torch-spatiotemporal

tsl: a PyTorch library for processing spatiotemporal data.

https://github.com/torchspatiotemporal/tsl

Science Score: 44.0%

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Low similarity (11.6%) to scientific vocabulary

Keywords

deep-learning gnn graph-neural-networks pytorch spatio-temporal spatio-temporal-analysis spatio-temporal-data spatio-temporal-graph spatio-temporal-prediction spatiotemporal spatiotemporal-data spatiotemporal-data-analysis spatiotemporal-forecasting temporal-data temporal-graphs

Last synced: 6 months ago · JSON representation ·

Repository

tsl: a PyTorch library for processing spatiotemporal data.

Basic Info

Host: GitHub
Owner: TorchSpatiotemporal
License: mit
Language: Python
Default Branch: main
Homepage: https://torch-spatiotemporal.readthedocs.io/
Size: 977 KB

Statistics

Stars: 347
Watchers: 9
Forks: 37
Open Issues: 24
Releases: 8

Topics

Created almost 4 years ago · Last pushed 8 months ago

Metadata Files

Readme Contributing License Code of conduct Citation

README.md

Neural spatiotemporal forecasting with PyTorch

🚀 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:

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

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

bash pip install torch-spatiotemporal

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

bash 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

latex @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.

Owner

Name: TorchSpatiotemporal
Login: TorchSpatiotemporal
Kind: organization

Repositories: 1
Profile: https://github.com/TorchSpatiotemporal

Citation (CITATION.cff)

cff-version: 1.2.0
message: "If you use this code in your work, please cite it as below."
authors:
  - family-names: "Cini"
    given-names: "Andrea"
  - family-names: "Marisca"
    given-names: "Ivan"
title: "Torch Spatiotemporal"
date-released: 2022-03-16
license: MIT
url: "https://github.com/TorchSpatiotemporal/tsl"

GitHub Events

Total

Issues event: 8
Watch event: 75
Delete event: 1
Issue comment event: 3
Push event: 21
Pull request event: 6
Fork event: 13
Create event: 1

Last Year

Issues event: 8
Watch event: 75
Delete event: 1
Issue comment event: 3
Push event: 21
Pull request event: 6
Fork event: 13
Create event: 1

Committers

Last synced: 9 months ago

All Time

Total Commits: 287
Total Committers: 11
Avg Commits per committer: 26.091
Development Distribution Score (DDS): 0.397

Past Year

Commits: 24
Committers: 4
Avg Commits per committer: 6.0
Development Distribution Score (DDS): 0.292

Top Committers

Name	Email	Commits
marshka	i**a@h**t	173
andreacini	a**i@u**h	76
Stefano Imoscopi	s**i@u**h	21
Daniele Zambon	d**n@u**h	5
torch-spatiotemporal	1****l	4
ascarrambad	m**a@m**m	2
Javier Solís García	j**i@g**m	2
Luca Butera	2****a	1
Fyodor Velikonivtsev	m**0@g**m	1
Filippo Maria Bianchi	f**i@u**o	1
steve3nto	s**i@g**i	1

Committer Domains (Top 20 + Academic)

usi.ch: 3 graphsight.ai: 1 uit.no: 1 me.com: 1 hotmail.it: 1

Issues and Pull Requests

Last synced: 6 months ago

All Time

Total issues: 45
Total pull requests: 11
Average time to close issues: 28 days
Average time to close pull requests: 17 days
Total issue authors: 29
Total pull request authors: 9
Average comments per issue: 1.58
Average comments per pull request: 0.36
Merged pull requests: 7
Bot issues: 0
Bot pull requests: 0

Past Year

Issues: 17
Pull requests: 3
Average time to close issues: about 2 months
Average time to close pull requests: 2 months
Issue authors: 13
Pull request authors: 3
Average comments per issue: 1.06
Average comments per pull request: 0.0
Merged pull requests: 2
Bot issues: 0
Bot pull requests: 0

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Top Authors

Issue Authors

LucaButera (6)
StefanBloemheuvel (4)
ucsky (4)
agoodge (2)
Mr-Nobody-dey (2)
LaurentBerder (2)
tboussaid (2)
thefriedbee (2)
gcoyle83 (1)
mrluin (1)
viktor-ktorvi (1)
b2jia (1)
ggmirandac (1)
Abusagit (1)
Anjum48 (1)

Pull Request Authors

LucaButera (3)
FTi130 (2)
FilippoMB (2)
marshka (2)
Abusagit (2)
arahosu (1)
javiersgjavi (1)
steve3nto (1)
dzambon (1)
MicheleCattaneo (1)

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bug (1) help wanted (1)

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Packages

Total packages: 3
Total downloads:
- pypi 313 last-month

Total dependent packages: 0
(may contain duplicates)
Total dependent repositories: 1
(may contain duplicates)
Total versions: 24
Total maintainers: 1

proxy.golang.org: github.com/TorchSpatiotemporal/tsl

Documentation: https://pkg.go.dev/github.com/TorchSpatiotemporal/tsl#section-documentation
License: mit
Latest release: v0.9.5
published over 1 year ago

Versions: 8
Dependent Packages: 0
Dependent Repositories: 0

Rankings

Stargazers count: 3.5%

Forks count: 4.4%

Average: 4.8%

Dependent packages count: 5.4%

Dependent repos count: 5.7%

Last synced: 6 months ago

proxy.golang.org: github.com/torchspatiotemporal/tsl

Documentation: https://pkg.go.dev/github.com/torchspatiotemporal/tsl#section-documentation
License: mit
Latest release: v0.9.5
published over 1 year ago

Versions: 8
Dependent Packages: 0
Dependent Repositories: 0

Rankings

Stargazers count: 3.5%

Forks count: 4.4%

Average: 4.8%

Dependent packages count: 5.4%

Dependent repos count: 5.7%

Last synced: 6 months ago

pypi.org: torch-spatiotemporal

A PyTorch library for spatiotemporal data processing

Homepage: https://github.com/TorchSpatiotemporal/tsl
Documentation: https://torch-spatiotemporal.readthedocs.io/
License: MIT
Latest release: 0.9.5
published over 1 year ago

Versions: 8
Dependent Packages: 0
Dependent Repositories: 1
Downloads: 313 Last month

Rankings

Stargazers count: 5.7%

Forks count: 9.1%

Dependent packages count: 10.0%

Average: 11.9%

Downloads: 13.1%

Dependent repos count: 21.7%

Maintainers (1)

torchspatiotemporal

Last synced: 6 months ago

Dependencies

docs/requirements.txt pypi

sphinx ==4.2.0
sphinx_rtd_theme ==0.5.2

requirements.txt pypi

PyYAML *
einops *
numpy *
pandas *
pytorch_lightning >=1.5
scikit_learn *
scipy *
tables *
test_tube *
torch >=1.9
torch_geometric >=2.0
torchaudio *
torchmetrics >=0.7
torchvision *
tqdm *

setup.py pypi

PyYAML *
einops *
numpy *
pandas *
pytorch_lightning >=1.5
scikit_learn *
scipy *
tables *
test_tube *
torch >=1.9
torch_geometric >=2.0
torchmetrics >=0.7
tqdm *