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test_continuous.py
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# Copyright 2024 The PyMC Developers
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import functools as ft
import numpy as np
import numpy.testing as npt
import pytensor
import pytensor.tensor as pt
import pytest
import scipy.special as sp
import scipy.stats as st
from pytensor.compile.mode import Mode
import pymc as pm
from pymc.distributions.continuous import get_tau_sigma, interpolated
from pymc.distributions.dist_math import clipped_beta_rvs
from pymc.logprob.basic import icdf, logcdf, logp
from pymc.logprob.utils import ParameterValueError
from pymc.pytensorf import floatX
from pymc.testing import (
BaseTestDistributionRandom,
Circ,
Domain,
R,
Rplus,
Rplusbig,
Rplusunif,
Runif,
Unit,
assert_support_point_is_expected,
check_icdf,
check_logcdf,
check_logp,
continuous_random_tester,
seeded_numpy_distribution_builder,
seeded_scipy_distribution_builder,
select_by_precision,
)
try:
from polyagamma import polyagamma_cdf, polyagamma_pdf, random_polyagamma
_polyagamma_not_installed = False
except ImportError: # pragma: no cover
_polyagamma_not_installed = True
def polyagamma_pdf(*args, **kwargs):
raise RuntimeError("polyagamma package is not installed!")
def polyagamma_cdf(*args, **kwargs):
raise RuntimeError("polyagamma package is not installed!")
def random_polyagamma(*args, **kwargs):
raise RuntimeError("polyagamma package is not installed!")
class TestBoundedContinuous:
def get_dist_params_and_interval_bounds(self, model, rv_name):
rv = model.named_vars[rv_name]
dist_params = rv.owner.inputs
lower_interval, upper_interval = model.rvs_to_transforms[rv].args_fn(*rv.owner.inputs)
return (
dist_params,
lower_interval,
upper_interval,
)
def test_upper_bounded(self):
bounded_rv_name = "lower_bounded"
with pm.Model() as model:
pm.TruncatedNormal(bounded_rv_name, mu=1, sigma=2, lower=None, upper=3)
(
(_, _, _, _, lower, upper),
lower_interval,
upper_interval,
) = self.get_dist_params_and_interval_bounds(model, bounded_rv_name)
assert lower.value == -np.inf
assert upper.value == 3
assert lower_interval is None
assert upper_interval.value == 3
def test_lower_bounded(self):
bounded_rv_name = "upper_bounded"
with pm.Model() as model:
pm.TruncatedNormal(bounded_rv_name, mu=1, sigma=2, lower=-2, upper=None)
(
(_, _, _, _, lower, upper),
lower_interval,
upper_interval,
) = self.get_dist_params_and_interval_bounds(model, bounded_rv_name)
assert lower.value == -2
assert upper.value == np.inf
assert lower_interval.value == -2
assert upper_interval is None
def test_lower_bounded_vector(self):
bounded_rv_name = "upper_bounded"
with pm.Model() as model:
pm.TruncatedNormal(
bounded_rv_name,
mu=np.array([1, 1]),
sigma=np.array([2, 3]),
lower=np.array([-1.0, 0]),
upper=None,
)
(
(_, _, _, _, lower, upper),
lower_interval,
upper_interval,
) = self.get_dist_params_and_interval_bounds(model, bounded_rv_name)
assert np.array_equal(lower.eval(), [-1, 0])
assert np.array_equal(upper.eval(), [np.inf])
assert np.array_equal(lower_interval.eval(), [-1, 0])
assert upper_interval is None
def test_lower_bounded_broadcasted(self):
bounded_rv_name = "upper_bounded"
with pm.Model() as model:
pm.TruncatedNormal(
bounded_rv_name,
mu=np.array([1, 1]),
sigma=np.array([2, 3]),
lower=-1,
upper=np.array([np.inf, np.inf]),
)
(
(_, _, _, _, lower, upper),
lower_interval,
upper_interval,
) = self.get_dist_params_and_interval_bounds(model, bounded_rv_name)
assert np.array_equal(lower.eval(), [-1])
assert np.array_equal(upper.eval(), [np.inf, np.inf])
assert np.array_equal(lower_interval.eval(), [-1])
assert upper_interval is None
