def test_pymc3(self):
# test objective logl against pymc3
# don't run this test if pymc3 is not installed
try:
import pymc3 as pm
except ImportError:
return
logl = self.objective.logl()
from refnx.analysis import pymc3_model
from refnx.analysis.objective import _to_pymc3_distribution
mod = pymc3_model(self.objective)
with mod:
pymc_logl = mod.logp({
"p0": self.p[0].value,
"p1": self.p[1].value
})
assert_allclose(logl, pymc_logl)
# now check some of the distributions
with pm.Model():
p = Parameter(1, bounds=(1, 10))
d = _to_pymc3_distribution("a", p)
assert_almost_equal(d.distribution.logp(2).eval(), p.logp(2))
assert_(np.isneginf(d.distribution.logp(-1).eval()))
q = Parameter(1, bounds=PDF(stats.uniform(1, 9)))
d = _to_pymc3_distribution("b", q)
assert_almost_equal(d.distribution.logp(2).eval(), q.logp(2))
assert_(np.isneginf(d.distribution.logp(-1).eval()))
p = Parameter(1, bounds=PDF(stats.uniform))
d = _to_pymc3_distribution("c", p)
assert_almost_equal(d.distribution.logp(0.5).eval(), p.logp(0.5))
p = Parameter(1, bounds=PDF(stats.norm))
d = _to_pymc3_distribution("d", p)
assert_almost_equal(d.distribution.logp(2).eval(), p.logp(2))
p = Parameter(1, bounds=PDF(stats.norm(1, 10)))
d = _to_pymc3_distribution("e", p)
assert_almost_equal(d.distribution.logp(2).eval(), p.logp(2))
def test_pymc3_sample(self):
# test sampling with pymc3
try:
import pymc3 as pm
from refnx.analysis import pymc3_model
except (ModuleNotFoundError, ImportError, AttributeError):
# can't run test if pymc3/theano not installed
return
with pymc3_model(self.objective):
s = pm.NUTS()
pm.sample(
200,
tune=100,
step=s,
discard_tuned_samples=True,
compute_convergence_checks=False,
random_seed=1,
)