To read on our findings, please read our paper.

Note: If you encounter any trouble with the installation, please refer to WILDS-README for a detailed installation process, as it outlines all dependencies.

To install the dependencies automatically, use:

pip install wilds

Running pip install wilds will automatically check for and install all required dependencies, except for torch-scatter and torch-geometric packages. These require manual installation. For more information, visit: PyTorch Geometric Installation.

To install py150, ensure you are in the topmost level of the repository (path/to/wilds) and execute:

python wilds/download_datasets.py --datasets py150 --root_dir data

In our paper, we mention the datasets data24k and data1500, which are subsets of py150 containing 24,000 and 1,500 training samples, respectively, instead of approximately 80,000 as in py150. This sampling decision was made due to the long runtime of training the full set. However, the ratios between the validation and testing data have been kept consistent in both subsets relative to py150.

To sample these subsets or others, ensure you are in the topmost level of the repository (path/to/wilds) and run with appropriate arguments:

python augmentation/sample_data.py --input data --output data24k --sample_size 24000

or

python augmentation/sample_data.py --input data --output data1500 --sample_size 1500

For clarity on command line arguments, run:

python augmentation/sample_data.py -h

Note: If you wish to run the augmentations on the full py150, you can skip the Setup section.

The precise versions of data24k and data1500used by the authors are not uploaded to the repository due to their large size. For access to these datasets, you may contact the authors at becker.mel@northeastern.edu or jovanovic.l@northeastern.edu.

After sampling your dataset, it needs to be processed. Follow these steps:

Step 0: Navigate to the directory of the sampled set:

cd path/to/wilds/data1500/py150_v1.0/

Step 1: Run the tokenization script:

python script/1.prepare_tokenized.py

Step 2: Run the evaluation preparation script:

python script/2.prepare_eval_position.py

We provide two different augmentation strategies: 'mixed' and 'combined'. The difference between these is explained in Section 4.2 of our paper.

To run the augmentations with the 'mixed' strategy, ensure you are in the topmost level of the repository (path/to/wilds) and execute:

python augmentation/augment_mixed_file.py --data_dir data24k --aug_dir data24k-aug -s 24000 -k 1 logs/logs24k/mixed/all-1

or

python augmentation/augment_mixed_file.py --data_dir data1500 --aug_dir data1500-aug -s 1500 -k 1 logs/logs1500/mixed/all-1

For clarification on command line arguments, run:

python augmentation/augment_mixed_file.py -h

To run the augmentations with the 'combined' strategy, ensure you are in the topmost level of the repository (path/to/wilds) and execute:

python augmentation/augment_combined_file.py --data_dir data24k --aug_dir data24k-aug -s 24000 -k 1 logs/logs24k/combined/all-1

or

python augmentation/augment_combined_file.py --data_dir data1500 --aug_dir data1500-aug -s 1500 -k 1 logs/logs1500/combined/all-1

For clarification on command line arguments, run:

python augmentation/augment_combined_file.py -h

The logs directory (or any directory of your choice) will store the aug.log file, indicating how many times each augmentation has been applied to the dataset, as well as the total number of augmentations. This content will also be printed on the terminal, indicating a successful execution of the script.

Note: Both augment_mixed_file.py and augment_combined_file.py implement what we call in the paper the 'enhanced augmentation set'. To exclude certain augmentations, please edit the script you wish to run by removing or commenting out the undesired refactorings from the refactors_list at the top of the script.

After augmenting your dataset, it needs to be processed before running on the model. Follow these steps:

Step 0: Navigate to the directory of the augmented set:

cd path/to/wilds/data1500-aug/py150_v1.0/

Step 1: Run the tokenization script:

python script/1.prepare_tokenized.py

Step 2: Run the evaluation preparation script:

python script/2.prepare_eval_position.py

Now, you are ready to run the control dataset. Ensure you are in the topmost level of the repository (path/to/wilds) and execute:

python examples/run_expt.py --root_dir data24k --log_dir logs/logs24k/control --dataset py150 --algorithm ERM --seed 0 --lr 8e-5 --weight_decay 0

or

python examples/run_expt.py --root_dir data1500 --log_dir logs/logs1500/control --dataset py150 --algorithm ERM --seed 0 --lr 8e-5 --weight_decay 0

Once run_expt.py has finished running, your results will be stored in the specified log_dir, within a file named log.txt. The results will also be printed on the terminal, indicating successful completion.

These command-line arguments run the model using the same parameters reported in the larger WILDS project. For clarification on what the arguments mean, run:

python examples/run_expt.py -h

Now, you are ready to run the augmented dataset. Ensure you are in the topmost level of the repository (path/to/wilds) and execute:

python examples/run_expt.py --root_dir data24k-aug --log_dir logs/logs24k/mixed/all-1 --dataset py150 --algorithm ERM --seed 0 --lr 8e-5 --weight_decay 0

or

python examples/run_expt.py --root_dir data1500-aug --log_dir logs/logs1500/combined/all-1 --dataset py150 --algorithm ERM --seed 0 --lr 8e-5 --weight_decay 0

Once run_expt.py has finished running, your results will be stored in the specified log_dir, within a file named log.txt. The results will also be printed on the terminal, indicating successful completion.

For clarification on command line arguments, run:

python examples/run_expt.py -h

We offer a script for comparing the results of two experiments. To use it, edit the filepaths at the top of logs/eval-results.py to point to the directories containing the log.txt files for experiments you wish to compare. Then, ensure you are in the topmost level of the repository (path/to/wilds) and execute:

python logs/eval_results.py

This script outputs a comparison of both experiments for every category in every epoch, including the numerical difference between the experiments' accuracies for the category. In addition, it indicates which experiment performed better in the larger number of categories for every epoch.