https://github.com/deepcausality-rs/deep_causality
Hyper-geometric computational causality library for Rust
https://github.com/deepcausality-rs/deep_causality
causality causality-algorithms causality-analysis causality-test rust rust-lang
Last synced: about 2 months ago
JSON representation
Hyper-geometric computational causality library for Rust
- Host: GitHub
- URL: https://github.com/deepcausality-rs/deep_causality
- Owner: deepcausality-rs
- License: other
- Created: 2023-06-18T10:08:15.000Z (almost 2 years ago)
- Default Branch: main
- Last Pushed: 2024-03-20T02:47:41.000Z (about 1 year ago)
- Last Synced: 2024-05-14T23:27:12.671Z (about 1 year ago)
- Topics: causality, causality-algorithms, causality-analysis, causality-test, rust, rust-lang
- Language: Rust
- Homepage: https://deepcausality.com/
- Size: 6.88 MB
- Stars: 123
- Watchers: 0
- Forks: 7
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
- Codeowners: CODEOWNERS
- Security: SECURITY.md
- Support: SUPPORT.md
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README
[//]: # (---)
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[![DeepCausality Logo][image]][hyperlink]
[hyperlink]: https://deepcausality.com
[image]: /img/logo_color.png (DeepCausality Logo)
Web: https://deepcausality.com
DeepCausality is a hyper-geometric computational causality library that enables fast and deterministic context-aware
causal reasoning over complex multi-stage causality models. Deep Causality adds only minimal overhead and thus is
suitable for real-time applications without additional acceleration hardware.
DeepCausality is hosted as a sandbox project in the [LF AI & Data Foundation](https://landscape.lfai.foundation/).
## 🤔 Why DeepCausality?
1) DeepCausality is written in Rust with safety, reliability, and performance in mind.
2) DeepCausality provides recursive causal data structures that concisely express arbitrary complex causal
structures.
3) DeepCausality enables context awareness across data-like, time-like, space-like, spacetime-like entities stored
within (multiple) context-hyper-graphs.
4) DeepCausality simplifies modeling of complex tempo-spatial patterns.
5) DeepCausality comes with Causal State Machine (CSM)
## 📚 Docs
* [API Docs](https://docs.rs/deep_causality/0.2.4/deep_causality/)
* [Documentation](https://deepcausality.com/docs/intro/)
* [Changelog](CHANGELOG.md)
* [Slides](docs/slides/LF_2023/DeepCausality.pdf)
## 🚀 Getting Started
In your project folder, just run in a terminal:
```bash
cargo add deep_causality
```
See the [starter example](https://deepcausality.com/getting-started/).
## How to run the example code
```bash
git clone https://github.com/deepcausality-rs/deep_causality.git
cd deep_causality
make example
```
You can also run the example code from the project root with cargo:
```bash
# make sure you're in the project root folder
cd deep_causality
# CSM (Causal State Machine)
cargo run --release --bin example-csm
# CTX (Context)
cargo run --release --bin example-ctx
# Smoking inference
cargo run --release --bin example-smoking
# Getting started example
cargo run --release --bin starter
```
## 📦 Sub-Crates
* [Datastructures](https://github.com/deepcausality-rs/deep_causality/tree/main/dcl_data_structures/README.md)
* [Ultragraph](https://github.com/deepcausality-rs/deep_causality/tree/main/ultragraph/README.md)
* [Macros](https://github.com/deepcausality-rs/deep_causality/tree/main/deep_causality_macros/README.md)
## ⭐ Usage
Docs:
* [Introduction](https://deepcausality.com/docs/intro/)
* [Architecture](https://deepcausality.com/docs/architecture/)
* [Background](https://deepcausality.com/docs/background/)
* [Concepts](https://deepcausality.com/docs/concepts/)
Code:
* [Benchmarks](deep_causality/benches/benchmarks)
* [Example code](deep_causality_examples)
* [Tests](deep_causality/tests)
## 🛠️ Cargo & Make
Cargo works as expected, but in addition to cargo, a makefile exists
that abstracts over several additional tools you may have to install
before all make commands work. To do so, please run the following command:
```bash
make install
```
The make install command tests and tries to install all required developer dependencies.
if the automatic install fails, the script will show a link with further installation instructions.
