit-learn style interface
• Download large tabular regression data sets from our benchmark
• Compare BMDAL algorithms using our benchmark

If you use this code for research purposes, plese cite our paper.

This repository contains an efficient implementation of our framework for building BMDAL algorithms for NN regression, which includes

• an implementation of the greedy Core-Set method for regression
• an implementation of BAIT for regression
• an implementation of variants of ACS-FW for regression
• an adaptation of BADGE for regression
• an adaptation of BatchBALD for regression
• an adaptation of BALD for regression
• an implementation of our own LCMD-TP method with sketched gradient features

• The commit corresponding to version 1 of our arXiv paper is tagged arxiv_v1 and also archived with the corresponding data at DaRUS.
• The commit corresponding to version 2 of our arXiv paper is tagged arxiv_v2 and also archived with the corresponding data at DaRUS.
• The commit corresponding to version 3 of our arXiv paper is tagged arxiv_v3 and also archived with the corresponding data at DaRUS. Results from versions 1 and 2 are run with slightly different options, hence they should not be mixed though the numbers (except for the runtimes) are very similar. Changes in version 2 are listed below. Changes in version 3 are minimal and produce almost the same numbers.

This source code is licensed under the Apache 2.0 license. However, the implementation of the acs-rf-hyper kernel transformation in bmdal/features.py is adapted from the source code at https://github.com/rpinsler/active-bayesian-coresets, which comes with its own (non-commercial) license. Please respect this license when using the acs-rf-hyper transformation directly from bmdal/features.py or indirectly through the interface provided at bmdal/algorithms.py.

This code has been tested with Python 3.9.2 but may be compatible with versions down to Python 3.6.

For running our NN and the active learning methods, a pip installation is sufficient. The library can be installed via

pip3 install bmdal_reg

When using our benchmarking code through a pip installation, the paths where experiment data and plots are saved can be modified through changing the corresponding path variables of bmdal_reg.custom_paths.CustomPaths before running the benchmark.

For certain purposes, especially trying new methods and running the benchmark, it might be helpful or necessary to modify the code. For this, the code can be manually installed via cloning the GitHub repository and then following the instructions below:

The following packages (available through pip) need to be installed:

• General: torch, numpy, dill
• For running experiments with run_experiments.py: psutil
• For plotting the experiment results: matplotlib, seaborn, scipy
• For downloading the data sets with download_data.py: pandas, openml, mat4py

If you want to install PyTorch with GPU support, please follow the instructions on the PyTorch website. The following command installs the versions of the libraries we used for running the benchmark, which however come with security warnings in the meantime:

pip3 install -r requirements_original.txt

Alternatively, the following command installs current versions of the packages:

pip3 install torch numpy dill psutil matplotlib seaborn pandas openml mat4py scipy

Clone the repository (or download the files from the repository) and change to its folder:

git clone git@github.com:dholzmueller/bmdal_reg.git
cd bmdal_reg

Then, copy the file bmdal_reg/custom_paths.py.default to bmdal_reg/custom_paths.py via

cp bmdal_reg/custom_paths.py.default bmdal_reg/custom_paths.py

and, if you want to, adjust the paths in custom_paths.py to specify the folders in which you want to save data and results.

If you want to use the benchmark data sets, you need to download and preprocess them. We do not provide preprocessed versions of the data sets to avoid copyright issues, but you can download and preprocess the data sets using

python3 download_data.py

Note that this may take a while. This depends of course on your download speed. The preprocessing is mostly fast, but for the (large) methane data set, it took around five minutes and 25 GB of RAM for us. If you cannot download/process the data due to limited RAM, please contact the main developer (see below).

Depending on your use case, some of the following introductory Jupyter notebooks may be helpful:

• examples/benchmark.ipynb shows how to download or reproduce our experimental results, how to benchmark other methods, and how to evaluate the results.
• examples/using_bmdal.ipynb shows how to apply our BMDAL framework to your use-case.
• examples/framework_details.ipynb explains how our BMDAL framework is implemented, which may be relevant for advanced usage.
• New: examples/nn_interface.ipynb shows how our NN configuration can be used (without active learning) through a simple scikit-learn style interface.

Besides these notebooks, you can also take a look at the code directly. The more important parts of our code are documented with docstrings.

The code is structured as follows:

• Library code is contained in the bmdal_reg folder, while directly executable files are contained in the top-level folder.
• The bmdal_reg/bmdal folder contains the implementation of all BMDAL methods, with its main interface in bmdal/algorithms.py.
• The bmdal_reg/evaluation folder contains code for analyzing and plotting generated data, which is called from run_evaluation.py.
• The examples folder contains Jupyter Notebooks for instructive purposes as mentioned above.
• The file download_data.py allows for downloading the data, run_experiments.py allows for starting the experiments, test_single_task.py allows for testing a configuration on a data set, and rename_algs.py contains some functionality for adjusting experiment data in case of mistakes.
• The file check_task_learnability.py has been used to check the reduction in RMSE on different data sets when going from 256 to 4352 random samples. We used this to sort out the data sets where the reduction in RMSE was too small, since these data sets are unlikely to make a substantial difference in the benchmark results.
• The files bmdal_reg/data.py, bmdal_reg/layers.py, bmdal_reg/models.py, bmdal_reg/task_execution.py, bmdal_reg/train.py and bmdal_reg/utils.py implement parts of data loading, training, and parallel execution.

• Added the BAIT selection method with variants BAIT-F and BAIT-FB.
• For the normalization of input data, mean and standard deviations for the features are now computed on training and pool set instead of only on the initial training set.
• More precise runtime measurement through CUDA synchronize (only applied in one of the 20 splits where only one process is run per GPU).
• Now, 64-bit floating point computations are used for computations involving posterior transformations. This can sometimes cause RAM overflows when using parallel execution, though.
• We use $\sigma^2 = 10^{-6}$ instead of $\sigma^2 = 10^{-4}$ now, which still works well due to the change to 64-bit floats.
• The computation of the last-layer kernel does not require the full backward pass now since the earlier layers set requires_grad=False for the computation.
• Fixed a discrepancy between the implementation of selection methods and the corresponding paper pseudocode: Previously, some selection methods could re-select already selected samples in case of numerical issues, which triggered a code filling up the batch with random samples. Now, selecting already selected samples is explicitly prevented.
• Changed the interface of run_experiments.py to be based on lists instead of callbacks.

• We fixed a bug where MaxDist, LCMD and KMeansPP selected the first point incorrectly when applied in TP mode (i.e., when using sel_with_train=True). This only led to negligible differences at the batch sizes used in our benchmark.
• We fixed a bug where MaxDet and BAIT could select a training point for the batch, which would then cause the rest of the batch to be filled up randomly instead. This bug should occur rarely since training points usually have very low uncertainty, and it did not affect our benchmark results.
• We fixed a problem where adding jitter if needed for the Cholesky decomposition was only performed for CPU computations. This should also not affect the benchmark results because the benchmark was run with regularization and 64-bit floats.

• David Holzmüller (main developer)
• Viktor Zaverkin (contributed to the evaluation code)

If you would like to contribute to the code or would be interested in additional features, please contact David Holzmüller.