Implement non-blocked sampling for array based samplers. #587
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Note that this only samples in a non-blocked way on separate RVs. If a RV is multidimensional it will still update those in a blocked way. I have some experimental code that also applies the sampler to each dimension individually. This might be useful as the pymc3 style of creating models is with a vector based syntax rather than for-loops. |
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@jsalvatier Just found this: Could this allow for an alternative implementation? Would still have to loop over |
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| class ArrayStep(object): | |||
| def __init__(self, vars, fs, allvars=False): | |||
| def __new__(cls, *args, **kwargs): | |||
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I don't really like using new on something like this, seems confusing and complicated. Is there something simpler we could do?
Maybe we could just have a function, elemwise_sampler(vars, sampler, args, kwargs) or something?
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I agree that it's adding quite a bit of complexity. The other alternative I saw was #450 and having separate classes. Having a special elemwise_sampler function seems similar.
I reconsidered this approach though as I think the interface and UX is more important than the implementation.
I also tried to have the element wise iteration over dimensions/RVs be done inside of sample() but it e.g. didn't work with Metropolis because scaling was still the original/blocked dimensionality.
I'm not sure what other way there is to get a simple blocked=False kwarg without a large refactoring.
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@jsalvatier Do you have another idea for implementing this that has the same UX? I don't think it's ideal but personally can't think of a better way.
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@twiecki "Could this allow for an alternative implementation? Would still have to loop over vars in ArrayStep.step() though, right?" I don't quite get what you're asking. |
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I'm gonna go ahead and merge this soon since there haven't been any alternative ideas and I think it just improves the status quo. |
Implement non-blocked sampling for array based samplers.
Before, all our samplers operated in the joint space (i.e. blocked) by default. This often leads to bad convergence for metropolis or slice.
This adds functionality to set
blocked=Falsewhen instantiating any sampler which will instead create a step-method for each RV and append them to aCompoundStep.Samplers like metropolis and slice default to not being blocked (as was the case in pymc2 and is what people assume). HMC and NUTS however default to being blocked as they are specifically designed to operate in the joint space.
Unfortunately because of all the configurations
ArrayStepcould be called there's quite some code to determine the configuration it was called in but it's fully backwards compatible that way.This supersedes #450 which took a different approach.