implementation of fGES (fast greedy equivalence search)
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- Dominant language
- Python
- Stars
- 1.7k
- Forks
- 274
- PR merge metrics
- No merged PRs in 30d
Description
Does this repo have plans to implement the algorithm fGES[1]? fGES seems to work well for large scale problems. I wanna do some work on a large scale problem. If there is a related plan, it will help to use fGES more conveniently on the python platform, instead of calling Tetrad implemented in Java.
[1] Ramsey J, Glymour M, Sanchez-Romero R, et al. A million variables and more: the fast greedy equivalence search algorithm for learning high-dimensional graphical causal models, with an application to functional magnetic resonance images[J]. International journal of data science and analytics, 2017, 3(2): 121-129.
Contributor guide
No contributing guide indexed for this repository
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
No repository files, tests, or entry points are identified. Start by reading the issue and the cited fGES paper, then review the existing causal-discovery implementations and how Tetrad exposes the algorithm. Done means a Python implementation is available for large-scale problems with scope and validation agreed by the maintainers.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- java, python
- Domain
- machine-learning
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Quiet
- Clarity
- Needs clarification
- Newbie friendliness
- 35/100