Upgrades to PyMC compatibility #61
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@@ Coverage Diff @@
## master #61 +/- ##
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- Coverage 97.49% 97.23% -0.26%
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Files 6 7 +1
Lines 919 905 -14
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- Hits 896 880 -16
- Misses 23 25 +2
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This also refactors the way how log-likelihood graphs are created, because the API for that changed between v4 and v5. Also re-runs some example notebooks.
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omg, finally it's green 😌 |
lhelleckes
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Dec 14, 2022
calibr8/core.py
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| if pm.__version__[0] == "4": | ||
| return pm.joint_logp(rv, y, sum=True) | ||
| elif int(pm.__version__[0]) >= 5: | ||
| return pm.logprob.joint_logprob({rv: y_tensor}, sum=True) |
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So in PyMC v5 this is only working with tensor inputs?
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Yes, the previous pm.joint_logp was a wrapper that apparently did this automatically..
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lhelleckes
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lhelleckes
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| if unexpected_rv_nodes: | ||
| raise ValueError( | ||
| f"Random variables detected in the logp graph: {unexpected_rv_nodes}.\n" | ||
| "This can happen when DensityDist logp or Interval transform functions " |
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I am not sure anybody except you or maybe me could fix this in case of an error, but I guess that's ok.
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Major changes
pymc3compatibilitypymc==4.4.0pymc==5.0.0Maintenance