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Refactoring the ChiSquared distribution #4695

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merged 13 commits into from
Jun 7, 2021
Merged

Refactoring the ChiSquared distribution #4695

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48e46d6
Refactoring ChiSquared distribution
May 14, 2021
4de2192
Refactoring ChiSquared (minor edit)
May 14, 2021
53c8cc8
Refactoring Chisquared (another one-line change)
May 14, 2021
ba3099b
Trying to rebase/merge my branch with updated upstream v4
May 20, 2021
d4aa9f0
Fixing merge conflict
May 20, 2021
be682a2
Using aesara chisquare op (r.f. PR #414) and renamed ChiSquared to Ch…
May 20, 2021
dff62a1
Added logpdf & logcdf to the ChiSquare class
May 30, 2021
e7ba224
Corrected function name
May 31, 2021
78ca870
Updating branch
Jun 2, 2021
f3c0144
Refactoring ChiSquared: bug fixed, tests work locally
Jun 4, 2021
163d0b0
Minor fix: removed float32 specification
Jun 4, 2021
b18336a
:sunny: underflow to -inf seems normal in float32
Jun 7, 2021
074b3af
Minor fix in documentation
Jun 7, 2021
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2 changes: 1 addition & 1 deletion docs/source/api/distributions/continuous.rst
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Original file line number Diff line number Diff line change
Expand Up @@ -25,7 +25,7 @@ Continuous
InverseGamma
Weibull
Lognormal
ChiSquared
ChiSquare
Wald
Pareto
ExGaussian
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4 changes: 2 additions & 2 deletions pymc3/distributions/__init__.py
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Expand Up @@ -27,7 +27,7 @@
AsymmetricLaplace,
Beta,
Cauchy,
ChiSquared,
ChiSquare,
ExGaussian,
Exponential,
Flat,
Expand Down Expand Up @@ -125,7 +125,7 @@
"Bound",
"Lognormal",
"HalfStudentT",
"ChiSquared",
"ChiSquare",
"HalfNormal",
"Wald",
"Pareto",
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11 changes: 7 additions & 4 deletions pymc3/distributions/continuous.py
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Expand Up @@ -28,6 +28,7 @@
BetaRV,
WeibullRV,
cauchy,
chisquare,
exponential,
gamma,
gumbel,
Expand Down Expand Up @@ -87,7 +88,7 @@
"Weibull",
"HalfStudentT",
"Lognormal",
"ChiSquared",
"ChiSquare",
"HalfNormal",
"Wald",
"Pareto",
Expand Down Expand Up @@ -2548,7 +2549,7 @@ def logcdf(value, alpha, beta):
)


class ChiSquared(Gamma):
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class ChiSquare(Gamma):
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r"""
:math:`\chi^2` log-likelihood.

Expand Down Expand Up @@ -2586,9 +2587,11 @@ class ChiSquared(Gamma):
nu: int
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Degrees of freedom (nu > 0).
"""
rv_op = chisquare

def __init__(self, nu, *args, **kwargs):
self.nu = nu = at.as_tensor_variable(floatX(nu))
@classmethod
def dist(cls, nu, *args, **kwargs):
nu = at.as_tensor_variable(floatX(nu))
super().__init__(alpha=nu / 2.0, beta=0.5, *args, **kwargs)
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4 changes: 2 additions & 2 deletions pymc3/distributions/multivariate.py
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Expand Up @@ -40,7 +40,7 @@

from pymc3.aesaraf import floatX, intX
from pymc3.distributions import transforms
from pymc3.distributions.continuous import ChiSquared, Normal
from pymc3.distributions.continuous import ChiSquare, Normal
from pymc3.distributions.dist_math import bound, factln, logpow
from pymc3.distributions.distribution import Continuous, Discrete
from pymc3.distributions.special import gammaln, multigammaln
Expand Down Expand Up @@ -905,7 +905,7 @@ def WishartBartlett(name, S, nu, is_cholesky=False, return_cholesky=False, testv
tril_testval = None

c = at.sqrt(
ChiSquared("%s_c" % name, nu - np.arange(2, 2 + n_diag), shape=n_diag, testval=diag_testval)
ChiSquare("%s_c" % name, nu - np.arange(2, 2 + n_diag), shape=n_diag, testval=diag_testval)
)
pm._log.info("Added new variable %s_c to model diagonal of Wishart." % name)
z = Normal("%s_z" % name, 0.0, 1.0, shape=n_tril, testval=tril_testval)
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2 changes: 1 addition & 1 deletion pymc3/gp/gp.py
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Expand Up @@ -278,7 +278,7 @@ def _build_prior(self, name, X, reparameterize=True, **kwargs):
cov = stabilize(self.cov_func(X))
shape = infer_shape(X, kwargs.pop("shape", None))
if reparameterize:
chi2 = pm.ChiSquared(name + "_chi2_", self.nu)
chi2 = pm.ChiSquare(name + "_chi2_", self.nu)
v = pm.Normal(name + "_rotated_", mu=0.0, sigma=1.0, size=shape, **kwargs)
f = pm.Deterministic(name, (at.sqrt(self.nu) / chi2) * (mu + cholesky(cov).dot(v)))
else:
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5 changes: 2 additions & 3 deletions pymc3/tests/test_distributions.py
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Expand Up @@ -48,7 +48,7 @@
Bound,
Categorical,
Cauchy,
ChiSquared,
ChiSquare,
Constant,
DensityDist,
Dirichlet,
Expand Down Expand Up @@ -1030,10 +1030,9 @@ def test_half_normal(self):
lambda value, sigma: sp.halfnorm.logcdf(value, scale=sigma),
)

@pytest.mark.xfail(reason="Distribution not refactored yet")
def test_chi_squared(self):
self.check_logp(
ChiSquared,
ChiSquare,
Rplus,
{"nu": Rplusdunif},
lambda value, nu: sp.chi2.logpdf(value, df=nu),
Expand Down
13 changes: 10 additions & 3 deletions pymc3/tests/test_distributions_random.py
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Expand Up @@ -277,10 +277,17 @@ class TestAsymmetricLaplace(BaseTestCases.BaseTestCase):
params = {"kappa": 1.0, "b": 1.0, "mu": 0.0}


@pytest.mark.xfail(reason="This distribution has not been refactored for v4")
class TestChiSquared(BaseTestCases.BaseTestCase):
distribution = pm.ChiSquared
class TestChiSquare(BaseTestCases.BaseTestCase):
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distribution = pm.ChiSquare
params = {"nu": 2.0}
expected_rv_op_params = {"nu": 2.0}
reference_dist_params = {"df": 2.0}
reference_dist = seeded_numpy_distribution_builder("chisquare")
tests_to_run = [
"check_pymc_params_match_rv_op",
"check_pymc_draws_match_reference",
"check_rv_size",
]


@pytest.mark.xfail(reason="This distribution has not been refactored for v4")
Expand Down