SNLE and SNRE fail with a 1D prior #1283
Comments
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Hi there, thanks a lot for reporting this! The following will fix it: class WrappedExponential(Exponential):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
def log_prob(*args, **kwargs):
log_probs = Exponential.log_prob(*args, **kwargs)
return log_probs.squeeze()
exp_prior = WrappedExponential(torch.tensor([2.0]))The reason that the issue was happening is that I will leave this issue open though because we should deal with this in I hope that the above fixes the issue! Let me know if you have any more questions! All the best |
michaeldeistler
changed the title
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More notes for future fixing on our side: This is only an issue for 1D pytorch distributions. The issue is that, e.g., from torch.distributions import Exponential, MultivariateNormal
prior = Exponential(torch.tensor(2.0))
samples = prior.sample((10,)) # (10,)
log_probs = prior.log_prob(samples) # (10,)
# `sbi` raises an error because one must have a sample dimension.
prior = Exponential(torch.tensor([2.0]))
samples = prior.sample((10,)) # (10, 1)
log_probs = prior.log_prob(samples) # (10, 1)
# `sbi` fails because the log_prob dimension contains the data dim.
prior = MultivariateNormal(torch.tensor([2.0]), torch.tensor([2.0]))
samples = prior.sample((10,)) # (10, 1)
log_probs = prior.log_prob(samples) # (10,)
# `sbi` works.IMO, the easiest fix would be to introduce the following: class OneDimDistributionWrapper(torch.Distribution):
def __init__(self, dist, *args, **kwargs):
super().__init__()
self.dist = dist
def sample(*args, **kwargs):
return self.dist.sample(*args, **kwargs)
def log_prob(*args, **kwargs):
return self.dist.log_prob(*args, **kwargs)[..., 0] # Remove the additional dimension.
@property
def arg_constraints(self) -> Dict[str, constraints.Constraint]:
return self.dist.arg_constraints
@property
def support(self):
return self.dist.support
@property
def mean(self) -> Tensor:
return self.dist.mean
@property
def variance(self) -> Tensor:
return self.dist.varianceWe could then use this to wrap the 1D distributions which have a sample dimension. |
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Hi Michael! I am pleasantly surprised by your prompt response! I did make the first change as you had suggested and it did resolve the issue! However, I am now facing newer issues such as the network not converging/not converging randomly. Thanks for the help!! Result for SNLE: 
Result for SNRE (around 1/3rd of the time) 
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I don't understand what is being shown in the plot. |
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The plot is for the posterior on the scale parameter for the exponential being learned by SNLE/SNRE as shown in the code below for SNLE, against the true value of theta. An example of a desired result is comes from SNPE_C implementation as shown below: 
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I looked into this, and found three issues with your model:
simulator = lambda theta: ((1/theta)*torch.exp(-t/theta) + 0.5 * torch.randn_like(theta)).float()
theta = proposal.sample((num_sims,))
x = simulator(theta)
x[x > 5.0] = 5.0
With all of these fixes, I get the results below: As you can see, even with n=10, the posterior is very narrow. Hope this helps |
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Thank you so much for your suggestions! I did try them out. However, the estimates that I am getting from each of the 3 algorithms (SNPE_C, SNLE, SNRE_B) are somewhat unreliable. I get bad posterior estimates in between and the good estimates are not reproducible. Below are some examples of pathological posterior estimates:
It would be great if you could give some general suggestions, as my ultimate aim is to implement these algorithms for a 21 parameter ODE model. |
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I am sorry for not attaching the code for this issue, so here goes:
I had another question: They all give pathological outputs as shown in the previous comment. I would appreciate any suggestions! |
Describe the bug
I am implementing SNRE and SNLE (from implemented algorithms) on a simple exponential simulator model with noise and a prior. The algorithms work just fine for a uniform prior but give the following error: 'number of categories cannot exceed 2^24', with the exponential prior.
To Reproduce
Versions
Python version: 3.9.13
SBI version: 0.23.1
Code for SNLE implementation but I get the same error for SNRE implementation as well (with Inference object: NRE)
Expected behavior
I expect the network to undergo multiple rounds of training (2 in this example) or give me a pairplot after one round of training (not shown above).
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