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Add warning if a Minibatched variable is used without total_size #7742

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Merged
merged 4 commits into from
Apr 1, 2025
Merged

Add warning if a Minibatched variable is used without total_size #7742

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5febf12
Add warning if a Minibatched variable is used but total_size is forgo…
zaxtax Mar 30, 2025
79c551e
Lint fix
zaxtax Mar 30, 2025
a14fdf6
Fix for Shared Data
zaxtax Mar 30, 2025
e10511a
Add better signposting for warning
zaxtax Apr 1, 2025
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11 changes: 10 additions & 1 deletion pymc/model/core.py
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Original file line number Diff line number Diff line change
Expand Up @@ -40,7 +40,7 @@
from pytensor.tensor.variable import TensorConstant, TensorVariable

from pymc.blocking import DictToArrayBijection, RaveledVars
from pymc.data import is_valid_observed
from pymc.data import MinibatchOp, is_valid_observed
from pymc.exceptions import (
BlockModelAccessError,
ImputationWarning,
Expand Down Expand Up @@ -1241,6 +1241,15 @@ def register_rv(
self.add_named_variable(rv_var, dims)
self.set_initval(rv_var, initval)
else:
if (
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this should have checked if minibatch is anywhere in the ancestors of value not just immediately

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Do we need to do that? This check is only for observed random variables. I thought for those we don't allow the value to depend on another node

isinstance(observed, TensorVariable)
and observed.owner is not None
and isinstance(observed.owner.op, MinibatchOp)
and total_size is None
):
warnings.warn(
f"total_size not provided for observed variable `{name}` that uses pm.Minibatch"
)
if not is_valid_observed(observed):
raise TypeError(
"Variables that depend on other nodes cannot be used for observed data."
Expand Down
7 changes: 7 additions & 0 deletions tests/variational/test_minibatch_rv.py
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Expand Up @@ -112,6 +112,13 @@ def test_random(self):
assert mx is not x
np.testing.assert_array_equal(draw(mx, random_seed=1), draw(x, random_seed=1))

def test_warning_on_missing_total_size(self):
total_size = 1000
with pytest.warns(match="total_size not provided"):
with pm.Model() as m:
MB = pm.Minibatch(np.arange(total_size, dtype="float64"), batch_size=100)
pm.Normal("n", observed=MB)

@pytest.mark.filterwarnings("error")
def test_minibatch_parameter_and_value(self):
rng = np.random.default_rng(161)
Expand Down
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