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Fix binominal distribution #1860

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merged 5 commits into from
Sep 10, 2024
Merged

Fix binominal distribution #1860

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0dabbdd
Comments added why the binominal_dispatch function will run into an i…
InfinityMod Sep 9, 2024
baf4d35
Fix for the infinite loop problem of binominal_dispatch.
InfinityMod Sep 9, 2024
1ce6995
Update of the fix to a more concise version.
InfinityMod Sep 10, 2024
ddc8a7c
Linting fix
InfinityMod Sep 10, 2024
ccb4c80
Changed to the proposed solution, to correct log1_p value correctly
InfinityMod Sep 10, 2024
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2 changes: 2 additions & 0 deletions numpyro/distributions/util.py
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Original file line number Diff line number Diff line change
Expand Up @@ -156,6 +156,8 @@ def _binom_inv_cond_fn(val):
def _binomial_dispatch(key, p, n):
def dispatch(key, p, n):
is_le_mid = p <= 0.5
#Make sure p=0 is never taken into account as a fix for possible zeros in p.
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could we just simply clip p to tiny? jnp.clip(p, minval=jnp.finfo(p.dtype).tiny)

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@InfinityMod InfinityMod Sep 10, 2024 •
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This works for me: jnp.clip(p, jnp.finfo(jnp.float32).tiny)
There's a change with a depreciation of the argument a_min, which changes to min for jnp.clip.
So, to support future and early versions, I set the min argument as a positional argument.
However, this can also be an error if the argument order changes.

jnp.finfo(p.dtype) doesn't seem to work, with float32 it's working.

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this might affect samples with small p in x64. How about updating _binomial_inversion to

geom = jnp.floor(jnp.log1p(-u) / log1_p) + 1
...
log1_p = jnp.log1p(-p)
log1_p = jnp.where(log1_p == 0, -jnp.finfo(log1_p.dtype).tiny, log1_p)

The issue seems to come from log1_p=0 and jnp.log1p(-u) < 0, which leads to a negative geom

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Could you also fix the lint issue? I think you need a space # comment for the comment.

p = jnp.sum(jnp.stack((p, jnp.ones(p.shape)*0.001)), where = (p == 0))
pq = jnp.where(is_le_mid, p, 1 - p)
mu = n * pq
k = lax.cond(
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