def laplace_asymmetric_logpdf(value, kappa, b, mu):
kapinv = 1 / kappa
value = value - mu
lPx = value * b * np.where(value >= 0, -kappa, kapinv)
lPx += np.log(b / (kappa + kapinv))
return lPx
class TestMatchesScipy:
def test_uniform(self):
check_logp(
pm.Uniform,
Runif,
{"lower": -Rplusunif, "upper": Rplusunif},
lambda value, lower, upper: st.uniform.logpdf(value, lower, upper - lower),
skip_paramdomain_outside_edge_test=True,
)
check_logcdf(
pm.Uniform,
Runif,
{"lower": -Rplusunif, "upper": Rplusunif},
lambda value, lower, upper: st.uniform.logcdf(value, lower, upper - lower),
skip_paramdomain_outside_edge_test=True,
)
check_icdf(
pm.Uniform,
{"lower": -Rplusunif, "upper": Rplusunif},
lambda q, lower, upper: st.uniform.ppf(q=q, loc=lower, scale=upper - lower),
skip_paramdomain_outside_edge_test=True,
)
# Custom logp / logcdf check for invalid parameters
invalid_dist = pm.Uniform.dist(lower=1, upper=0)
with pytensor.config.change_flags(mode=Mode("py")):
with pytest.raises(ParameterValueError):
logp(invalid_dist, np.array(0.5)).eval()
with pytest.raises(ParameterValueError):
logcdf(invalid_dist, np.array(0.5)).eval()
with pytest.raises(ParameterValueError):
icdf(invalid_dist, np.array(0.5)).eval()
def test_triangular(self):
check_logp(
pm.Triangular,
Runif,
{"lower": -Rplusunif, "c": Runif, "upper": Rplusunif},
lambda value, c, lower, upper: st.triang.logpdf(value, c - lower, lower, upper - lower),
skip_paramdomain_outside_edge_test=True,
)
check_logcdf(
pm.Triangular,
Runif,
{"lower": -Rplusunif, "c": Runif, "upper": Rplusunif},
lambda value, c, lower, upper: st.triang.logcdf(value, c - lower, lower, upper - lower),
skip_paramdomain_outside_edge_test=True,
)
check_icdf(
pm.Triangular,
{"lower": -Rplusunif, "c": Runif, "upper": Rplusunif},
lambda q, c, lower, upper: st.triang.ppf(q, c - lower, lower, upper - lower),
skip_paramdomain_outside_edge_test=True,
)
# Custom logp/logcdf check for values outside of domain
valid_dist = pm.Triangular.dist(lower=0, upper=1, c=0.9, size=2)
with pytensor.config.change_flags(mode=Mode("py")):
assert np.all(logp(valid_dist, np.array([-1, 2])).eval() == -np.inf)
assert np.all(logcdf(valid_dist, np.array([-1, 2])).eval() == [-np.inf, 0])
# Custom logcdf check for invalid parameters.
# Invalid logp checks for triangular are being done in aeppl
invalid_dist = pm.Triangular.dist(lower=1, upper=0, c=0.1)
with pytensor.config.change_flags(mode=Mode("py")):
with pytest.raises(ParameterValueError):
logcdf(invalid_dist, 2).eval()
invalid_dist = pm.Triangular.dist(lower=0, upper=1, c=2.0)
with pytensor.config.change_flags(mode=Mode("py")):
with pytest.raises(ParameterValueError):
logcdf(invalid_dist, 2).eval()
@pytest.mark.skipif(
condition=_polyagamma_not_installed,
reason="`polyagamma package is not available/installed.",
)
def test_polyagamma(self):
check_logp(
pm.PolyaGamma,
Rplus,
{"h": Rplus, "z": R},
lambda value, h, z: polyagamma_pdf(value, h, z, return_log=True),
decimal=select_by_precision(float64=6, float32=-1),
)
check_logcdf(
pm.PolyaGamma,
Rplus,
{"h": Rplus, "z": R},
lambda value, h, z: polyagamma_cdf(value, h, z, return_log=True),
decimal=select_by_precision(float64=6, float32=-1),
)
def test_flat(self):
check_logp(pm.Flat, R, {}, lambda value: 0)
with pm.Model():
x = pm.Flat("a")
check_logcdf(pm.Flat, R, {}, lambda value: np.log(0.5))
# Check infinite cases individually.
assert 0.0 == logcdf(pm.Flat.dist(), np.inf).eval()
assert -np.inf == logcdf(pm.Flat.dist(), -np.inf).eval()
def test_half_flat(self):
check_logp(pm.HalfFlat, Rplus, {}, lambda value: 0)
with pm.Model():
x = pm.HalfFlat("a", size=2)
check_logcdf(pm.HalfFlat, Rplus, {}, lambda value: -np.inf)
