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Bayesian Batch Active Learning as Sparse Subset Approximation

This repository contains the code to reproduce the experiments carried out in Bayesian Batch Active Learning as Sparse Subset Approximation.

The code has been authored by: Robert Pinsler and Jonathan Gordon.

Dependencies and Data Requirements

This code requires the following:

• Python >= 3.5
• torch >= 1.0
• torchvision >= 0.2.2
• numpy
• scipy
• sklearn
• pandas
• matplotlib
• gtimer

To run the regression experiments, please download the UCI regression datasets and place them into ./data.

GPU Requirements

• The code supports experiments on either GPU or CPU processors.

Usage

The experiments provided in this code base include active learning on standard vision based datasets (classification) and UCI datasets (regression). The following experiments are provided (see section 7 of the paper):

1. Active learning for regression: run the following command

   ./scripts/run_active_regression.sh DATASET ACQ CORESET

   where

   • DATASET may be one of {yacht, boston, energy, power, year} (determines the dataset to be used).
   • ACQ may be one of {BALD, Entropy, ACS} (determines the acquisition function to be used).
   • CORESET may be one of {Argmax, Random, Best, FW} (determines the querying strategy to be used)

   For example, to run the proposed method on the boston dataset, please run:

   ./scripts/run_active_regression.sh boston ACS FW

   This will automatically generate an experimental directory with the appropriate name, and place results from 40 seeds in the directory. Hyper-parameters for the experiments can all be found in the main body of the paper.

2. Active learning for regression (with projections -- should be used for large datasets e.g., year and power): run the following command

   ./scripts/run_active_regression_projections.sh DATASET NUM_PROJECTIONS

   where

   • DATASET may be one of {yacht, boston, power, protein, year} (determines the dataset to be used).
   • NUM_PROJECTIONS is an integer (determines the number of samples used to estimate values).

   For example, to run the proposed method on the year dataset, please run:

   ./scripts/run_active_regression.sh year 10

   This will automatically generate an experimental directory with the appropriate name, and place results from 40 seeds in the directory. Hyper-parameters for the experiments can all be found in the main body of the paper.

3. Active learning for classification (using standard active learning methods): run the following command

   ./scripts/run_active_torchvision.sh ACQ CORESET DATASET

   where

   • ACQ may be one of {BALD, Entropy} (determines the acquisition function to be used).
   • CORESET may be one of {Argmax, Random, Best} (determines the querying strategy to be used)
   • DATASET may be one of {cifar10, svhn, fashion_mnist} (determines the dataset to be used).

   For example, to run greedy BALD on CIFAR10, run the following command:

   ./scripts/run_active_torchvision.sh BALD Argmax cifar10

   This will automaticall generate an experimental directory with an appropriate name, and place results from 5 runs in the directory.

4. Active learning for classification (using projections as in section 5 of the paper): run the following command

   ./scripts/run_active_torchvision_projections.sh CORESET DATASET

   where -CORESET may be one of {Argmax, Random, Best, FW} (determines the querying strategy to be used) -DATASET may be one of {cifar10, svhn, fashion_mnist} (determines the dataset to be used).

   For example, to run the proposed method on the CIFAR10 dataset, please run:

   ./scripts/run_active_torchvision_projections.sh FW cifar10

   This will automaticall generate an experimental directory with an appropriate name, and place results from 5 runs in the directory.

Plotting

Code to generate active learning curves as exhibited in the paper is also provided. To generate appropriate learning curves, please run the command

python3 ./scripts/enjoy_learning_curves.py --load_dir=LOAD_DIR --metric=METRIC --eval_at=EVAL_AT --format=FORMAT

where

• LOAD_DIR is the path to a results directory generated by an experiment
• METRIC may be one of {LL, RMSE, Accuracy}
• EVAL_AT may be one of {num_evals, wt, num_samples} (wt signifies wall time)
• FORMAT may be one of {png, pdf}

Running this script will automatically generate a figure of the learning curve in the directory

Citation

If you use this code, please cite our paper:

@article{pinsler2019bayesian,
  title={Bayesian Batch Active Learning as Sparse Subset Approximation},
  author={Pinsler, Robert and Gordon, Jonathan and Nalisnick, Eric and Hern{\'a}ndez-Lobato, Jos{\'e} Miguel},
  journal={arXiv preprint arXiv:https://arxiv.org/abs/1908.02144},
  year={2019}
}

General Structure of the Repositiory

• acs: This directory contains the core code used in the repository. Inside you will find the following subdirectories and files:
  • Baselines: contains implementations of the following baseline methods used for comparison in the paper:
    • K-center greedy (https://arxiv.org/abs/1708.00489)
    • K-medoids (applies K-medoids clustering to generate a batch)
  • acquisition_functions: implementation of all the functions necessary for constructing acquisition functions (e.g., BALD, max-entropy, coreset norm computations)
  • al_data_set: implementation of a special data handling class that supports active learning in PyTorch (e.g., handles indexing of train and pool sets, and moves examples from pool to train set when queried). -coresets: core file handling the method proposed in the paper. Implements classes such as Coreset (general use for active learning querying), Frank-Wolfe coresets, random / argmax acquisition. Also contains implementations of baseline methods mentioned above in our active learning framework, -model: implements the model classes used in our experiments, including linear regression, neural networks, and approximate Bayesian inference layers such as variational inference, (local) reparametrization, etc. utils: collection of utility and probability functionalities required in the code base.
• experiments: Contains python scripts for running experiments in the paper. Has experimental scripts for:
  • linear_regression: simple training script for linear regression models on the UCI datasets.
  • linear_regression_active: runs an active learning experiment (starting with an initial labeled set, and querying the pool set in batch mode until budget is exhausted) with a specified model, acquisition function, dataset, and additional hyper-parameters.
  • linear_regression_active_projections: same, but using projection based methods for batch mode querying. Should be used for larger regression datasets such as year and protein.
  • torchvision_active: runs a classification based active learning experiment on one of the torchvision datasets (e.g, Fashion-MNIST, SVHN, CIFAR10). Should be used with baseline methods.
  • torchvision_active_projections: same, but with projection based methods for batch mode querying. Should be used with our methods (Frank-Wolf) optimization.
• resnet: Contains code necessary for loading and training ResNet modules.
• scripts: Contains .sh files used to run experiments (see above for more details).