Vs module #568
Vs module #568
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Signed-off-by: Nathaniel <[email protected]>
Signed-off-by: Nathaniel <[email protected]>
| :param vs_prior_type : str or None, default=None | ||
| Type of variable selection prior: 'spike_and_slab', 'horseshoe', or None. | ||
| If None, uses standard normal priors. | ||
| :param vs_hyperparams : dict, optional |
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This is sphinx format and not numpy
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We should add that into AGENTS.md if it's not already there.
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This is sphinx format and not numpy
You got me. The doc strings were AI generated. Will fix.
| Provides continuous shrinkage with heavy tails, allowing strong signals | ||
| to escape shrinkage while weak signals are dampened: | ||
| β_j = τ · λ̃_j · β_j^raw |
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We should be able to add maths in here for nice rendering in the API docs
Signed-off-by: Nathaniel <[email protected]>
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Check out this pull request on See visual diffs & provide feedback on Jupyter Notebooks. Powered by ReviewNB |
Signed-off-by: Nathaniel <[email protected]>
Signed-off-by: Nathaniel <[email protected]>
Signed-off-by: Nathaniel <[email protected]>
Signed-off-by: Nathaniel <[email protected]>
Codecov Report❌ Patch coverage is
Additional details and impacted files@@ Coverage Diff @@
## main #568 +/- ##
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+ Coverage 91.95% 92.27% +0.31%
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Files 33 35 +2
Lines 4776 5050 +274
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+ Hits 4392 4660 +268
- Misses 384 390 +6 ☔ View full report in Codecov by Sentry. 🚀 New features to boost your workflow:
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Signed-off-by: Nathaniel <[email protected]>
Signed-off-by: Nathaniel <[email protected]>
Signed-off-by: Nathaniel <[email protected]>
Signed-off-by: Nathaniel <[email protected]>
Signed-off-by: Nathaniel <[email protected]>
Signed-off-by: Nathaniel <[email protected]>
Signed-off-by: Nathaniel <[email protected]>
Signed-off-by: Nathaniel <[email protected]>
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Marking this one as ready for review. There is still some work to be done on the notebook illustrating the functionality. But i think there is enough here that's it worth flagging the architecture choices for discussion. I've made the variable selection priors available as a module. Currently, just integrated with the IV class, but in principle can be dropped into all regression based modules with coefficients. The pattern simply requires an if-else block to be used in e.g. the propensity score model, linear regression model etc.... What do you guys think? |
Signed-off-by: Nathaniel <[email protected]>
3 participants
Just a draft for the minute. Working through some ideas.
📚 Documentation preview 📚: https://causalpy--568.org.readthedocs.build/en/568/