# Check infinite cases individually.
assert 0.0 == logcdf(pm.HalfFlat.dist(), np.inf).eval()
assert -np.inf == logcdf(pm.HalfFlat.dist(), -np.inf).eval()
def test_normal(self):
check_logp(
pm.Normal,
R,
{"mu": R, "sigma": Rplus},
lambda value, mu, sigma: st.norm.logpdf(value, mu, sigma),
decimal=select_by_precision(float64=6, float32=1),
)
check_logcdf(
pm.Normal,
R,
{"mu": R, "sigma": Rplus},
lambda value, mu, sigma: st.norm.logcdf(value, mu, sigma),
decimal=select_by_precision(float64=6, float32=1),
)
check_icdf(
pm.Normal,
{"mu": R, "sigma": Rplus},
lambda q, mu, sigma: st.norm.ppf(q, mu, sigma),
)
def test_half_normal(self):
check_logp(
pm.HalfNormal,
Rplus,
{"sigma": Rplus},
lambda value, sigma: st.halfnorm.logpdf(value, scale=sigma),
decimal=select_by_precision(float64=6, float32=-1),
)
check_logcdf(
pm.HalfNormal,
Rplus,
{"sigma": Rplus},
lambda value, sigma: st.halfnorm.logcdf(value, scale=sigma),
)
check_icdf(
pm.HalfNormal,
{"sigma": Rplus},
lambda q, sigma: st.halfnorm.ppf(q, scale=sigma),
)
def test_chisquared_logp(self):
check_logp(
pm.ChiSquared,
Rplus,
{"nu": Rplus},
lambda value, nu: st.chi2.logpdf(value, df=nu),
)
@pytest.mark.skipif(
condition=(pytensor.config.floatX == "float32"),
reason="Fails on float32 due to numerical issues",
)
def test_chisquared_logcdf(self):
check_logcdf(
pm.ChiSquared,
Rplus,
{"nu": Rplus},
lambda value, nu: st.chi2.logcdf(value, df=nu),
)
def test_wald_logp(self):
check_logp(
pm.Wald,
Rplus,
{"mu": Rplus, "alpha": Rplus},
lambda value, mu, alpha: st.invgauss.logpdf(value, mu=mu, loc=alpha),
decimal=select_by_precision(float64=6, float32=1),
)
def test_wald_logcdf(self):
check_logcdf(
pm.Wald,
Rplus,
{"mu": Rplus, "alpha": Rplus},
lambda value, mu, alpha: st.invgauss.logcdf(value, mu=mu, loc=alpha),
)
@pytest.mark.parametrize(
"value,mu,lam,phi,alpha,logp",
[
(0.5, 0.001, 0.5, None, 0.0, -124500.7257914),
(1.0, 0.5, 0.001, None, 0.0, -4.3733162),
(2.0, 1.0, None, None, 0.0, -2.2086593),
(5.0, 2.0, 2.5, None, 0.0, -3.4374500),
(7.5, 5.0, None, 1.0, 0.0, -3.2199074),
(15.0, 10.0, None, 0.75, 0.0, -4.0360623),
(50.0, 15.0, None, 0.66666, 0.0, -6.1801249),
(0.5, 0.001, 0.5, None, 0.0, -124500.7257914),
(1.0, 0.5, 0.001, None, 0.5, -3.3330954),
(2.0, 1.0, None, None, 1.0, -0.9189385),
(5.0, 2.0, 2.5, None, 2.0, -2.2128783),
(7.5, 5.0, None, 1.0, 2.5, -2.5283764),
(15.0, 10.0, None, 0.75, 5.0, -3.3653647),
(50.0, 15.0, None, 0.666666, 10.0, -5.6481874),
],
)
def test_wald_logp_custom_points(self, value, mu, lam, phi, alpha, logp):
