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Recognize cast data in InferenceData #5646

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Merged
merged 1 commit into from
Mar 29, 2022
Merged

Recognize cast data in InferenceData #5646

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4 changes: 4 additions & 0 deletions pymc/aesaraf.py
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Original file line number Diff line number Diff line change
Expand Up @@ -47,6 +47,7 @@
from aesara.graph.fg import FunctionGraph
from aesara.graph.op import Op, compute_test_value
from aesara.sandbox.rng_mrg import MRG_RandomStream as RandomStream
from aesara.scalar.basic import Cast
from aesara.tensor.elemwise import Elemwise
from aesara.tensor.random.op import RandomVariable
from aesara.tensor.shape import SpecifyShape
Expand Down Expand Up @@ -232,6 +233,9 @@ def extract_obs_data(x: TensorVariable) -> np.ndarray:
return x.data
if isinstance(x, SharedVariable):
return x.get_value()
if x.owner and isinstance(x.owner.op, Elemwise) and isinstance(x.owner.op.scalar_op, Cast):
array_data = extract_obs_data(x.owner.inputs[0])
return array_data.astype(x.type.dtype)
if x.owner and isinstance(x.owner.op, (AdvancedIncSubtensor, AdvancedIncSubtensor1)):
array_data = extract_obs_data(x.owner.inputs[0])
mask_idx = tuple(extract_obs_data(i) for i in x.owner.inputs[2:])
Expand Down
8 changes: 8 additions & 0 deletions pymc/tests/test_aesaraf.py
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Expand Up @@ -380,6 +380,14 @@ def test_extract_obs_data():
assert isinstance(res, np.ndarray)
assert np.ma.allequal(res, data_m)

# Cast check
data = np.array(5)
t = at.cast(at.as_tensor(5.0), np.int64)
res = extract_obs_data(t)

assert isinstance(res, np.ndarray)
assert np.array_equal(res, data)


@pytest.mark.parametrize("input_dtype", ["int32", "int64", "float32", "float64"])
def test_pandas_to_array(input_dtype):
Expand Down
4 changes: 2 additions & 2 deletions pymc/tests/test_sampling.py
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Expand Up @@ -1078,15 +1078,15 @@ def test_shared(self):
o = pm.Deterministic("o", obs)
gen1 = pm.sample_prior_predictive(draws)

assert gen1.prior["y"].shape == (1, draws, n1)
assert gen1.prior_predictive["y"].shape == (1, draws, n1)
assert gen1.prior["o"].shape == (1, draws, n1)

n2 = 20
obs.set_value(np.random.rand(n2) < 0.5)
with m:
gen2 = pm.sample_prior_predictive(draws)

assert gen2.prior["y"].shape == (1, draws, n2)
assert gen2.prior_predictive["y"].shape == (1, draws, n2)
assert gen2.prior["o"].shape == (1, draws, n2)

def test_density_dist(self):
Expand Down