Metadata-Version: 2.1
Name: wilds
Version: 1.2.0
Summary: WILDS distribution shift benchmark
Home-page: https://wilds.stanford.edu
Author: WILDS team
Author-email: wilds@cs.stanford.edu
License: MIT
Description: <p align='center'>
          <img width='40%' src='https://wilds.stanford.edu/WILDS_cropped.png' />
        </p>
        
        --------------------------------------------------------------------------------
        
        [![PyPI](https://img.shields.io/pypi/v/wilds)](https://pypi.org/project/wilds/)
        [![License](https://img.shields.io/badge/license-MIT-blue.svg)](https://github.com/p-lambda/wilds/blob/master/LICENSE)
        
        ## 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](https://arxiv.org/abs/2012.07421) or visit [our website](https://wilds.stanford.edu).
        For questions and feedback, please post on the [discussion board](https://github.com/p-lambda/wilds/discussions).
        
        ## 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 "1.1.0". 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 git@github.com:p-lambda/wilds.git
        cd wilds
        pip install -e .
        ```
        
        ### Requirements
        - numpy>=1.19.1
        - ogb>=1.2.6
        - outdated>=0.2.0
        - pandas>=1.1.0
        - pillow>=7.2.0
        - pytz>=2020.4
        - torch>=1.7.0
        - torch-scatter>=2.0.5
        - torch-geometric>=1.6.1
        - tqdm>=4.53.0
        
        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](https://pytorch-geometric.readthedocs.io/en/latest/notes/installation.html#installation-via-binaries).
        
        
        ### 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 git@github.com: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.
        
        
        ## Using the example scripts
        
        In the `examples/` folder, we provide a set of scripts that can be used to download WILDS datasets and train models on them.
        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:
        
        ```bash
        python examples/run_expt.py --dataset iwildcam --algorithm ERM --root_dir data
        python examples/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.
        
        ### Downloading and training on the WILDS datasets
        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 examples/run_expt.py --dataset civilcomments --algorithm groupDRO --root_dir data --download
        ```
        
        Alternatively, you can use the standalone `wilds/download_datasets.py` script to download the datasets, for example:
        
        ```bash
        python wilds/download_datasets.py --root_dir data
        ```
        
        This will download all datasets to the specified `data` folder. You can also use the `--datasets` argument to download particular datasets.
        
        These are the sizes of each of our datasets, as well as their approximate time taken to train and evaluate the default model for a single ERM run using a NVIDIA V100 GPU.
        
        | Dataset command | Modality | Download size (GB) | Size on disk (GB) | Train+eval time (Hours) |
        |-----------------|----------|--------------------|-------------------|-------------------------|
        | iwildcam        | Image    | 11                 | 25                | 7                       |
        | camelyon17      | Image    | 10                 | 15                | 2                       |
        | rxrx1           | Image    | 7                  | 7                 | 11                      |
        | ogb-molpcba     | Graph    | 0.04               | 2                 | 15                      |
        | globalwheat     | Image    | 10                 | 10                | 2                       |
        | civilcomments   | Text     | 0.1                | 0.3               | 4.5                     |
        | fmow            | Image    | 50                 | 55                | 6                       |
        | poverty         | Image    | 12                 | 14                | 5                       |
        | amazon          | Text     | 7                  | 7                 | 5                       |
        | py150           | Text     | 0.1                | 0.8               | 9.5                     |
        
        While the `camelyon17` dataset is small and fast to train on, we advise against using it as the only dataset to prototype methods on, as the test performance of models trained on this dataset tend to exhibit a large degree of variability over random seeds.
        
        The image datasets (`iwildcam`, `camelyon17`, `fmow`, and `poverty`) tend to have high disk I/O usage. If training time is much slower for you than the approximate times listed above, consider checking if I/O is a bottleneck (e.g., by moving to a local disk if you are using a network drive, or by increasing the number of data loader workers). To speed up training, you could also disable evaluation at each epoch or for all splits by toggling `--evaluate_all_splits` and related arguments.
        
        ### Evaluating trained models
        We also provide an evaluation script that aggregates prediction CSV files for different replicates and reports on their combined evaluation. To use this, run:
        
        ```bash
        python examples/evaluate.py <predictions_dir> <output_dir> --root-dir <root_dir>
        ```
        
        where `<predictions_dir>` is the path to your predictions directory, `<output_dir>` is where the results JSON will be writte, and `<root_dir>` is the dataset root directory.
        The predictions directory should have a subdirectory for each dataset
        (e.g. `iwildcam`) containing prediction CSV files to evaluate; see our [submission guidelines](https://wilds.stanford.edu/submit/) for the format.
        The evaluation script will skip over any datasets that has missing prediction files.
        Any dataset not in `<root_dir>` will be downloaded to `<root_dir>`.
        
