Maintenance will be performed on,,, and on the morning of Tuesday 11th August 2020, starting at approximately 9am PDT. It is expected to take around 20 minutes and there will be a short period of downtime (less than five minutes) towards the end of the maintenance window. Please direct any comments, questions, or concerns to

Commit 4a1fa9f8 authored by Colm Talbot's avatar Colm Talbot

Merge branch 'hyperpe_sampling_prior_format' into 'master'

Use the `PriorSet` from the first step of PE as the `sampling_prior` for hyper-pe

See merge request !545
parents 0b1aa313 47e69bec
Pipeline #70843 passed with stages
in 6 minutes and 18 seconds
......@@ -6,6 +6,7 @@ import numpy as np
from ..core.likelihood import Likelihood
from .model import Model
from ..core.prior import PriorDict
class HyperparameterLikelihood(Likelihood):
......@@ -32,12 +33,17 @@ class HyperparameterLikelihood(Likelihood):
def __init__(self, posteriors, hyper_prior, sampling_prior,
def __init__(self, posteriors, hyper_prior, sampling_prior=None,
log_evidences=None, max_samples=1e100):
if not isinstance(hyper_prior, Model):
hyper_prior = Model([hyper_prior])
if not isinstance(sampling_prior, Model):
sampling_prior = Model([sampling_prior])
if sampling_prior is None:
if ('log_prior' not in posteriors[0].keys()) and ('prior' not in posteriors[0].keys()):
raise ValueError('Missing both sampling prior function and prior or log_prior '
'column in posterior dictionary. Must pass one or the other.')
if not (isinstance(sampling_prior, Model) or isinstance(sampling_prior, PriorDict)):
sampling_prior = Model([sampling_prior])
if log_evidences is not None:
self.evidence_factor = np.sum(log_evidences)
......@@ -57,7 +63,7 @@ class HyperparameterLikelihood(Likelihood):
def log_likelihood_ratio(self):
log_l = np.sum(np.log(np.sum(self.hyper_prior.prob( /
self.sampling_prior.prob(, axis=-1)))['prior'], axis=-1)))
log_l += self.samples_factor
return np.nan_to_num(log_l)
......@@ -87,10 +93,18 @@ class HyperparameterLikelihood(Likelihood):
for posterior in self.posteriors:
self.max_samples = min(len(posterior), self.max_samples)
data = {key: [] for key in self.posteriors[0]}
if 'log_prior' in data.keys():
if 'prior' not in data.keys():
data['prior'] = []
logging.debug('Downsampling to {} samples per posterior.'.format(
for posterior in self.posteriors:
temp = posterior.sample(self.max_samples)
if self.sampling_prior is not None:
temp['prior'] = self.sampling_prior.prob(temp, axis=0)
elif 'log_prior' in temp.keys():
temp['prior'] = np.exp(temp['log_prior'])
for key in data:
for key in data:
......@@ -23,7 +23,7 @@ class Model(object):
for key in param_keys:
self.parameters[key] = None
def prob(self, data):
def prob(self, data, **kwargs):
probability = 1.0
for ii, function in enumerate(self.models):
probability *= function(data, **self._get_function_parameters(function))
Markdown is supported
0% or
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment