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Updates for new Aesara deprecations and AePPL version
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brandonwillard committed Feb 21, 2023
1 parent 4f4ca7c commit 64b0e50
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Showing 6 changed files with 6 additions and 8 deletions.
2 changes: 1 addition & 1 deletion aemcmc/transforms.py
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Expand Up @@ -113,7 +113,7 @@ def invgamma_exponential(invgamma_expr, invexponential_expr):
size=size_lv,
dtype=dtype_lv,
)
invexponential_et = etuple(at.true_div, at.as_tensor(1.0), exponential_et)
invexponential_et = etuple(at.true_divide, at.as_tensor(1.0), exponential_et)

return lall(
eq(invgamma_expr, invgamma_et), eq(invexponential_expr, invexponential_et)
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4 changes: 2 additions & 2 deletions environment.yml
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Expand Up @@ -11,8 +11,8 @@ dependencies:
- compilers
- numpy>=1.18.1
- scipy>=1.4.0
- aesara>=2.8.3
- aeppl>=0.1.0
- aesara>=2.8.11
- aeppl>=0.1.2
- aehmc>=0.0.10
- polyagamma>=1.3.2
- cons
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4 changes: 2 additions & 2 deletions setup.py
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Expand Up @@ -38,8 +38,8 @@ def get_versions():
install_requires=[
"numpy>=1.18.1",
"scipy>=1.4.0",
"aesara>=2.8.3",
"aeppl>=0.1.0",
"aesara>=2.8.11",
"aeppl>=0.1.2",
"aehmc>=0.0.10",
"polyagamma>=1.3.2",
"cons",
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1 change: 0 additions & 1 deletion tests/test_basic.py
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Expand Up @@ -145,7 +145,6 @@ def test_nuts_with_closed_form():
assert beta_rv in sampler.sample_steps


@pytest.mark.xfail(reason="An `OpFromGraph` fix is needed to remove extra updates")
def test_create_gibbs():
srng = RandomStream(0)

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1 change: 0 additions & 1 deletion tests/test_conjugates.py
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Expand Up @@ -31,7 +31,6 @@ def test_gamma_poisson_conjugate_contract():
q_lv = var()
(posterior_expr,) = run(1, q_lv, gamma_poisson_conjugateo(y_vv, Y_rv, q_lv))
posterior = eval_if_etuple(posterior_expr)
aesara.dprint(posterior)

assert isinstance(posterior.owner.op, type(at.random.gamma))

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2 changes: 1 addition & 1 deletion tests/test_rewriting.py
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Expand Up @@ -119,7 +119,7 @@ def test_SubsumingElemwise_constant_inputs():
srng = at.random.RandomStream(0)

s = at.lscalar("s")
# The `1` is the constant input to a `true_div` `Elemwise` that should be
# The `1` is the constant input to a `true_divide` `Elemwise` that should be
# "subsumed"
Z = srng.exponential(1, size=s, name="Z")
mu = 1 / Z
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