From d7ffd8853eef2f0d5fbd7e122d9b73b31048b566 Mon Sep 17 00:00:00 2001
From: "rhys.green" <rhys.green@ligo.org>
Date: Wed, 9 Jan 2019 23:12:59 +0000
Subject: [PATCH] fix import error, and added sampler_meta, also fixed flake8
stuff
---
bilby/core/sampler/ptmcmc.py | 72 ++++++++++++++++++++----------------
1 file changed, 41 insertions(+), 31 deletions(-)
diff --git a/bilby/core/sampler/ptmcmc.py b/bilby/core/sampler/ptmcmc.py
index 95074f89..b75bf1c8 100644
--- a/bilby/core/sampler/ptmcmc.py
+++ b/bilby/core/sampler/ptmcmc.py
@@ -4,7 +4,7 @@ import numpy as np
# from pandas import DataFrame
# from ..utils import logger, get_progress_bar
-from .base_sampler import MCMCSampler
+from .base_sampler import MCMCSampler, SamplerNotInstalledError
class PTMCMCSampler(MCMCSampler):
@@ -38,16 +38,16 @@ class PTMCMCSampler(MCMCSampler):
verbose - Update current run-status to the screen (default=False)
"""
- default_kwargs = {'p0': None, 'Niter': 10**4 + 1, 'neff': 10**4,
+ default_kwargs = {'p0': None, 'Niter': 2 * 10**4 + 1, 'neff': 10**4,
'burn': 5 * 10**3, 'verbose': True,
'ladder': None, 'Tmin': 1, 'Tmax': None, 'Tskip': 100,
- 'isave': 1000, 'thin': 1, 'covUpdate': 500,
- 'SCAMweight': 0, 'AMweight': 1, 'DEweight': 1,
+ 'isave': 1000, 'thin': 1, 'covUpdate': 1000,
+ 'SCAMweight': 1, 'AMweight': 1, 'DEweight': 1,
'HMCweight': 0, 'MALAweight': 0, 'NUTSweight': 0,
'HMCstepsize': 0.1, 'HMCsteps': 300,
'groups': None, 'custom_proposals': None,
'loglargs': {}, 'loglkwargs': {}, 'logpargs': {}, 'logpkwargs': {},
- 'logl_grad': None, 'logp_grad': None, 'outDir': './outdir'}
+ 'logl_grad': None, 'logp_grad': None, 'outDir': './temp'}
def __init__(self, likelihood, priors, outdir='outdir', label='label', use_ratio=False, plot=False,
skip_import_verification=False, pos0=None, nburn=None, burn_in_fraction=0.25, **kwargs):
@@ -61,6 +61,15 @@ class PTMCMCSampler(MCMCSampler):
self.likelihood = likelihood
self.priors = priors
+ # PTMCMC is imported with Caps so need to overwrite this.
+ def _verify_external_sampler(self):
+ external_sampler_name = self.__class__.__name__
+ try:
+ self.external_sampler = __import__(external_sampler_name)
+ except (ImportError, SystemExit):
+ raise SamplerNotInstalledError(
+ "Sampler {} is not installed on this system".format(external_sampler_name))
+
@property
def kwargs(self):
""" Ensures that proper keyword arguments are used for the PTMCMC sampler.
@@ -136,14 +145,6 @@ class PTMCMCSampler(MCMCSampler):
def nburn(self):
return self.kwargs['burn']
- @nsteps.setter
- def nsteps(self, nsteps):
- self.kwargs['Niter'] = nsteps
-
- @nburn.setter
- def nburn(self, nsteps):
- self.kwargs['burn'] = nburn
-
@staticmethod
def _import_external_sampler():
from PTMCMCSampler import PTMCMCSampler
@@ -163,8 +164,6 @@ class PTMCMCSampler(MCMCSampler):
sampler = PTMCMCSampler.PTSampler(ndim=self.ndim, logp=self.log_prior,
logl=self.log_likelihood, cov=np.eye(self.ndim),
**init_kwargs)
- # tqdm = get_progress_bar()
-
if self.custom_proposals is not None:
for proposal in self.custom_proposals:
print('adding ' + str(proposal) + ' to proposals with weight:'
@@ -173,32 +172,43 @@ class PTMCMCSampler(MCMCSampler):
self.custom_proposals[proposal][1])
else:
pass
+ print(self.p0)
sampler.sample(p0=self.p0, **sampler_kwargs)
# The next bit is very hacky, the ptmcmc writes the samples and
# other info to file so here i read this info, write it to the result
# object then delete it
- data = np.loadtxt('outdir/chain_1.txt')
- # jumpfiles = glob.glob('ptmcmc_test/*jump.txt')
- # jumps = map(np.loadtxt, jumpfiles)
+ data = np.loadtxt('temp/chain_1.txt')
+ jumpfiles = glob.glob('temp/*jump.txt')
+ jumps = map(np.loadtxt, jumpfiles)
samples = data[:, :-4]
- # log_post = data[:, -4]
loglike = data[:, -3]
- # acceptance_rate = data[:,-2]
- # pt_swap_accept = data[:,-1]
- # for f in glob.glob('./ptmcmc_test/chain*'):
- # os.remove('./outdir/chain_1.txt')
- # os.remove('./outdir/cov.npy')
- # os.rmdir('ptmcmc_test')
- self.result.sampler_output = np.nan
+ meta = {}
+ jump_accept = {}
+ for ct, j in enumerate(jumps):
+ label = jumpfiles[ct].split('/')[-1].split('_jump.txt')[0]
+ jump_accept[label] = j
+ PT_swap = {'swap_accept': data[-1]}
+ tot_accept = {'tot_accept': data[-2]}
+ log_post = {'log_post': data[:, -4]}
+ meta['tot_accept'] = tot_accept
+ meta['PT_swap'] = PT_swap
+ meta['proposals'] = jump_accept
+ meta['log_post'] = log_post
+
+ samples = data[:, :-4]
+ for f in glob.glob('./temp/*'):
+ os.remove(f)
+ os.rmdir('temp')
+
+ self.result.nburn = self.nburn
+ self.result.samples = samples[self.nburn:]
+ self.meta_data['sampler_meta'] = meta
self.result.log_likelihood_evaluations = loglike[self.nburn:]
# self.calculate_autocorrelation(sampler.chain.reshape((-1, self.ndim)))
# self.print_nburn_logging_info()
- self.result.nburn = self.nburn
- self.result.samples = samples[self.nburn:]
- # Walkers isn't really applicable here but appears to be needed to
- # turn samples into data frame
- self.result.walkers = samples[self.nburn:]
+ self.result.sampler_output = np.nan
+ self.result.walkers = np.nan
self.result.log_evidence = np.nan
self.result.log_evidence_err = np.nan
return self.result
--
GitLab