# Log probabilities calculated using the dIG function from the R package gamlss.
# See e.g., doi: 10.1111/j.1467-9876.2005.00510.x, or
# http://www.gamlss.org/.
with pm.Model() as model:
pm.Wald("wald", mu=mu, lam=lam, phi=phi, alpha=alpha, default_transform=None)
point = {"wald": value}
decimals = select_by_precision(float64=6, float32=1)
npt.assert_almost_equal(
model.compile_logp()(point), logp, decimal=decimals, err_msg=str(point)
)
def test_beta_logp(self):
check_logp(
pm.Beta,
Unit,
{"alpha": Rplus, "beta": Rplus},
lambda value, alpha, beta: st.beta.logpdf(value, alpha, beta),
)
def beta_mu_sigma(value, mu, sigma):
kappa = mu * (1 - mu) / sigma**2 - 1
return st.beta.logpdf(value, mu * kappa, (1 - mu) * kappa)
# The mu/sigma parametrization is not always valid
safe_mu_domain = Domain([0, 0.3, 0.5, 0.8, 1])
safe_sigma_domain = Domain([0, 0.05, 0.1, np.inf])
check_logp(
pm.Beta,
Unit,
{"mu": safe_mu_domain, "sigma": safe_sigma_domain},
beta_mu_sigma,
)
@pytest.mark.skipif(
condition=(pytensor.config.floatX == "float32"),
reason="Fails on float32 due to numerical issues",
)
def test_beta_logcdf(self):
check_logcdf(
pm.Beta,
Unit,
{"alpha": Rplus, "beta": Rplus},
lambda value, alpha, beta: st.beta.logcdf(value, alpha, beta),
)
def test_beta_icdf(self):
check_icdf(
pm.Beta,
{"alpha": Rplus, "beta": Rplus},
lambda q, alpha, beta: st.beta.ppf(q, alpha, beta),
)
def test_kumaraswamy(self):
# Scipy does not have a built-in Kumaraswamy
def scipy_log_pdf(value, a, b):
return np.log(a) + np.log(b) + (a - 1) * np.log(value) + (b - 1) * np.log(1 - value**a)
def log1mexp(x):
return np.log1p(-np.exp(x)) if x < np.log(0.5) else np.log(-np.expm1(x))
def scipy_log_cdf(value, a, b):
return log1mexp(b * np.log1p(-(value**a)))
check_logp(
pm.Kumaraswamy,
Unit,
{"a": Rplus, "b": Rplus},
scipy_log_pdf,
)
check_logcdf(
pm.Kumaraswamy,
Unit,
{"a": Rplus, "b": Rplus},
scipy_log_cdf,
)
def test_exponential(self):
check_logp(
pm.Exponential,
Rplus,
{"lam": Rplus},
lambda value, lam: st.expon.logpdf(value, 0, 1 / lam),
)
check_logcdf(
pm.Exponential,
Rplus,
{"lam": Rplus},
lambda value, lam: st.expon.logcdf(value, 0, 1 / lam),
)
check_icdf(
pm.Exponential,
{"lam": Rplus},
lambda q, lam: st.expon.ppf(q, loc=0, scale=1 / lam),
)
def test_laplace(self):
check_logp(
pm.Laplace,
R,
{"mu": R, "b": Rplus},
lambda value, mu, b: st.laplace.logpdf(value, mu, b),
)
check_logcdf(
pm.Laplace,
R,
{"mu": R, "b": Rplus},
lambda value, mu, b: st.laplace.logcdf(value, mu, b),
)
check_icdf(pm.Laplace, {"mu": R, "b": Rplus}, lambda q, mu, b: st.laplace.ppf(q, mu, b))
def test_laplace_asymmetric(self):
check_logp(
pm.AsymmetricLaplace,
R,
{"b": Rplus, "kappa": Rplus, "mu": R},
laplace_asymmetric_logpdf,
decimal=select_by_precision(float64=6, float32=2),
)
def test_lognormal(self):
check_logp(
pm.LogNormal,
Rplus,
{"mu": R, "tau": Rplusbig},
lambda value, mu, tau: floatX(st.lognorm.logpdf(value, tau**-0.5, 0, np.exp(mu))),
)
check_logp(
pm.LogNormal,
Rplus,
{"mu": R, "sigma": Rplusbig},
lambda value, mu, sigma: floatX(st.lognorm.logpdf(value, sigma, 0, np.exp(mu))),
)
check_logcdf(
pm.LogNormal,
Rplus,
{"mu": R, "tau": Rplusbig},
lambda value, mu, tau: st.lognorm.logcdf(value, tau**-0.5, 0, np.exp(mu)),
)
check_logcdf(
pm.LogNormal,
Rplus,
{"mu": R, "sigma": Rplusbig},
lambda value, mu, sigma: st.lognorm.logcdf(value, sigma, 0, np.exp(mu)),
)
check_icdf(
pm.LogNormal,
{"mu": R, "tau": Rplusbig},
lambda q, mu, tau: floatX(st.lognorm.ppf(q, tau**-0.5, 0, np.exp(mu))),
)
