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提交 a71c1aca authored 作者: Brandon T. Willard's avatar Brandon T. Willard 提交者： Brandon T. Willard
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Use distribution tests on Numba samplers that don't match NumPy

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tests/link/test_numba.py
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......@@ -5,6 +5,7 @@ from unittest import mock
import numba
import numpy as np
import pytest
import scipy.stats as stats
import aesara.scalar as aes
import aesara.scalar.basic as aesb
......@@ -2731,21 +2732,6 @@ def test_shared():
],
at.as_tensor([3, 2]),
),
pytest.param(
aer.beta,
[
set_test_value(
at.dvector(),
np.array([1.0, 2.0], dtype=np.float64),
),
set_test_value(
at.dscalar(),
np.array(1.0, dtype=np.float64),
),
],
at.as_tensor([3, 2]),
marks=pytest.mark.xfail(reason="Numba and NumPy rng states do not match"),
),
(
aer.lognormal,
[
......@@ -2760,32 +2746,6 @@ def test_shared():
],
at.as_tensor([3, 2]),
),
pytest.param(
aer.gamma,
[
set_test_value(
at.dvector(),
np.array([1.0, 2.0], dtype=np.float64),
),
set_test_value(
at.dscalar(),
np.array(1.0, dtype=np.float64),
),
],
at.as_tensor([3, 2]),
marks=pytest.mark.xfail(reason="Numba and NumPy rng states do not match"),
),
pytest.param(
aer.chisquare,
[
set_test_value(
at.dvector(),
np.array([1.0, 2.0], dtype=np.float64),
)
],
at.as_tensor([3, 2]),
marks=pytest.mark.xfail(reason="Numba and NumPy rng states do not match"),
),
pytest.param(
aer.pareto,
[
......@@ -2797,21 +2757,6 @@ def test_shared():
at.as_tensor([3, 2]),
marks=pytest.mark.xfail(reason="Not implemented"),
),
pytest.param(
aer.gumbel,
[
set_test_value(
at.dvector(),
np.array([1.0, 2.0], dtype=np.float64),
),
set_test_value(
at.dscalar(),
np.array(1.0, dtype=np.float64),
),
],
at.as_tensor([3, 2]),
marks=pytest.mark.xfail(reason="Numba and NumPy rng states do not match"),
),
(
aer.exponential,
[
......@@ -2846,21 +2791,6 @@ def test_shared():
],
at.as_tensor([3, 2]),
),
pytest.param(
aer.vonmises,
[
set_test_value(
at.dvector(),
np.array([1.0, 2.0], dtype=np.float64),
),
set_test_value(
at.dscalar(),
np.array(1.0, dtype=np.float64),
),
],
at.as_tensor([3, 2]),
marks=pytest.mark.xfail(reason="Numba and NumPy rng states do not match"),
),
(
aer.geometric,
[
......@@ -2889,21 +2819,6 @@ def test_shared():
],
at.as_tensor([3, 2]),
),
pytest.param(
aer.cauchy,
[
set_test_value(
at.dvector(),
np.array([1.0, 2.0], dtype=np.float64),
),
set_test_value(
at.dscalar(),
np.array(1.0, dtype=np.float64),
),
],
at.as_tensor([3, 2]),
marks=pytest.mark.xfail(reason="Numba and NumPy rng states do not match"),
),
(
aer.wald,
[
......@@ -2946,20 +2861,6 @@ def test_shared():
],
at.as_tensor([3, 2]),
),
(
aer.negative_binomial,
[
set_test_value(
at.lvector(),
np.array([1, 2], dtype=np.int64),
),
set_test_value(
at.dscalar(),
np.array(0.9, dtype=np.float64),
),
],
at.as_tensor([3, 2]),
),
(
aer.normal,
[
......@@ -3040,7 +2941,8 @@ def test_shared():
],
ids=str,
)
def test_RandomVariable(rv_op, dist_args, size):
def test_aligned_RandomVariable(rv_op, dist_args, size):
"""Tests for Numba samplers that are one-to-one with Aesara's/NumPy's samplers."""
rng = shared(np.random.RandomState(29402))
g = rv_op(*dist_args, size=size, rng=rng)
g_fg = FunctionGraph(outputs=[g])
......@@ -3055,6 +2957,149 @@ def test_RandomVariable(rv_op, dist_args, size):
)
@pytest.mark.parametrize(
"rv_op, dist_args, base_size, cdf_name, params_conv",
[
(
aer.beta,
[
set_test_value(
at.dvector(),
np.array([1.0, 2.0], dtype=np.float64),
),
set_test_value(
at.dscalar(),
np.array(1.0, dtype=np.float64),
),
],
(2,),
"beta",
lambda *args: args,
),
(
aer.gamma,
[
set_test_value(
at.dvector(),
np.array([1.0, 2.0], dtype=np.float64),
),
set_test_value(
at.dscalar(),
np.array(1.0, dtype=np.float64),
),
],
(2,),
"gamma",
lambda a, b: (a, 0.0, b),
),
(
aer.cauchy,
[
set_test_value(
at.dvector(),
np.array([1.0, 2.0], dtype=np.float64),
),
set_test_value(
at.dscalar(),
np.array(1.0, dtype=np.float64),
),
],
(2,),
"cauchy",
lambda *args: args,
),
(
aer.chisquare,
[
set_test_value(
at.dvector(),
np.array([1.0, 2.0], dtype=np.float64),
)
],
(2,),
"chi2",
lambda *args: args,
),
(
aer.gumbel,
[
set_test_value(
at.dvector(),
np.array([1.0, 2.0], dtype=np.float64),
),
set_test_value(
at.dscalar(),
np.array(1.0, dtype=np.float64),
),
],
(2,),
"gumbel_r",
lambda *args: args,
),
(
aer.negative_binomial,
[
set_test_value(
at.lvector(),
np.array([100, 200], dtype=np.int64),
),
set_test_value(
at.dscalar(),
np.array(0.09, dtype=np.float64),
),
],
(2,),
"nbinom",
lambda *args: args,
),
pytest.param(
aer.vonmises,
[
set_test_value(
at.dvector(),
np.array([-0.5, 0.5], dtype=np.float64),
),
set_test_value(
at.dscalar(),
np.array(1.0, dtype=np.float64),
),
],
(2,),
"vonmises_line",
lambda mu, kappa: (kappa, mu),
marks=pytest.mark.xfail(
reason=(
"Numba's parameterization of `vonmises` does not match NumPy's."
"See https://github.com/numba/numba/issues/7886"
)
),
),
],
ids=str,
)
def test_unaligned_RandomVariable(rv_op, dist_args, base_size, cdf_name, params_conv):
"""Tests for Numba samplers that are not one-to-one with Aesara's/NumPy's samplers."""
rng = shared(np.random.RandomState(29402))
g = rv_op(*dist_args, size=(2000,) + base_size, rng=rng)
g_fn = function(dist_args, g, mode=numba_mode)
samples = g_fn(
*[
i.tag.test_value
for i in g_fn.maker.fgraph.inputs
if not isinstance(i, (SharedVariable, Constant))
]
)
bcast_dist_args = np.broadcast_arrays(*[i.tag.test_value for i in dist_args])
for idx in np.ndindex(*base_size):
cdf_params = params_conv(*tuple(arg[idx] for arg in bcast_dist_args))
test_res = stats.cramervonmises(
samples[(Ellipsis,) + idx], cdf_name, args=cdf_params
)
assert test_res.pvalue > 0.1
@pytest.mark.parametrize(
"rv_op, dist_args, size, cm",
[
......
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