Implementation of replica exchange MCMC #865
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I apologize. I'll fix it. |
edward/inferences/__init__.py
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| from edward.inferences.variational_inference import * | |||
| from edward.inferences.wake_sleep import * | |||
| from edward.inferences.wgan_inference import * | |||
| from edward.inferences.replica_exchange_mc import * | |||
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can you add where the imports are in alphabetical order?
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OK. I'll fix.
| from edward.util import check_latent_vars, copy | ||
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| class memory_lambda: |
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For implementation names, we recommend privates: i.e., _memory_lambda (or _MemoryLambda). Another possible name is _stateful_lambda.
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OK. I'll fix.
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| class ReplicaExchangeMC(MonteCarlo): | ||
| """ | ||
| Replica Exchange MCMC(https://en.wikipedia.org/wiki/Parallel_tempering) |
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Cool! Is there a canonical citation we can use instead of the Wiki article?
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I think we should cite Swendsen and Wang(1986), Hukushima and Nemoto(1996).
I'll add the citations to bib.bib and this code.
| #### Examples | ||
| ```python | ||
| cat = Categorical(probs=[0.5,0.5]) | ||
| x = Mixture(cat=cat, components= |
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For PEP8, need
x = Mixture(cat=cat, components=[
MultivariateNormal...,
....])
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OK. I'll fix.
| def __init__(self, latent_vars, proposal_vars, data=None, | ||
| betas=np.logspace(0, -2, 5), exchange_freq=0.1): | ||
| """Create an inference algorithm. | ||
| Args: |
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add blankline inbetween args and top-level docstring
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OK. I'll fix.
| # Exchange adjacent replicas at frequency of exchange_freq | ||
| i = tf.random_uniform((), maxval=2, dtype=tf.int32) | ||
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| def c(i, new_replica_idx): |
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OK. I'll fix.
| def c(i, new_replica_idx): | ||
| return tf.less(i, self.n_replica - 1) | ||
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| def b(i, new_replica_idx): |
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OK. I'll fix.
| return tf.less(i, self.n_replica - 1) | ||
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| def b(i, new_replica_idx): | ||
| return [tf.add(i, 2), self.replica_exchange(i, i + 1, replica_sample, |
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OK. I'll fix.
| zip(six.iterkeys(new_sample), sample_values)} | ||
| return sample, accept | ||
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| def replica_exchange(self, candi, candj, replica_sample, new_replica_idx): |
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rename to private method, _replica_exchange
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OK. I'll fix.
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| return tf.group(*assign_ops) | ||
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| def mh_sample(self, latent_vars, beta): |
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rename to private method _mh_sample
Also, since this is a direct copy of MH algorithm's build_update, does it make sense to just build this class by inheriting from MetropolisHastings?
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Thanks!
As you say, except for using inverse temperature beta, this code is a copy of a part of MH algorithm's build_update. However in MH algorithm's build_update, sampling and updating are perform in the same function. In replica exchange method, exchanging must be perform between sampling and updating. Therefore, I think it is difficult to make ReplicaExchangeMC by inheriting from current MetropolisHastings.
To make ReplicaExchangeMC by inheriting from current MetropolisHastings,
It may be necessary to modify MetropolisHastings. (ex. creating _mh_sample)
And, replica exchange monte calro method doesn't necessarily have to use MH algorithm. Instead of MH algorithm, we can use Hamiltonian Monte Carlo and so on. So if possible, I'd like to make monte calro algorithms to use in ReplicaExchangeMC selectable.
Thank you for reading.
It would be extremely helpful if I could receive your advice.
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I see. Your choice is great—let's leave as is for now. We can always do refactoring in a future PR to avoid that code duplication.
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Thanks! I understand.
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Thank you for your quick and polite comments! Based on your comment, I corrected everything I can fix. But, the Travis CI check does not pass. |
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Sure, restarting job. |
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Thank you very much! And I'm happy that all checks passed. |
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All looks great. Thanks again! |
#861
Happy to open this pull request. I implemented replica exchange MCMC. I would appreciate it if you would review and comment.