From de6ab27a14dc6e30d9e00e1e30371a3c37e5877f Mon Sep 17 00:00:00 2001
From: Gregory Ashton <gregory.ashton@ligo.org>
Date: Tue, 20 Apr 2021 09:48:46 +0000
Subject: [PATCH] Improvements to the core prior class
Cherry picked results from !905:
- Add ignore errstate to avoid warning messages
- Add helper properties for the width, non_fixed_keys, fixed_keys, and constraint_keys
- Add constraints to the ConditionalPrior
---
bilby/core/prior/analytical.py | 7 +++++--
bilby/core/prior/base.py | 7 ++++++-
bilby/core/prior/dict.py | 33 +++++++++++++++++++++++++++++++--
3 files changed, 42 insertions(+), 5 deletions(-)
diff --git a/bilby/core/prior/analytical.py b/bilby/core/prior/analytical.py
index 63ce9683b..1c3da1edc 100644
--- a/bilby/core/prior/analytical.py
+++ b/bilby/core/prior/analytical.py
@@ -148,8 +148,11 @@ class PowerLaw(Prior):
normalising = (1 + self.alpha) / (self.maximum ** (1 + self.alpha) -
self.minimum ** (1 + self.alpha))
- return (self.alpha * np.nan_to_num(np.log(val)) + np.log(normalising)) + np.log(
- 1. * self.is_in_prior_range(val))
+ with np.errstate(divide='ignore', invalid='ignore'):
+ ln_in_range = np.log(1. * self.is_in_prior_range(val))
+ ln_p = self.alpha * np.nan_to_num(np.log(val)) + np.log(normalising)
+
+ return ln_p + ln_in_range
def cdf(self, val):
if self.alpha == -1:
diff --git a/bilby/core/prior/base.py b/bilby/core/prior/base.py
index ab3f2e128..023f1609f 100644
--- a/bilby/core/prior/base.py
+++ b/bilby/core/prior/base.py
@@ -185,7 +185,8 @@ class Prior(object):
np.nan
"""
- return np.log(self.prob(val))
+ with np.errstate(divide='ignore'):
+ return np.log(self.prob(val))
def is_in_prior_range(self, val):
"""Returns True if val is in the prior boundaries, zero otherwise
@@ -313,6 +314,10 @@ class Prior(object):
def maximum(self, maximum):
self._maximum = maximum
+ @property
+ def width(self):
+ return self.maximum - self.minimum
+
def get_instantiation_dict(self):
subclass_args = infer_args_from_method(self.__init__)
dict_with_properties = get_dict_with_properties(self)
diff --git a/bilby/core/prior/dict.py b/bilby/core/prior/dict.py
index c38c047d7..5e50d3771 100644
--- a/bilby/core/prior/dict.py
+++ b/bilby/core/prior/dict.py
@@ -367,6 +367,28 @@ class PriorDict(dict):
logger.debug('{} not a known prior.'.format(key))
return samples
+ @property
+ def non_fixed_keys(self):
+ keys = self.keys()
+ keys = [k for k in keys if isinstance(self[k], Prior)]
+ keys = [k for k in keys if self[k].is_fixed is False]
+ keys = [k for k in keys if k not in self.constraint_keys]
+ return keys
+
+ @property
+ def fixed_keys(self):
+ return [
+ k for k, p in self.items()
+ if (p.is_fixed and k not in self.constraint_keys)
+ ]
+
+ @property
+ def constraint_keys(self):
+ return [
+ k for k, p in self.items()
+ if isinstance(p, Constraint)
+ ]
+
def sample_subset_constrained(self, keys=iter([]), size=None):
if size is None or size == 1:
while True:
@@ -432,6 +454,9 @@ class PriorDict(dict):
prob = np.product([self[key].prob(sample[key])
for key in sample], **kwargs)
+ return self.check_prob(sample, prob)
+
+ def check_prob(self, sample, prob):
ratio = self.normalize_constraint_factor(tuple(sample.keys()))
if np.all(prob == 0.):
return prob
@@ -465,7 +490,9 @@ class PriorDict(dict):
"""
ln_prob = np.sum([self[key].ln_prob(sample[key])
for key in sample], axis=axis)
+ return self.check_ln_prob(sample, ln_prob)
+ def check_ln_prob(self, sample, ln_prob):
ratio = self.normalize_constraint_factor(tuple(sample.keys()))
if np.all(np.isinf(ln_prob)):
return ln_prob
@@ -648,7 +675,8 @@ class ConditionalPriorDict(PriorDict):
for key, value in sample.items():
self[key].least_recently_sampled = value
res = [self[key].prob(sample[key], **self.get_required_variables(key)) for key in sample]
- return np.product(res, **kwargs)
+ prob = np.product(res, **kwargs)
+ return self.check_prob(sample, prob)
def ln_prob(self, sample, axis=None):
"""
@@ -669,7 +697,8 @@ class ConditionalPriorDict(PriorDict):
for key, value in sample.items():
self[key].least_recently_sampled = value
res = [self[key].ln_prob(sample[key], **self.get_required_variables(key)) for key in sample]
- return np.sum(res, axis=axis)
+ ln_prob = np.sum(res, axis=axis)
+ return self.check_ln_prob(sample, ln_prob)
def rescale(self, keys, theta):
"""Rescale samples from unit cube to prior
--
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