# Because we exponentiate the normal quantile function, setting sigma >= 9.5
# return extreme values that results in relative errors above 4 digits
# we circumvent it by keeping it below or equal to 9.
custom_rplusbig = Domain([0, 0.5, 0.9, 0.99, 1, 1.5, 2, 9, np.inf])
check_icdf(
pm.LogNormal,
{"mu": R, "sigma": custom_rplusbig},
lambda q, mu, sigma: floatX(st.lognorm.ppf(q, sigma, 0, np.exp(mu))),
decimal=select_by_precision(float64=4, float32=3),
)
def test_studentt_logp(self):
check_logp(
pm.StudentT,
R,
{"nu": Rplus, "mu": R, "lam": Rplus},
lambda value, nu, mu, lam: st.t.logpdf(value, nu, mu, lam**-0.5),
)
check_logp(
pm.StudentT,
R,
{"nu": Rplus, "mu": R, "sigma": Rplus},
lambda value, nu, mu, sigma: st.t.logpdf(value, nu, mu, sigma),
)
def test_skewstudentt_logp(self):
# NOTE: Test with less extreme positive numbers
rplusnonzero = Domain([0, 0.01, 0.5, 2, 15, 69, np.inf])
check_logp(
pm.SkewStudentT,
R,
{"a": rplusnonzero, "b": rplusnonzero, "mu": R, "sigma": rplusnonzero},
lambda value, a, b, mu, sigma: st.jf_skew_t.logpdf(value, a, b, mu, sigma),
)
@pytest.mark.skipif(
condition=(pytensor.config.floatX == "float32"),
reason="Fails on float32 due to numerical issues",
)
def test_studentt_logcdf(self):
check_logcdf(
pm.StudentT,
R,
{"nu": Rplus, "mu": R, "lam": Rplus},
lambda value, nu, mu, lam: st.t.logcdf(value, nu, mu, lam**-0.5),
)
check_logcdf(
pm.StudentT,
R,
{"nu": Rplus, "mu": R, "sigma": Rplus},
lambda value, nu, mu, sigma: st.t.logcdf(value, nu, mu, sigma),
)
def test_studentt_icdf(self):
check_icdf(
pm.StudentT,
{"nu": Rplusbig, "mu": R, "sigma": Rplusbig},
lambda q, nu, mu, sigma: st.t.ppf(q, nu, mu, sigma),
)
@pytest.mark.skipif(
condition=(pytensor.config.floatX == "float32"),
reason="Fails on float32 due to numerical issues",
)
def test_skewstudentt_logcdf(self):
check_logcdf(
pm.SkewStudentT,
R,
{"a": Rplus, "b": Rplus, "mu": R, "sigma": Rplusbig},
lambda value, a, b, mu, sigma: st.jf_skew_t.logcdf(value, a, b, mu, sigma),
)
def test_skewstudentt_icdf(self):
check_icdf(
pm.SkewStudentT,
{"a": Rplusbig, "b": Rplusbig, "mu": R, "sigma": Rplusbig},
lambda q, a, b, mu, sigma: st.jf_skew_t.ppf(q, a, b, mu, sigma),
)
def test_cauchy(self):
check_logp(
pm.Cauchy,
R,
{"alpha": R, "beta": Rplusbig},
lambda value, alpha, beta: st.cauchy.logpdf(value, alpha, beta),
)
check_logcdf(
pm.Cauchy,
R,
{"alpha": R, "beta": Rplusbig},
lambda value, alpha, beta: st.cauchy.logcdf(value, alpha, beta),
)
check_icdf(
pm.Cauchy,
{"alpha": R, "beta": Rplusbig},
lambda q, alpha, beta: st.cauchy.ppf(q, alpha, beta),
)
def test_half_cauchy(self):
check_logp(
pm.HalfCauchy,
Rplus,
{"beta": Rplusbig},
lambda value, beta: st.halfcauchy.logpdf(value, scale=beta),
)
check_logcdf(
pm.HalfCauchy,
Rplus,
{"beta": Rplusbig},
lambda value, beta: st.halfcauchy.logcdf(value, scale=beta),
)
check_icdf(
pm.HalfCauchy, {"beta": Rplusbig}, lambda q, beta: st.halfcauchy.ppf(q, scale=beta)
)
def test_gamma_logp(self):
check_logp(
pm.Gamma,
Rplus,
{"alpha": Rplusbig, "beta": Rplusbig},
lambda value, alpha, beta: st.gamma.logpdf(value, alpha, scale=1.0 / beta),
)
def test_fun(value, mu, sigma):
return st.gamma.logpdf(value, mu**2 / sigma**2, scale=1.0 / (mu / sigma**2))
check_logp(
pm.Gamma,
