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p-lambda
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Description

A machine learning benchmark of in-the-wild distribution shifts, with data loaders, evaluators, and default models.

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Overview

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.

Installation

We recommend using pip to install WILDS:

bash
pip install wilds

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

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

Requirements

  • 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  • outdated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
or
pip install -e .
will automatically check for and install all of these requirements except for the
torch-scatter
and
torch-geometric
packages, which require a quick manual install.

Default models

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):
bash
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.

Usage

Default models

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

Data loading

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.

Domain information

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)

Evaluators

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, ...}

Citing WILDS

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.

Acknowledgements

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.

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