Raise ValueError if random variables are present in the logp graph #5614
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@@ Coverage Diff @@
## main #5614 +/- ##
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Coverage 87.63% 87.64%
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Files 76 76
Lines 13717 13722 +5
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+ Hits 12021 12026 +5
Misses 1696 1696
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ricardoV94
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| N = 100 | ||
| with pm.Model() as model: | ||
| mu = pm.Normal("mu", 0, 1) | ||
| normal_dist = pm.Normal.dist(mu, 1, size=N) | ||
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| def logp(x): | ||
| def logp(x, mu): | ||
| normal_dist = pm.Normal.dist(mu, 1, size=N) | ||
| out = pm.logp(normal_dist, x) | ||
| return out | ||
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| obs = pm.DensityDist("density_dist", logp=logp, observed=np.random.randn(N), size=N) | ||
| obs = pm.DensityDist("density_dist", mu, logp=logp, observed=np.random.randn(N), size=N) |
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This is a perfect illustration of the problem that's detected by this PR
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michaelosthege
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Aeppl allows for graphs containing random variables. PyMC models do not generally allow for this, with the current exception of models that include SimulatorRVs.
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Failing test seems unrelated |
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Aeppl allows for graphs containing random variables. PyMC models do not generally allow for this, with the current exception of models that include SimulatorRVs.
This PR adds a check to avoid subtle bugs when RandomVariables creep in into the logp unexpectedly, which can happen when mis-using DensityDist or Interval transforms.
Closes #5155