Rplus,
{"mu": Rplusbig, "sigma": Rplusbig},
test_fun,
)
@pytest.mark.skipif(
condition=(pytensor.config.floatX == "float32"),
reason="Fails on float32 due to numerical issues",
)
def test_gamma_logcdf(self):
check_logcdf(
pm.Gamma,
Rplus,
{"alpha": Rplusbig, "beta": Rplusbig},
lambda value, alpha, beta: st.gamma.logcdf(value, alpha, scale=1.0 / beta),
)
def test_gamma_icdf(self):
check_icdf(
pm.Gamma,
{"alpha": Rplusbig, "beta": Rplusbig},
lambda q, alpha, beta: st.gamma.ppf(q, alpha, scale=1.0 / beta),
)
def test_inverse_gamma_logp(self):
check_logp(
pm.InverseGamma,
Rplus,
{"alpha": Rplus, "beta": Rplus},
lambda value, alpha, beta: st.invgamma.logpdf(value, alpha, scale=beta),
)
def test_inverse_gamma_icdf(self):
check_icdf(
pm.InverseGamma,
{"alpha": Rplusbig, "beta": Rplusbig},
lambda q, alpha, beta: st.invgamma.ppf(q, alpha, scale=beta),
)
@pytest.mark.skipif(
condition=(pytensor.config.floatX == "float32"),
reason="Fails on float32 due to numerical issues",
)
def test_inverse_gamma_logcdf(self):
check_logcdf(
pm.InverseGamma,
Rplus,
{"alpha": Rplus, "beta": Rplus},
lambda value, alpha, beta: st.invgamma.logcdf(value, alpha, scale=beta),
)
@pytest.mark.skipif(
condition=(pytensor.config.floatX == "float32"),
reason="Fails on float32 due to scaling issues",
)
def test_inverse_gamma_alt_params(self):
def test_fun(value, mu, sigma):
alpha, beta = pm.InverseGamma._get_alpha_beta(None, None, mu, sigma)
return st.invgamma.logpdf(value, alpha, scale=beta)
check_logp(
pm.InverseGamma,
Rplus,
{"mu": Rplus, "sigma": Rplus},
test_fun,
decimal=select_by_precision(float64=4, float32=3),
)
def test_pareto(self):
check_logp(
pm.Pareto,
Rplus,
{"alpha": Rplusbig, "m": Rplusbig},
lambda value, alpha, m: st.pareto.logpdf(value, alpha, scale=m),
)
check_logcdf(
pm.Pareto,
Rplus,
{"alpha": Rplusbig, "m": Rplusbig},
lambda value, alpha, m: st.pareto.logcdf(value, alpha, scale=m),
)
check_icdf(
pm.Pareto,
{"alpha": Rplusbig, "m": Rplusbig},
lambda q, alpha, m: st.pareto.ppf(q, alpha, scale=m),
)
@pytest.mark.skipif(
condition=(pytensor.config.floatX == "float32"),
reason="Fails on float32 due to numerical issues",
)
def test_weibull_logp(self):
# SciPy has new (?) precision issues at {alpha=20, beta=2, x=100}
# We circumvent it by skipping alpha=20:
rplusbig = Domain([0, 0.5, 0.9, 0.99, 1, 1.5, 2, np.inf])
check_logp(
pm.Weibull,
Rplus,
{"alpha": rplusbig, "beta": Rplusbig},
lambda value, alpha, beta: st.exponweib.logpdf(value, 1, alpha, scale=beta),
)
@pytest.mark.skipif(
condition=(pytensor.config.floatX == "float32"),
reason="Fails on float32 due to inf issues",
)
def test_weibull_logcdf(self):
check_logcdf(
pm.Weibull,
Rplus,
{"alpha": Rplusbig, "beta": Rplusbig},
lambda value, alpha, beta: st.exponweib.logcdf(value, 1, alpha, scale=beta),
)
def test_weibull_icdf(self):
check_icdf(
pm.Weibull,
{"alpha": Rplusbig, "beta": Rplusbig},
lambda q, alpha, beta: st.exponweib.ppf(q, 1, alpha, scale=beta),
)
def test_half_studentt(self):
# this is only testing for nu=1 (halfcauchy)
check_logp(
pm.HalfStudentT,
Rplus,
{"sigma": Rplus},
lambda value, sigma: st.halfcauchy.logpdf(value, 0, sigma),
extra_args={"nu": 1},
)
def test_skew_normal(self):
check_logp(
pm.SkewNormal,
R,
{"mu": R, "sigma": Rplusbig, "alpha": R},
