Merge branch 'pymc3_to_pymc4' into 'master'

Switch PyMC3 to PyMC (which is the new name starting from PyMC v4.0.0)

See merge request !1117

bilby/core/sampler/__init__.py

@@ -21,7 +21,7 @@ from .nestle import Nestle
 from .polychord import PyPolyChord
 from .ptemcee import Ptemcee
 from .ptmcmc import PTMCMCSampler
-from .pymc3 import Pymc3
+from .pymc import Pymc
 from .pymultinest import Pymultinest
 from .ultranest import Ultranest
 from .zeus import Zeus
@@ -38,7 +38,7 @@ IMPLEMENTED_SAMPLERS = {
     "nestle": Nestle,
     "ptemcee": Ptemcee,
     "ptmcmcsampler": PTMCMCSampler,
-    "pymc3": Pymc3,
+    "pymc": Pymc,
     "pymultinest": Pymultinest,
     "pypolychord": PyPolyChord,
     "ultranest": Ultranest,

docs/samplers.txt

@@ -69,7 +69,7 @@ MCMC samplers
 - bilby-mcmc :code:`bilby.bilby_mcmc.sampler.Bilby_MCMC`
 - emcee :code:`bilby.core.sampler.emcee.Emcee`
 - ptemcee :code:`bilby.core.sampler.ptemcee.Ptemcee`
-- pymc3 :code:`bilby.core.sampler.pymc3.Pymc3`
+- pymc :code:`bilby.core.sampler.pymc.Pymc`
 - zeus :code:`bilby.core.sampler.zeus.Zeus`
 
 

examples/core_examples/alternative_samplers/linear_regression_pymc3.py → examples/core_examples/alternative_samplers/linear_regression_pymc.py

@@ -11,7 +11,7 @@ import numpy as np
 from bilby.core.likelihood import GaussianLikelihood
 
 # A few simple setup steps
-label = "linear_regression_pymc3"
+label = "linear_regression_pymc"
 outdir = "outdir"
 bilby.utils.check_directory_exists_and_if_not_mkdir(outdir)
 
@@ -58,7 +58,7 @@ priors["c"] = bilby.core.prior.Uniform(-2, 2, "c")
 result = bilby.run_sampler(
     likelihood=likelihood,
     priors=priors,
-    sampler="pymc3",
+    sampler="pymc",
     injection_parameters=injection_parameters,
     outdir=outdir,
     draws=2000,

examples/core_examples/alternative_samplers/linear_regression_pymc3_custom_likelihood.py → examples/core_examples/alternative_samplers/linear_regression_pymc_custom_likelihood.py

@@ -11,10 +11,10 @@ would give equivalent results as using the pre-defined 'Gaussian Likelihood'
 import bilby
 import matplotlib.pyplot as plt
 import numpy as np
-import pymc3 as pm
+import pymc as pm
 
 # A few simple setup steps
-label = "linear_regression_pymc3_custom_likelihood"
+label = "linear_regression_pymc_custom_likelihood"
 outdir = "outdir"
 bilby.utils.check_directory_exists_and_if_not_mkdir(outdir)
 
@@ -50,7 +50,7 @@ fig.savefig("{}/{}_data.png".format(outdir, label))
 
 # Parameter estimation: we now define a Gaussian Likelihood class relevant for
 # our model.
-class GaussianLikelihoodPyMC3(bilby.core.likelihood.GaussianLikelihood):
+class GaussianLikelihoodPyMC(bilby.core.likelihood.GaussianLikelihood):
     def __init__(self, x, y, sigma, func):
         """
         A general Gaussian likelihood - the parameters are inferred from the
@@ -68,45 +68,44 @@ class GaussianLikelihoodPyMC3(bilby.core.likelihood.GaussianLikelihood):
             will require a prior and will be sampled over (unless a fixed
             value is given).
         """
-        super(GaussianLikelihoodPyMC3, self).__init__(x=x, y=y, func=func, sigma=sigma)
+        super(GaussianLikelihoodPyMC, self).__init__(x=x, y=y, func=func, sigma=sigma)
 
     def log_likelihood(self, sampler=None):
         """
         Parameters
         ----------
-        sampler: :class:`bilby.core.sampler.Pymc3`
+        sampler: :class:`bilby.core.sampler.Pymc`
             A Sampler object must be passed containing the prior distributions
             and PyMC3 :class:`~pymc3.Model` to use as a context manager.
             If this is not passed, the super class is called and the regular
             likelihood is evaluated.
         """
 
-        from bilby.core.sampler import Pymc3
+        from bilby.core.sampler import Pymc
 
-        if not isinstance(sampler, Pymc3):
-            print(sampler, type(sampler))
-            return super(GaussianLikelihoodPyMC3, self).log_likelihood()
+        if not isinstance(sampler, Pymc):
+            return super(GaussianLikelihoodPyMC, self).log_likelihood()
 
-        if not hasattr(sampler, "pymc3_model"):
-            raise AttributeError("Sampler has not PyMC3 model attribute")
+        if not hasattr(sampler, "pymc_model"):
+            raise AttributeError("Sampler has not PyMC model attribute")
 