After all dependencies have been installed, the following commands are ready to use.
```bash
make build Builds the code base incrementally (fast) for dev.
make bench Runs all benchmarks across all crates.
make check Checks the code base for security vulnerabilities.
make example Runs the example code.
make fix Fixes linting issues as reported by clippy
make format Formats call code according to cargo fmt style
make install Tests and installs all make script dependencies
make start Starts the dev day with updating rust, pulling from git remote, and build the project
make test Runs all tests across all crates.
```
The scripts called by each make command are located in the [script folder.](scripts)
## 👩👩👧👦 Community
* [Charta](DeepCausalityProjectCharter.pdf)
* [Code of Conduct](CODE_OF_CONDUCT.md)
* [Support](SUPPORT.md)
## 👨💻👩💻 Contribution
Contributions are welcomed especially related to documentation, example code, and fixes.
If unsure where to start, open an issue and ask. For more significant code contributions,
please run make test and make check locally before opening a PR.
Unless you explicitly state otherwise, any contribution intentionally submitted for inclusion in deep_causality by you,
shall be licensed under the MIT license without additional terms or conditions.
For details:
* [Code of Conduct](CODE_OF_CONDUCT.md)
* [Contributing](CONTRIBUTING.md)
* [Release](RELEASE.md)
## 🙏 Credits
The project took inspiration from several researchers and their projects in the field:
* [Judea Pearl](http://bayes.cs.ucla.edu/jp_home.html) at UCLA
* [Lucien Hardy](https://perimeterinstitute.ca/people/lucien-hardy) at the Perimeter Institute
* [Kenneth O. Stanley](https://www.kenstanley.net/home) at OpenAI
* [Ilya Shpitser](https://www.cs.jhu.edu/~ilyas/) at Johns Hopkins University
* [Miguel Hernan](https://www.hsph.harvard.edu/miguel-hernan/), [Causal Lab](https://causalab.sph.harvard.edu/) at
Harvard University
* [Elias Bareinboim](https://causalai.net/) at Columbia University
* [Causality and Machine Learning](https://www.microsoft.com/en-us/research/group/causal-inference/) at Microsoft
Research
* [Causal ML](https://github.com/uber/causalml) at uber.
Parts of the implementation are inspired by:
* [Differentiable Types](https://github.com/tensorflow/swift/blob/main/docs/DifferentiableTypes.md)
* [Extension Trait](http://xion.io/post/code/rust-extension-traits.html)
* [Storage API](https://github.com/petgraph/petgraph/issues/563)
* [gTime: Time Graph](https://youtu.be/dIeYjLtg6s4)
Finally, inspiration, especially related to the hypergraph structure, was derived from reading
the [Quanta Magazine](https://www.quantamagazine.org/).
## 📜 Licence
This project is licensed under the [MIT license](LICENSE).
## 👮️ Security
For details about security, please read
the [security policy](https://github.com/deepcausality-rs/deep_causality/blob/main/SECURITY.md).
## 🎁 Sponsors
[JetBrains](https://www.jetbrains.com/), the premier software development tool provider, has granted a
free [all-product license](https://www.jetbrains.com/all/) under
its [open-source community support program](https://www.jetbrains.com/community/opensource/#support) to the
DeepCausality project. The project team expresses its gratitude towards JetBrains generous contribution. Thank you for
your commitment to OSS development!
## 💻 Author
* [Marvin Hansen](https://github.com/marvin-hansen).
* Github GPG key ID: 369D5A0B210D39BC
* GPG Fingerprint: 4B18 F7B2 04B9 7A72 967E 663E 369D 5A0B 210D 39BC