lambda value, alpha, mu, sigma: st.skewnorm.logpdf(value, alpha, mu, sigma),
decimal=select_by_precision(float64=5, float32=3),
)
@pytest.mark.parametrize(
"value,mu,sigma,nu,logcdf_val",
[
(0.5, -50.000, 0.500, 0.500, 0.0000000),
(1.0, -1.000, 0.001, 0.001, 0.0000000),
(2.0, 0.001, 1.000, 1.000, -0.2365674),
(5.0, 0.500, 2.500, 2.500, -0.2886489),
(7.5, 2.000, 5.000, 5.000, -0.5655104),
(15.0, 5.000, 7.500, 7.500, -0.4545255),
(50.0, 50.000, 10.000, 10.000, -1.433714),
(1000.0, 500.000, 10.000, 20.000, -1.573708e-11),
(0.01, 0.01, 100.0, 0.01, -0.69314718), # Fails in scipy version
(-0.43402407, 0.0, 0.1, 0.1, -13.59615423), # Previous 32-bit version failed here
(-0.72402009, 0.0, 0.1, 0.1, -31.26571842), # Previous 64-bit version failed here
],
)
def test_ex_gaussian_cdf(self, value, mu, sigma, nu, logcdf_val):
"""Log probabilities calculated using the pexGAUS function from the R package gamlss.
See e.g., doi: 10.1111/j.1467-9876.2005.00510.x, or http://www.gamlss.org/."""
npt.assert_almost_equal(
logcdf(pm.ExGaussian.dist(mu=mu, sigma=sigma, nu=nu), value).eval(),
logcdf_val,
decimal=select_by_precision(float64=6, float32=2),
err_msg=str((value, mu, sigma, nu, logcdf_val)),
)
def test_ex_gaussian_cdf_outside_edges(self):
check_logcdf(
pm.ExGaussian,
R,
{"mu": R, "sigma": Rplus, "nu": Rplus},
None,
skip_paramdomain_inside_edge_test=True, # Valid values are tested above
)
@pytest.mark.skipif(condition=(pytensor.config.floatX == "float32"), reason="Fails on float32")
def test_vonmises(self):
check_logp(
pm.VonMises,
Circ,
{"mu": R, "kappa": Rplus},
lambda value, mu, kappa: floatX(st.vonmises.logpdf(value, kappa, loc=mu)),
)
def test_gumbel(self):
check_logp(
pm.Gumbel,
R,
{"mu": R, "beta": Rplusbig},
lambda value, mu, beta: st.gumbel_r.logpdf(value, loc=mu, scale=beta),
)
check_logcdf(
pm.Gumbel,
R,
{"mu": R, "beta": Rplusbig},
lambda value, mu, beta: st.gumbel_r.logcdf(value, loc=mu, scale=beta),
)
check_icdf(
pm.Gumbel,
{"mu": R, "beta": Rplusbig},
lambda q, mu, beta: st.gumbel_r.ppf(q, loc=mu, scale=beta),
)
def test_logistic(self):
check_logp(
pm.Logistic,
R,
{"mu": R, "s": Rplus},
lambda value, mu, s: st.logistic.logpdf(value, mu, s),
decimal=select_by_precision(float64=6, float32=1),
)
check_logcdf(
pm.Logistic,
R,
{"mu": R, "s": Rplus},
lambda value, mu, s: st.logistic.logcdf(value, mu, s),
decimal=select_by_precision(float64=6, float32=1),
)
check_icdf(
pm.Logistic,
{"mu": R, "s": Rplus},
lambda q, mu, s: st.logistic.ppf(q, mu, s),
decimal=select_by_precision(float64=6, float32=1),
)
def test_logitnormal(self):
check_logp(
pm.LogitNormal,
Unit,
{"mu": R, "sigma": Rplus},
lambda value, mu, sigma: (
st.norm.logpdf(sp.logit(value), mu, sigma) - (np.log(value) + np.log1p(-value))
),
decimal=select_by_precision(float64=6, float32=1),
)
@pytest.mark.skipif(
condition=(pytensor.config.floatX == "float32"),
reason="Some combinations underflow to -inf in float32 in pymc version",
)
def test_rice(self):
check_logp(
pm.Rice,
Rplus,
{"b": Rplus, "sigma": Rplusbig},
lambda value, b, sigma: st.rice.logpdf(value, b=b, loc=0, scale=sigma),
)
if pytensor.config.floatX == "float32":
raise Exception("Flaky test: It passed this time, but XPASS is not allowed.")