        ### Reproducibility
        We have an [executable version](https://wilds.stanford.edu/codalab) of our paper on CodaLab that contains the exact commands, code, and data for the experiments reported in our paper, which rely on these scripts. Trained model weights for all datasets can also be found there.
        All configurations and hyperparameters can also be found in the `examples/configs` folder of this repo, and dataset-specific parameters are in `examples/configs/datasets.py`.
        
        ## Using the WILDS package
        ### Data
        
        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.
        We discuss data loading in more detail in [#Data loading](#data-loading).
        
        ```py
        >>> from wilds import get_dataset
        >>> from wilds.common.data_loaders import get_train_loader
        >>> import torchvision.transforms as transforms
        
        # Load the full dataset, and download it if necessary
        >>> dataset = get_dataset(dataset='iwildcam', download=True)
        
        # Get the training set
        >>> train_data = dataset.get_subset('train',
        ...                                 transform=transforms.Compose([transforms.Resize((448,448)),
        ...                                                               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., as well as other metadata.
        
        ### 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 are helper objects that extract group annotations from metadata, allowing users to specify the grouping scheme in a flexible fashion.
        They are used to initialize group-aware data loaders (as discussed in [#Data loading](#data-loading)) and to implement algorithms that rely on domain annotations (e.g., Group DRO).
        In the following code snippet, we initialize and use a `Grouper` that extracts the domain annotations on the iWildCam dataset, where the domain is location.
        
        ```py
        >>> 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)
        ...   ...
        ```
        
        ### Data loading
        
        For training, the WILDS package provides two types of data loaders.
        The standard data loader shuffles examples in the training set, and is used for the standard approach of empirical risk minimization (ERM), where we minimize the average loss.
        ```py
        >>> from wilds.common.data_loaders import get_train_loader
        
        # Prepare the standard data loader
        >>> train_loader = get_train_loader('standard', train_data, batch_size=16)
        ```
        
        To support other algorithms that rely on specific data loading schemes, we also provide the group data loader.
        In each minibatch, the group loader first samples a specified number of groups, and then samples a fixed number of examples from each of those groups.
        (By default, the groups are sampled uniformly at random, which upweights minority groups as a result. This can be toggled by the `uniform_over_groups` parameter.)
        We initialize group loaders as follows, using `Grouper` that specifies the grouping scheme.
        
        ```py
        # 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)
        ```
        
        Lastly, we also provide a data loader for evaluation, which loads examples without shuffling (unlike the training loaders).
        
        ```py
        >>> 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 evaluation data loader
        >>> test_loader = get_eval_loader('standard', test_data, 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.
        
        ```py
        >>> 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, ...}
        ```
        Most `eval` methods take in predicted labels for `all_y_pred` by default, but the default inputs vary across datasets and are documented in the `eval` docstrings of the corresponding dataset class.
        
        ## Leaderboard
        If you are developing new training algorithms and/or models on WILDS, please consider submitting them to our [public leaderboard](https://wilds.stanford.edu/leaderboard/).
        
        ## Citing WILDS
        If you use WILDS datasets in your work, please cite [our paper](https://arxiv.org/abs/2012.07421) ([Bibtex](https://wilds.stanford.edu/assets/files/bibtex.md)):
        
        - **WILDS: A Benchmark of in-the-Wild Distribution Shifts** (2021). Pang Wei Koh*, Shiori Sagawa*, Henrik Marklund, Sang Michael Xie, Marvin Zhang, Akshay Balsubramani, Weihua Hu, Michihiro Yasunaga, Richard Lanas Phillips, Irena Gao, Tony Lee, Etienne David, Ian Stavness, Wei Guo, Berton A. Earnshaw, Imran S. Haque, 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](https://wilds.stanford.edu/datasets/).
        
        ## Acknowledgements
        The design of the WILDS benchmark was inspired by the [Open Graph Benchmark](https://ogb.stanford.edu/), and we are grateful to the Open Graph Benchmark team for their advice and help in setting up WILDS.
        
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Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Intended Audience :: Science/Research
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
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Description-Content-Type: text/markdown
