Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0, tolerance: 0.0
Open
Nobody has claimed this yet.
- Dominant language
- Jupyter Notebook
- Stars
- 4.8k
- Forks
- 827
- PR merge metrics
- No merged PRs in 30d
Description
The error on the title appears when I fit the following model:
est = SparseLinearDML(model_y= ElasticNetCV(cv=[(fold00, fold11), (fold11, fold00)],fit_intercept=False,max_iter=10000,n_alphas = 500),
model_t= MultiTaskElasticNetCV(cv=[(fold00, fold11), (fold11, fold00)],max_iter=10000,n_alphas = 500),
n_splits = [(fold0, fold1), (fold1, fold0)],
linear_first_stages=False,
max_iter=10000)
I actually increased a lot the number of iterations so I don't know what's wrong. Can you please help me figuring out this?
Thank you
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 reproducing the reported SparseLinearDML configuration with ElasticNetCV and MultiTaskElasticNetCV, including the custom cross-validation folds and iteration settings. Trace where the convergence error is surfaced and compare the failing configuration with a simpler model; done means identifying a reproducible cause and documenting or implementing a project-appropriate resolution.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python, scikit-learn
- Domain
- machine-learning
- Issue type
- Bug
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100