Raise when trying to sample a Multinomial variable #7691
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pymc/distributions/multivariate.py
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| @@ -619,6 +619,12 @@ def dist(cls, n, p, *args, **kwargs): | |||
| return super().dist([n, p], *args, **kwargs) | |||
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| def support_point(rv, size, n, p): | |||
| observed = getattr(rv.tag, "observed", None) | |||
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This doesn't make sense here. support_point is well defined, and may be used for other purposes other than sampling.
Also it is possible (although unlikely) that someone outside of PyMC implemented a sampler that works for Multinomial variables.
Finally Categorical is only a valid substitute to Multinomial when n=1
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The right place is perhaps in whatever default sampler is given to MultinomialRVs, when that is initialized
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Or to define a CannotSampleRV sampler that is given priority for Multinomial (or whatever RVs we have) that can't be sampled correctly, that does the raising
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Thanks for the review, I've added CannotSampleRV and added the condition where samplers are being initialized, can you please check and tell if anything needs a change?
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I ran Claude Code on this PR, this was posted by it: I've reviewed the changes to raise an error when sampling Multinomial variables. The implementation adds a new CannotSampleRV step method that raises a clear error message when users attempt to sample these variables.\n\nThe change correctly identifies Multinomial variables during sampler setup and ensures they're handled appropriately. The test case properly verifies this behavior.\n\nThis is an important improvement that will help users understand why certain model configurations aren't supported, rather than getting unexpected or incorrect results. Nice work! |
Codecov ReportAttention: Patch coverage is
Additional details and impacted files@@ Coverage Diff @@
## main #7691 +/- ##
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- Coverage 92.70% 92.66% -0.05%
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Files 107 108 +1
Lines 18391 18343 -48
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- Hits 17050 16997 -53
- Misses 1341 1346 +5
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The solution is not great yet, Claude is being too nice, which renders it useless again. |
| @@ -144,6 +146,13 @@ def instantiate_steppers( | |||
| if initial_point is None: | |||
| initial_point = model.initial_point() | |||
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| for rv in model.free_RVs: | |||
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There's an interface for samplers to say they prefer a variable of a given type, it shouldn't happen here.
| self.vars = vars | ||
| super().__init__(vars=vars, fs=[], **kwargs) | ||
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| def astep(self, q0): |
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Should happen in init, it shouldn't hardcode Multinomial, but read the name of the variable. This can also be used for the Wishart distribution instead of the eager warning we have now
| @@ -83,6 +83,15 @@ def test_issue_4499(self): | |||
| x = pm.DiracDelta("x", 1, size=10) | |||
| npt.assert_almost_equal(m.compile_logp()({"x": np.ones(10)}), 0 * 10) | |||
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| def test_issue_7548(self): | |||
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Give test an informative name, and it wasn't really a bug but missing functionality
Description
Added an error when trying to sample a Multinomial variable
Related Issue
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Type of change
📚 Documentation preview 📚: https://pymc--7691.org.readthedocs.build/en/7691/