Verbose logging in LinearDML and SparseLinearDML
Nobody has claimed this yet.
- Dominant language
- Jupyter Notebook
- Stars
- 4.8k
- Forks
- 827
- PR merge metrics
- No merged PRs in 30d
Description
I'm trying to fit LinearDML and SparseLinearDML on a marketing data set with 350,000 examples, 25 real valued treatment variables, and 50 nuisance variables. The fitting takes a long time (and eventually gives warnings - more about that in another issue if I can't figure it out). For now my question is if there is any way to get logging? I understand there are multiple stages in the fitting process (multiple models to fit), and I don't know what takes so long. LinearDML and SparseLinearDML models don't seem to accept the verbose parameters listed in the DML base class. Thanks for your help!
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
Start by tracing the LinearDML and SparseLinearDML entry points and comparing their accepted parameters with the verbose parameters in the DML base class. Identify the fitting stages that need visibility and define completion as providing useful logging for those stages without relying on undocumented behavior.
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
- 25/100