Support for custom priors via Prior class #488
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This is very cool!
As far as I can tell, these changes are currently entirely invisible to the end user, right?
Before we do a new release I will follow up with another PR that adds some documentation. This will most likely be in the form of just giving a couple of worked examples
Main request is to add custom-ness to the Dirichlet for the WeightedSumFitter class. Like I say in a comment, I mostly see people wanting to customise the hyperparams, not the distribution itself.
| @@ -237,6 +248,11 @@ class LinearRegression(PyMCModel): | |||
| Inference data... | |||
| """ # noqa: W605 | |||
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| default_priors = { | |||
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need to add @property decorator here? Or is that remembered from it being done in the PyMCModel base class?
Getting an Pylance warning: Type "dict[str, Prior]" is not assignable to declared type "property"
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What line of code bring that on? Maybe having a setter will help?
causalpy/pymc_models.py
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| @@ -286,9 +305,8 @@ def build_model(self, X, y, coords): | |||
| X = pm.Data("X", X, dims=["obs_ind", "coeffs"]) | |||
| y = pm.Data("y", y[:, 0], dims="obs_ind") | |||
| beta = pm.Dirichlet("beta", a=np.ones(n_predictors), dims="coeffs") | |||
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We definitely want a custom prior for the Dirichlet. I think the Dirichlet would be used always (or nearly always), but there are plenty of real world use cases where the user might want to change the hyper parameters (currently a=np.ones(n_predictors)).
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Alright. since it is function of data, we will have to handle differently
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I've address this with dc20e3e. If user wants to change, they can add to the "priors" at initialization.
Take a look and let me know what you think.
Codecov ReportAll modified and coverable lines are covered by tests ✅
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## main #488 +/- ##
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+ Coverage 94.40% 94.62% +0.21%
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Files 29 28 -1
Lines 2075 2064 -11
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tests pass :) |
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Excited about this PR! |
This using
pymc-extrasto allow for custom priors. Might need to adjust the remaining a bit to work.