WILDS is a benchmark of in-the-wild distribution shifts spanning diverse data modalities and applications, from tumor identification to wildlife monitoring to poverty mapping.

The WILDS package contains:

1. Data loaders that automatically handle data downloading, processing, and splitting, and
2. Dataset evaluators that standardize model evaluation for each dataset.

In addition, the example scripts contain default models, allowing new algorithms to be easily added and run on all of the WILDS datasets.

For more information, please read our paper or visit our website. For questions and feedback, please post on the discussion board.

We recommend using pip to install WILDS:

pip install wilds

If you have already installed it, please check that you have the latest version:

python -c "import wilds; print(wilds.__version__)"
# This should print "1.0.0". If it doesn't, update by running:
pip install -U wilds

If you plan to edit or contribute to WILDS, you should install from source:

git clone [email protected]:p-lambda/wilds.git
cd wilds
pip install -e .

• numpy>=1.19.1
• pandas>=1.1.0
• pillow>=7.2.0
• torch>=1.7.0
• tqdm>=4.53.0
• pytz>=2020.4
• outdated>=0.2.0
• ogb>=1.2.3
• torch-scatter>=2.0.5
• torch-geometric>=1.6.1

Running pip install wilds will check for all of these requirements except for the torch-scatter and torch-geometric packages, which require a quick manual install.

After installing the WILDS package, you can use the scripts in examples/ to train default models on the WILDS datasets. These scripts are not part of the installed WILDS package. To use them, you should clone the repo (assuming you did not install from source):

git clone [email protected]:p-lambda/wilds.git

To run these scripts, you will need to install these additional dependencies:

• torchvision>=0.8.1
• transformers>=3.5.0

All baseline experiments in the paper were run on Python 3.8.5 and CUDA 10.1.

In the examples/ folder, we provide a set of scripts that we used to train models on the WILDS package. These scripts are configured with the default models and hyperparameters that we used for all of the baselines described in our paper. All baseline results in the paper can be easily replicated with commands like:

cd examples
python run_expt.py --dataset iwildcam --algorithm ERM --root_dir data
python run_expt.py --dataset civilcomments --algorithm groupDRO --root_dir data

The scripts are set up to facilitate general-purpose algorithm development: new algorithms can be added to examples/algorithms and then run on all of the WILDS datasets using the default models.

The first time you run these scripts, you might need to download the datasets. You can do so with the --download argument, for example:

python run_expt.py --dataset civilcomments --algorithm groupDRO --root_dir data --download

The WILDS package provides a simple, standardized interface for all datasets in the benchmark. This short Python snippet covers all of the steps of getting started with a WILDS dataset, including dataset download and initialization, accessing various splits, and preparing a user-customizable data loader.

>>> from wilds.datasets.iwildcam_dataset import IWildCamDataset
>>> from wilds.common.data_loaders import get_train_loader
>>> import torchvision.transforms as transforms

# Load the full dataset, and download it if necessary
>>> dataset = IWildCamDataset(download=True)

# Get the training set
>>> train_data = dataset.get_subset('train',
...                                 transform=transforms.Compose([transforms.Resize((224,224)),
...                                                               transforms.ToTensor()]))

# Prepare the standard data loader
>>> train_loader = get_train_loader('standard', train_data, batch_size=16)

# Train loop
>>> for x, y_true, metadata in train_loader:
...   ...

The metadata contains information like the domain identity, e.g., which camera a photo was taken from, or which hospital the patient's data came from, etc.

To allow algorithms to leverage domain annotations as well as other groupings over the available metadata, the WILDS package provides Grouper objects. These Grouper objects extract group annotations from metadata, allowing users to specify the grouping scheme in a flexible fashion.

>>> from wilds.common.grouper import CombinatorialGrouper

# Initialize grouper, which extracts domain information
# In this example, we form domains based on location
>>> grouper = CombinatorialGrouper(dataset, ['location'])

# Train loop
>>> for x, y_true, metadata in train_loader:
...   z = grouper.metadata_to_group(metadata)
...   ...

The Grouper can be used to prepare a group-aware data loader that, for each minibatch, first samples a specified number of groups, then samples examples from those groups. This allows our data loaders to accommodate a wide array of training algorithms, some of which require specific data loading schemes.

# Prepare a group data loader that samples from user-specified groups
>>> train_loader = get_train_loader('group', train_data,
...                                 grouper=grouper,
...                                 n_groups_per_batch=2,
...                                 batch_size=16)

The WILDS package standardizes and automates evaluation for each dataset. Invoking the eval method of each dataset yields all metrics reported in the paper and on the leaderboard.

>>> from wilds.common.data_loaders import get_eval_loader

# Get the test set
>>> test_data = dataset.get_subset('test',
...                                 transform=transforms.Compose([transforms.Resize((224,224)),
...                                                               transforms.ToTensor()]))

# Prepare the data loader
>>> test_loader = get_eval_loader('standard', test_data, batch_size=16)

# Get predictions for the full test set
>>> for x, y_true, metadata in test_loader:
...   y_pred = model(x)
...   [accumulate y_true, y_pred, metadata]

# Evaluate
>>> dataset.eval(all_y_pred, all_y_true, all_metadata)
{'recall_macro_all': 0.66, ...}

If you use WILDS datasets in your work, please cite our paper (Bibtex):

• WILDS: A Benchmark of in-the-Wild Distribution Shifts (2020). Pang Wei Koh*, Shiori Sagawa*, Henrik Marklund, Sang Michael Xie, Marvin Zhang, Akshay Balsubramani, Weihua Hu, Michihiro Yasunaga, Richard Lanas Phillips, Sara Beery, Jure Leskovec, Anshul Kundaje, Emma Pierson, Sergey Levine, Chelsea Finn, and Percy Liang.

Please also cite the original papers that introduce the datasets, as listed on the datasets page.

The design of the WILDS benchmark was inspired by the Open Graph Benchmark, and we are grateful to the Open Graph Benchmark team for their advice and help in setting up WILDS.