def test_rice_nu(self):
check_logp(
pm.Rice,
Rplus,
{"nu": Rplus, "sigma": Rplusbig},
lambda value, nu, sigma: st.rice.logpdf(value, b=nu / sigma, loc=0, scale=sigma),
)
def test_moyal_logp(self):
# Using a custom domain, because the standard `R` domain underflows with scipy in float64
value_domain = Domain([-np.inf, -1.5, -1, -0.01, 0.0, 0.01, 1, 1.5, np.inf])
check_logp(
pm.Moyal,
value_domain,
{"mu": R, "sigma": Rplusbig},
lambda value, mu, sigma: floatX(st.moyal.logpdf(value, mu, sigma)),
)
@pytest.mark.skipif(
condition=(pytensor.config.floatX == "float32"),
reason="PyMC underflows earlier than scipy on float32",
)
def test_moyal_logcdf(self):
# SciPy has new (?) precision issues at {mu=-2.1, sigma=0.5, x=2.1}
# We circumvent it by skipping sigma=0.5:
rplusbig = Domain([0, 0.9, 0.99, 1, 1.5, 2, 20, np.inf])
check_logcdf(
pm.Moyal,
R,
{"mu": R, "sigma": rplusbig},
lambda value, mu, sigma: floatX(st.moyal.logcdf(value, mu, sigma)),
)
if pytensor.config.floatX == "float32":
raise Exception("Flaky test: It passed this time, but XPASS is not allowed.")
def test_moyal_icdf(self):
check_icdf(
pm.Moyal,
{"mu": R, "sigma": Rplusbig},
lambda q, mu, sigma: floatX(st.moyal.ppf(q, mu, sigma)),
)
def test_interpolated(self):
for mu in R.vals:
for sigma in Rplus.vals:
xmin = mu - 5 * sigma
xmax = mu + 5 * sigma
class TestedInterpolated(pm.Interpolated):
rv_op = interpolated
@classmethod
def dist(cls, **kwargs):
x_points = np.linspace(xmin, xmax, 100000)
pdf_points = st.norm.pdf(x_points, loc=mu, scale=sigma)
return super().dist(x_points=x_points, pdf_points=pdf_points, **kwargs)
def ref_pdf(value):
return np.where(
np.logical_and(value >= xmin, value <= xmax),
st.norm.logpdf(value, mu, sigma),
-np.inf * np.ones(value.shape),
)
check_logp(TestedInterpolated, R, {}, ref_pdf)
@pytest.mark.parametrize("transform", [pm.util.UNSET, None])
def test_interpolated_transform(self, transform):
# Issue: https://github.com/pymc-devs/pymc/issues/5048
x_points = np.linspace(0, 10, 10)
pdf_points = st.norm.pdf(x_points, loc=1, scale=1)
with pm.Model() as m:
x = pm.Interpolated("x", x_points, pdf_points, transform=transform)
if transform is pm.util.UNSET:
assert np.isfinite(m.compile_logp()({"x_interval__": -1.0}))
assert np.isfinite(m.compile_logp()({"x_interval__": 11.0}))
else:
assert not np.isfinite(m.compile_logp()({"x": -1.0}))
assert not np.isfinite(m.compile_logp()({"x": 11.0}))
def test_truncated_normal(self):
def scipy_logp(value, mu, sigma, lower, upper):
return st.truncnorm.logpdf(
value, (lower - mu) / sigma, (upper - mu) / sigma, loc=mu, scale=sigma
)
def scipy_logcdf(value, mu, sigma, lower, upper):
return st.truncnorm.logcdf(
value, (lower - mu) / sigma, (upper - mu) / sigma, loc=mu, scale=sigma
)
check_logp(
pm.TruncatedNormal,
R,
{"mu": R, "sigma": Rplusbig, "lower": -Rplusbig, "upper": Rplusbig},
scipy_logp,
decimal=select_by_precision(float64=6, float32=1),
skip_paramdomain_outside_edge_test=True,
)
check_logp(
pm.TruncatedNormal,
R,
{"mu": R, "sigma": Rplusbig, "upper": Rplusbig},