torch-spatiotemporal 0.9.5

A PyTorch library for spatiotemporal data processing

Neural spatiotemporal forecasting with PyTorch

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🚀 Getting Started - 📚 Documentation - 💻 Introductory notebook

tsl (Torch Spatiotemporal) is a library built to accelerate research on neural spatiotemporal data processing methods, with a focus on Graph Neural Networks.

Built upon popular libraries such as PyTorch, PyG (PyTorch Geometric), and PyTorch Lightning, tsl provides a unified and user-friendly framework for efficient neural spatiotemporal data processing, that goes from data preprocessing to model prototyping.

Features

• Create Custom Models and Datasets   Easily build your own custom models and datasets for spatiotemporal data analysis. Whether you're working with sensor networks, environmental data, or any other spatiotemporal domain, tsl's high-level APIs empower you to develop tailored solutions.

• Access a Wealth of Existing Datasets and Models   Leverage a vast collection of datasets and models from the spatiotemporal data processing literature. Explore and benchmark against state-of-the-art baselines, and test your brand new model on widely used public datasets.

• Handle Irregularities and Missing Data   Seamlessly manage irregularities in your spatiotemporal data streams, including missing data and variations in network structures. Ensure the robustness and reliability of your data processing pipelines.

• Streamlined Preprocessing   Automate the preprocessing phase with tsl's methods for scaling, resampling and clustering time series. Spend less time on data preparation and focus on extracting meaningful patterns and insights.

• Efficient Data Structures   Utilize tsl's straightforward data structures, seamlessly integrated with PyTorch and PyG, to accelerate your workflows. Benefit from the flexibility and compatibility of these widely adopted libraries.

• Scalability with PyTorch Lightning   Scale your computations effortlessly, from a single CPU to clusters of GPUs, with tsl's integration with PyTorch Lightning. Accelerate training and inference across various hardware configurations.

• Modular Neural Layers   Build powerful and modular neural spatiotemporal models using tsl's collection of specialized layers. Create architectures with ease, leveraging the flexibility and extensibility of the library.

• Reproducible Experiments   Ensure experiment reproducibility using the Hydra framework, a standard in the field. Validate and compare results confidently, promoting rigorous research in spatiotemporal data mining.

Getting Started

Before you start using tsl, please review the documentation to get an understanding of the library and its capabilities.

You can also explore the examples provided in the examples directory to see how train deep learning models working with spatiotemporal data.

Installation

Before installing tsl, make sure you have installed PyTorch (>=1.9.0) and PyG (>=2.0.3) in your virtual environment (see PyG installation guidelines). tsl is available for Python>=3.8. We recommend installation from github to be up-to-date with the latest version:

pip install git+https://github.com/TorchSpatiotemporal/tsl.git

Alternatively, you can install the library from the pypi repository:

pip install torch-spatiotemporal

To avoid dependencies issues, we recommend using Anaconda and the provided environment configuration by running the command:

conda env create -f conda_env.yml

Tutorial

The best way to start using tsl is by following the tutorial notebook in examples/notebooks/a_gentle_introduction_to_tsl.ipynb.

Documentation

Visit the documentation to learn more about the library, including detailed API references, examples, and tutorials.

The documentation is hosted on readthedocs. For local access, you can build it from the docs directory.

Contributing

Contributions are welcome! For major changes or new features, please open an issue first to discuss your ideas. See the Contributing guidelines for more details on how to get involved. Help us build a better tsl!

Thanks to all contributors! 🧡

Citing

If you use Torch Spatiotemporal for your research, please consider citing the library

@software{Cini_Torch_Spatiotemporal_2022,
    author = {Cini, Andrea and Marisca, Ivan},
    license = {MIT},
    month = {3},
    title = {{Torch Spatiotemporal}},
    url = {https://github.com/TorchSpatiotemporal/tsl},
    year = {2022}
}

By Andrea Cini and Ivan Marisca.

License

This project is licensed under the terms of the MIT license. See the LICENSE file for details.