def test_SmoothingPrior__global_eta():
# Non-log distributions shouldn't get the global eta
f = DummyForm({
"eta": {
"priors": 0.001,
"data": 0.001
},
"prior": {
"prior_type": "value",
"density": "uniform",
"min": 0,
"max": 10
},
})
f.validate_and_normalize()
assert f.prior.prior_object == priors.Uniform(0, 10)
# But log distributions which don't have their own eta should
f = DummyForm({
"eta": {
"priors": 0.001,
"data": 0.001
},
"prior": {
"prior_type": "value",
"density": "log_gaussian",
"mean": 0,
"std": 0.01
},
})
f.validate_and_normalize()
assert f.prior.prior_object == priors.LogGaussian(0, 0.01, eta=0.001)
# But if they do have their own eta, they shouldn't
f = DummyForm({
"eta": {
"priors": 0.001,
"data": 0.001
},
"prior": {
"prior_type": "value",
"density": "log_gaussian",
"mean": 0,
"std": 0.01,
"eta": 0.002
},
})
f.validate_and_normalize()
assert f.prior.prior_object == priors.LogGaussian(0, 0.01, eta=0.002)
def test_SmoothingPrior__full_form_validation__success():
f = DummyForm({
"prior": {
"prior_type": "value",
"density": "uniform",
"min": 0,
"max": 10
}
})
assert not f.validate_and_normalize()
assert f.prior.prior_object == priors.Uniform(0, 10)
f = DummyForm({
"prior": {
"prior_type": "value",
"density": "log_gaussian",
"mean": 0,
"std": 1,
"eta": 1
}
})
assert not f.validate_and_normalize()
assert f.prior.prior_object == priors.LogGaussian(0, 1, 1)
def _full_form_validation(self, root):
errors = []
if not self.is_field_unset("age_lower") and not self.is_field_unset(
"age_lower"):
if self.age_lower > self.age_upper:
errors.append(
"age_lower must be less than or equal to age_upper")
if not self.is_field_unset("time_lower") and not self.is_field_unset(
"time_lower"):
if self.time_lower > self.time_upper:
errors.append(
"time_lower must be less than or equal to time_upper")
try:
lower = self.min
upper = self.max
mean = self.mean
if mean is None and (np.isinf(lower) or np.isinf(upper)):
mean = max(lower, 0)
std = self.std
if self.nu is None:
if self.density == "students" and not root.is_field_unset(
"students_dof"):
nu = root.students_dof.priors
elif self.density == "log_students" and not root.is_field_unset(
"log_students_dof"):
nu = root.log_students_dof.priors
else:
nu = None
else:
nu = self.nu
if self.eta is None:
if not root.is_field_unset("eta"):
eta = root.eta.priors
else:
eta = None
else:
eta = self.eta
if self.density == "uniform":
self.prior_object = priors.Uniform(lower, upper, mean)
elif self.density == "gaussian":
self.prior_object = priors.Gaussian(mean, std, lower, upper)
elif self.density == "laplace":
self.prior_object = priors.Laplace(mean, std, lower, upper)
elif self.density == "students":
self.prior_object = priors.StudentsT(mean, std, nu, lower,
upper)
elif self.density == "log_gaussian":
self.prior_object = priors.LogGaussian(mean, std, eta, lower,
upper)
elif self.density == "log_laplace":
self.prior_object = priors.LogLaplace(mean, std, eta, lower,
upper)
elif self.density == "log_students":
self.prior_object = priors.LogStudentsT(
mean, std, nu, eta, lower, upper)
else:
errors.append(f"Unknown density '{self.density}'")
except priors.PriorError as e:
errors.append(f"Parameters incompatible with density '"
f"{self.density}': {str(e)}")
return errors