scikit-learn / scikit-learn/scikit-learn
Add individual penalization to precision matrix in graphical_lasso.py
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Description
Describe the workflow you want to enable
Friedman et al. (2008) describe the coordinate descent procedure used for the graphical lasso.
In the paper, there is a REMARK 2.1, which states that the objective function to be optimized can be modified to allow for a matrix of penalty values, rather than a scalar value.
Describe your proposed solution
This has been implemented here.
However, linear_model._cd_fast.pyx still has to be updated to allow for vectorized alpha input in enet_coordinate_descent_gram
Describe alternatives you've considered, if relevant
No response
Additional context
This change is motivated to allow for prior incorporation into the inference procedure. When strong priors for edges are available, this can affect the strength of the corresponding edges' penalization.
Contributor guide
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
Start with graphical_lasso.py and the referenced coordinate-descent procedure in Friedman et al. (2008), then inspect linear_model._cd_fast.pyx, especially enet_coordinate_descent_gram. Review the linked implementation for the proposed matrix penalty behavior. Done means supporting vectorized alpha input and individual penalization in the precision matrix.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- machine-learning
- Issue type
- Feature
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Mostly clear
- Newbie friendliness
- 38/100