Misc fixes to contrib.epidemiology #2527
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@fritzo any hypothesis as to why? |
Yes, I believe there is a bias-variance tradeoff where very small |
| # Try to initialize kernel.transforms using kernel.setup(). | ||
| if getattr(self.kernel, "transforms", None) is None: | ||
| warmup_steps = 0 | ||
| self.kernel.setup(warmup_steps, *args, **kwargs) | ||
| # Use `kernel.transforms` when available | ||
| if hasattr(self.kernel, 'transforms') and self.kernel.transforms is not None: | ||
| if getattr(self.kernel, "transforms", None) is not None: |
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This is required because manually calling initialize_model() below would ignore init_strategy and attempt to sample from ImproperUniform. Instead we let kernel.setup() call initialize_model() with proper arguments.
| @@ -92,16 +93,17 @@ def hook_fn(kernel, *unused): | |||
| warmup_steps=args.warmup_steps, | |||
| num_samples=args.num_samples, | |||
| num_chains=args.num_chains, | |||
| mp_context="spawn" if parallel else None, | |||
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unfortunately i've found this to be somewhat buggy (fair number of crashes)
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do you use "forkserver" instead?
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no i've used spawn. but i've tried to avoid it...
|
Phew 😅 thanks for reviewing! |
Addresses #2426
This implements three fixes in preparation for a tutorial notebook:
heuristic()to a method._heuristic().._concat_series()._RELAX_MIN_VARIANCEvariable, and lowers the value from 0.25 to 0.1, which seems to improve inference. (I may change again in the future, but future changes will be one-line changes rather than 6-line changes across 2 files).Tested
num_chains=1