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Improve random seed processing for SMC sampling #6298

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Merged
merged 3 commits into from
Nov 14, 2022
Merged

Improve random seed processing for SMC sampling #6298

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f8e8309
improve random seed processing
Nov 14, 2022
e8f11e0
fix-precommit
Nov 14, 2022
c25ab22
improve type-hint
Nov 14, 2022
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25 changes: 7 additions & 18 deletions pymc/smc/sampling.py
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Original file line number Diff line number Diff line change
Expand Up @@ -18,7 +18,6 @@
import warnings

from collections import defaultdict
from collections.abc import Iterable
from itertools import repeat

import cloudpickle
Expand All @@ -34,6 +33,7 @@
from pymc.model import modelcontext
from pymc.sampling.parallel import _cpu_count
from pymc.smc.kernels import IMH
from pymc.util import RandomState, _get_seeds_per_chain


def sample_smc(
Expand All @@ -42,7 +42,7 @@ def sample_smc(
*,
start=None,
model=None,
random_seed=None,
random_seed: RandomState = None,
chains=None,
cores=None,
compute_convergence_checks=True,
Expand All @@ -64,8 +64,10 @@ def sample_smc(
Starting point in parameter space. It should be a list of dict with length `chains`.
When None (default) the starting point is sampled from the prior distribution.
model: Model (optional if in ``with`` context)).
random_seed: int
random seed
random_seed : int, array-like of int, RandomState or Generator, optional
Random seed(s) used by the sampling steps. If a list, tuple or array of ints
is passed, each entry will be used to seed each chain. A ValueError will be
raised if the length does not match the number of chains.
chains : int
The number of chains to sample. Running independent chains is important for some
convergence statistics. If ``None`` (default), then set to either ``cores`` or 2, whichever
Expand Down Expand Up @@ -183,20 +185,7 @@ def sample_smc(
else:
cores = min(chains, cores)

if random_seed == -1:
raise FutureWarning(
f"random_seed should be a non-negative integer or None, got: {random_seed}"
"This will raise a ValueError in the Future"
)
random_seed = None
if isinstance(random_seed, int) or random_seed is None:
rng = np.random.default_rng(seed=random_seed)
random_seed = list(rng.integers(2**30, size=chains))
elif isinstance(random_seed, Iterable):
if len(random_seed) != chains:
raise ValueError(f"Length of seeds ({len(seeds)}) must match number of chains {chains}")
else:
raise TypeError("Invalid value for `random_seed`. Must be tuple, list, int or None")
random_seed = _get_seeds_per_chain(random_state=random_seed, chains=chains)

model = modelcontext(model)

Expand Down