Start a contrib.epidemiology module #2437
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| # Sample initial values. | ||
| state = self.initialize(params) | ||
| state = {i: torch.tensor(float(value)) for i, value in state.items()} |
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what is this for exactly?
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self.initialize currently returns deterministic initial states. Because those states are used in torch.nn.functional.pad they must be Python scalars. However in this function those initial states need to start out as tensors.
The state dict in this function acts similarly to the state dict in SMCFilter: it is a framework-managed storage location where users can read and write values. Note the self.transition_fwd(params, state, t) call below updates this state dict in-place.
| logp = pyro.distributions.hmm._sequential_logmatmulexp(logp) | ||
| logp = logp.reshape(-1).logsumexp(0) | ||
| warn_if_nan(logp) | ||
| pyro.factor("transition", logp) |
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what happened to the previous -log(4)?
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I have neglected that from this model because (1) it does not affect inference (it does not change gradients), and (2) we will probably be moving to stochastic initial state soon anyway.
Addresses #2426
This refactors examples/sir_hmc.py into a more reusable framework that separates (1) clear user-facing model specification from (2) intricate effect handler code for inference and prediction. I have preserved the existing examples/sir_hmc.py as a minipyro-like concrete explanation of the new module; it thus serves as architecture documentation. This also makes small changes to sir_hmc.py to keep the two versions aligned (the old concrete and new abstract versions).
Whereas
sir_hmc.pycontains four models, I was able to reduce user-facing modeling code down to a few methods and only a single duplication: we need a forward.transition_fwd()method and also a.transition_bwd()method; these contain reverse versions of dynamic equations and cannot easily be automatically generated.After this PR I plan to simplify sir_hmc.py by moving all the fancy DCT and spline stuff into the new module.
Tested
sir_hmc.py