From 5b1a57782cbc30025e1b32779f411cfa725c8d4f Mon Sep 17 00:00:00 2001
From: Bruce Edelman <bruce.edelman@ligo.org>
Date: Thu, 19 Mar 2020 09:38:10 -0700
Subject: [PATCH] change from lru_cached to just a plain dictionary approach
for ease of application
---
bilby/core/prior/dict.py | 45 ++++++++++++++++++----------------------
1 file changed, 20 insertions(+), 25 deletions(-)
diff --git a/bilby/core/prior/dict.py b/bilby/core/prior/dict.py
index 6e57ce0bc..b715e12fc 100644
--- a/bilby/core/prior/dict.py
+++ b/bilby/core/prior/dict.py
@@ -3,7 +3,6 @@ from io import open as ioopen
import json
import numpy as np
import os
-from functools import lru_cache
from future.utils import iteritems
from matplotlib.cbook import flatten
@@ -42,6 +41,7 @@ class PriorDict(dict):
self.from_file(filename)
elif dictionary is not None:
raise ValueError("PriorDict input dictionary not understood")
+ self.cached_normalizations= {}
self.convert_floats_to_delta_functions()
@@ -379,23 +379,27 @@ class PriorDict(dict):
if not isinstance(self[key], Constraint)}
return all_samples
- @lru_cache()
def normalize_constraint_factor(self, keys):
- min_accept = 1000
- sampling_chunk = 5000
- samples = self.sample_subset(keys=keys, size=sampling_chunk)
- keep = np.atleast_1d(self.evaluate_constraints(samples))
- if len(keep) == 1:
- return 1
- all_samples = {key: np.array([]) for key in keys}
- _first_key = list(all_samples.keys())[0]
- while np.count_nonzero(keep) < min_accept:
+ if repr(keys) in self.cached_norm_factors.keys():
+ return self._cached_normalizations[repr(keys)]
+ else:
+ min_accept = 1000
+ sampling_chunk = 5000
samples = self.sample_subset(keys=keys, size=sampling_chunk)
- for key in samples:
- all_samples[key] = np.hstack(
- [all_samples[key], samples[key].flatten()])
- keep = np.array(self.evaluate_constraints(all_samples), dtype=bool)
- return len(keep) / np.count_nonzero(keep)
+ keep = np.atleast_1d(self.evaluate_constraints(samples))
+ if len(keep) == 1:
+ return 1
+ all_samples = {key: np.array([]) for key in keys}
+ _first_key = list(all_samples.keys())[0]
+ while np.count_nonzero(keep) < min_accept:
+ samples = self.sample_subset(keys=keys, size=sampling_chunk)
+ for key in samples:
+ all_samples[key] = np.hstack(
+ [all_samples[key], samples[key].flatten()])
+ keep = np.array(self.evaluate_constraints(all_samples), dtype=bool)
+ factor = len(keep) / np.count_nonzero(keep)
+ self._cached_normalizations[repr(keys)] = factor
+ return factor
def prob(self, sample, **kwargs):
"""
@@ -511,15 +515,6 @@ class PriorDict(dict):
"""
return self.__class__(dictionary=dict(self))
- def __key(self):
- return tuple((k, self[k]) for k in sorted(self))
-
- def __hash__(self):
- return hash(self.__key())
-
- def __eq__(self, other):
- return self.__key() == other.__key()
-
class PriorSet(PriorDict):
--
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