-        with sampler.pymc3_model:
-            mdist = sampler.pymc3_priors["m"]
-            cdist = sampler.pymc3_priors["c"]
+        with sampler.pymc_model:
+            mdist = sampler.pymc_priors["m"]
+            cdist = sampler.pymc_priors["c"]
 
             mu = model(time, mdist, cdist)
 
             # set the likelihood distribution
-            pm.Normal("likelihood", mu=mu, sd=self.sigma, observed=self.y)
+            pm.Normal("likelihood", mu=mu, sigma=self.sigma, observed=self.y)
 
 
 # Now lets instantiate a version of our GaussianLikelihood, giving it
 # the time, data and signal model
-likelihood = GaussianLikelihoodPyMC3(time, data, sigma, model)
+likelihood = GaussianLikelihoodPyMC(time, data, sigma, model)
 
 
-# Define a custom prior for one of the parameter for use with PyMC3
-class PyMC3UniformPrior(bilby.core.prior.Uniform):
+# Define a custom prior for one of the parameter for use with PyMC
+class PyMCUniformPrior(bilby.core.prior.Uniform):
     def __init__(self, minimum, maximum, name=None, latex_label=None):
         """
         Uniform prior with bounds (should be equivalent to bilby.prior.Uniform)
@@ -124,10 +123,10 @@ class PyMC3UniformPrior(bilby.core.prior.Uniform):
         float or array to be passed to the superclass.
         """
 
-        from bilby.core.sampler import Pymc3
+        from bilby.core.sampler import Pymc
 
-        if not isinstance(sampler, Pymc3):
-            return super(PyMC3UniformPrior, self).ln_prob(sampler)
+        if not isinstance(sampler, Pymc):
+            return super(PyMCUniformPrior, self).ln_prob(sampler)
 
         return pm.Uniform(self.name, lower=self.minimum, upper=self.maximum)
 
@@ -136,13 +135,13 @@ class PyMC3UniformPrior(bilby.core.prior.Uniform):
 # We make a prior
 priors = dict()
 priors["m"] = bilby.core.prior.Uniform(0, 5, "m")
-priors["c"] = PyMC3UniformPrior(-2, 2, "c")
+priors["c"] = PyMCUniformPrior(-2, 2, "c")
 
 # And run sampler
 result = bilby.run_sampler(
     likelihood=likelihood,
     priors=priors,
-    sampler="pymc3",
+    sampler="pymc",
     draws=1000,
     tune=1000,
     discard_tuned_samples=True,

sampler_requirements.txt

@@ -3,7 +3,7 @@ dynesty
 emcee
 nestle
 ptemcee
-pymc3>=3.6
+pymc>=4.0.0
 pymultinest
 kombine
 ultranest>=3.0.0

test/core/sampler/pymc3_test.py → test/core/sampler/pymc_test.py

 import unittest
 from unittest.mock import MagicMock
 
-import pytest
-
 import bilby
 
 
-@pytest.mark.xfail(
-    raises=AttributeError,
-    reason="Dependency issue with pymc3 causes attribute error on import",
-)
-class TestPyMC3(unittest.TestCase):
+class TestPyMC(unittest.TestCase):
     def setUp(self):
         self.likelihood = MagicMock()
         self.priors = bilby.core.prior.PriorDict(
             dict(a=bilby.core.prior.Uniform(0, 1), b=bilby.core.prior.Uniform(0, 1))
         )
-        self.sampler = bilby.core.sampler.Pymc3(
+        self.sampler = bilby.core.sampler.Pymc(
             self.likelihood,
             self.priors,
             outdir="outdir",
@@ -37,7 +31,7 @@ class TestPyMC3(unittest.TestCase):
             step=None,
             init="auto",
             n_init=200000,
-            start=None,
+            initvals=None,
             trace=None,
             chain_idx=0,
             chains=2,
@@ -61,7 +55,7 @@ class TestPyMC3(unittest.TestCase):
             step=None,
             init="auto",
             n_init=200000,
-            start=None,
+            initvals=None,
             trace=None,
             chain_idx=0,
             chains=2,

test/integration/sampler_run_test.py

@@ -54,7 +54,7 @@ _sampler_kwargs = dict(
         frac_threshold=0.5,
     ),
     PTMCMCSampler=dict(Niter=101, burn=2, isave=100),
-    # pymc3=dict(draws=50, tune=50, n_init=250),  removed until testing issue can be resolved
+    pymc=dict(draws=50, tune=50, n_init=250),
     pymultinest=dict(nlive=100),
     pypolychord=dict(nlive=100),
     ultranest=dict(nlive=100, temporary_directory=False),
@@ -65,7 +65,7 @@ sampler_imports = dict(
     dynamic_dynesty="dynesty"
 )
 
-no_pool_test = ["dnest4", "pymultinest", "nestle", "ptmcmcsampler", "pypolychord", "ultranest"]
+no_pool_test = ["dnest4", "pymultinest", "nestle", "ptmcmcsampler", "pypolychord", "ultranest", "pymc"]
 
 
 def slow_func(x, m, c):