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Sampler Additions and PSR Jump Proposals #127

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Aug 31, 2021
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Sampler Additions and PSR Jump Proposals #127

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6f6d014
PSR Jump Proposals
Hazboun6 Aug 24, 2021
84a11ec
generalized gw log-uniform JP and HM addition
Hazboun6 Aug 30, 2021
e898227
generalized sampler gw log-uniform addition
Hazboun6 Aug 30, 2021
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17 changes: 13 additions & 4 deletions enterprise_extensions/hypermodel.py
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Original file line number Diff line number Diff line change
Expand Up @@ -181,7 +181,10 @@ def setup_sampler(self, outdir='chains', resume=False, sample_nmodel=True,
ndim = len(self.param_names)

# initial jump covariance matrix
cov = np.diag(np.ones(ndim) * 1**2) ## used to be 0.1
if os.path.exists(outdir+'/cov.npy'):
cov = np.load(outdir+'/cov.npy')
else:
cov = np.diag(np.ones(ndim) * 1.0**2)## used to be 0.1
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Fantastic modification that we've needed for a while. Thanks Jeff.


# parameter groupings
if groups is None:
Expand All @@ -194,6 +197,7 @@ def setup_sampler(self, outdir='chains', resume=False, sample_nmodel=True,

# additional jump proposals
jp = JumpProposal(self, self.snames, empirical_distr=empirical_distr)
sampler.jp = jp

# always add draw from prior
sampler.addProposalToCycle(jp.draw_from_prior, 5)
Expand Down Expand Up @@ -238,6 +242,11 @@ def setup_sampler(self, outdir='chains', resume=False, sample_nmodel=True,
print('Adding Solar Wind DM GP prior draws...\n')
sampler.addProposalToCycle(jp.draw_from_dm_sw_prior, 10)

# Chromatic GP noise prior draw
if 'chrom_gp' in self.snames:
print('Adding Chromatic GP noise prior draws...\n')
sampler.addProposalToCycle(jp.draw_from_chrom_gp_prior, 10)

# Ephemeris prior draw
if 'd_jupiter_mass' in self.param_names:
print('Adding ephemeris model prior draws...\n')
Expand Down Expand Up @@ -277,10 +286,10 @@ def setup_sampler(self, outdir='chains', resume=False, sample_nmodel=True,
if any([str(p).split(':')[0] for p in list(self.params) if 'gw' in str(p)]):
print('Adding gw param prior draws...\n')
sampler.addProposalToCycle(jp.draw_from_par_prior(
par_names=[str(p).split(':')[0] for
p in list(self.params)
par_names=[str(p).split(':')[0] for
p in list(self.params)
if 'gw' in str(p)]), 10)

# Model index distribution draw
if sample_nmodel:
if 'nmodel' in self.param_names:
Expand Down
150 changes: 126 additions & 24 deletions enterprise_extensions/sampler.py
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Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -17,6 +17,7 @@ def __init__(self, pta, snames=None, empirical_distr=None, f_stat_file=None):
"""Set up some custom jump proposals"""
self.params = pta.params
self.pnames = pta.param_names
self.psrnames = pta.pulsars
self.ndim = sum(p.size or 1 for p in pta.params)
self.plist = [p.name for p in pta.params]

Expand Down Expand Up @@ -47,16 +48,16 @@ def __init__(self, pta, snames=None, empirical_distr=None, f_stat_file=None):
else:
self.snames = snames

# empirical distributions
# empirical distributions
if isinstance(empirical_distr, list):
# check if a list of emp dists is provided
self.empirical_distr = empirical_distr

# check if a directory of empirical dist pkl files are provided
elif empirical_distr is not None and os.path.isdir(empirical_distr):

dir_files = glob.glob(empirical_distr+'*.pkl') # search for pkls

pickled_distr = np.array([])
for idx, emp_file in enumerate(dir_files):
try:
Expand All @@ -71,9 +72,9 @@ def __init__(self, pta, snames=None, empirical_distr=None, f_stat_file=None):
print("Empirical distributions set to 'None'")
pickled_distr = None
break

self.empirical_distr = pickled_distr

# check if single pkl file provided
elif empirical_distr is not None and os.path.isfile(empirical_distr): # checking for single file
try:
Expand All @@ -91,7 +92,7 @@ def __init__(self, pta, snames=None, empirical_distr=None, f_stat_file=None):
pickled_distr = None

self.empirical_distr = pickled_distr

# all other cases - emp dists set to None
else:
self.empirical_distr = None
Expand All @@ -110,7 +111,7 @@ def __init__(self, pta, snames=None, empirical_distr=None, f_stat_file=None):
self.empirical_distr = [self.empirical_distr[m] for m in mask]
else:
self.empirical_distr = None

if empirical_distr is not None and self.empirical_distr is None:
# if an emp dist path is provided, but fails the code, this helpful msg is provided
print("Adding empirical distributions failed!! Empirical distributions set to 'None'\n")
Expand Down Expand Up @@ -203,6 +204,48 @@ def draw_from_empirical_distr(self, x, iter, beta):

return q, float(lqxy)

def draw_from_psr_empirical_distr(self, x, iter, beta):
q = x.copy()
lqxy = 0

if self.empirical_distr is not None:

# make list of empirical distributions with psr name
psr = np.random.choice(self.psrnames)
pnames = [ed.param_name if ed.ndim==1 else ed.param_names
for ed in self.empirical_distr ]

# Retrieve indices of emp dists with pulsar pars.
idxs = []
for par in pnames:
if isinstance(par,str):
if psr in par:
idxs.append(pnames.index(par))
elif isinstance(par,list):
if any([psr in p for p in par]):
idxs.append(pnames.index(par))

for idx in idxs:
if self.empirical_distr[idx].ndim == 1:
pidx = self.pimap[self.empirical_distr[idx].param_name]
q[pidx] = self.empirical_distr[idx].draw()

lqxy += (self.empirical_distr[idx].logprob(x[pidx]) -
self.empirical_distr[idx].logprob(q[pidx]))

else:
oldsample = [x[self.pnames.index(p)]
for p in self.empirical_distr[idx].param_names]
newsample = self.empirical_distr[idx].draw()

for p,n in zip(self.empirical_distr[idx].param_names, newsample):
q[self.pnames.index(p)] = n

lqxy += (self.empirical_distr[idx].logprob(oldsample) -
self.empirical_distr[idx].logprob(newsample))

return q, float(lqxy)

def draw_from_dm_gp_prior(self, x, iter, beta):

q = x.copy()
Expand Down Expand Up @@ -300,14 +343,41 @@ def draw_from_dmx_prior(self, x, iter, beta):

return q, float(lqxy)

def draw_from_chrom_gp_prior(self, x, iter, beta):

q = x.copy()
lqxy = 0

signal_name = 'chrom_gp'

# draw parameter from signal model
param = np.random.choice(self.snames[signal_name])
if param.size:
idx2 = np.random.randint(0, param.size)
q[self.pmap[str(param)]][idx2] = param.sample()[idx2]

# scalar parameter
else:
q[self.pmap[str(param)]] = param.sample()

# forward-backward jump probability
lqxy = (param.get_logpdf(x[self.pmap[str(param)]]) -
param.get_logpdf(q[self.pmap[str(param)]]))

return q, float(lqxy)

def draw_from_gwb_log_uniform_distribution(self, x, iter, beta):

q = x.copy()
lqxy = 0

# draw parameter from signal model
idx = self.pnames.index('gw_log10_A')
q[idx] = np.random.uniform(-18, -11)
try:
idx = self.pnames.index('gw_log10_A')
except ValueError:
idx = self.pnames.index('gw_crn_log10_A')

q[idx] = np.random.uniform(-18, -14)

return q, 0

Expand Down Expand Up @@ -605,6 +675,26 @@ def draw(x, iter, beta):
draw.__name__ = 'draw_from_{}_log_uniform'.format(name_string)
return draw

def draw_from_psr_prior(self, x, iter, beta):

q = x.copy()
lqxy = 0

# draw parameter from pulsar names
psr = np.random.choice(self.psrnames)
idxs = [self.pimap[par] for par in self.pnames if psr in par]
params = np.array(self.params)[idxs]
for idx in idxs:
q[idx] = self.params[idx].sample()

# forward-backward jump probability
first = np.sum([self.params[idx].get_logpdf(x[idx]) for idx in idxs])
last = np.sum([self.params[idx].get_logpdf(q[idx]) for idx in idxs])

lqxy = first - last

return q, float(lqxy)

def draw_from_signal(self, signal_names):
# Preparing and comparing signal_names with PTA signals
signal_names = np.atleast_1d(signal_names)
Expand Down Expand Up @@ -656,9 +746,9 @@ def fe_jump(self, x, iter, beta):

q = x.copy()
lqxy = 0

fe_limit = np.max(self.fe)

#draw skylocation and frequency from f-stat map
accepted = False
while accepted==False:
Expand All @@ -678,40 +768,40 @@ def fe_jump(self, x, iter, beta):
psi = self.params[self.pimap['psi']].sample()
phase0 = self.params[self.pimap['phase0']].sample()
log10_h = self.params[self.pimap['log10_h']].sample()


#put new parameters into q
signal_name = 'cw'
for param_name, new_param in zip(['log10_fgw','gwphi','cos_gwtheta','cos_inc','psi','phase0','log10_h'],
[log_f_new, gw_phi, np.cos(gw_theta), cos_inc, psi, phase0, log10_h]):
q[self.pimap[param_name]] = new_param

#calculate Hastings ratio
log_f_old = x[self.pimap['log10_fgw']]
f_idx_old = (np.abs(np.log10(self.fe_freqs) - log_f_old)).argmin()

gw_theta_old = np.arccos(x[self.pimap['cos_gwtheta']])
gw_phi_old = x[self.pimap['gwphi']]
hp_idx_old = hp.ang2pix(hp.get_nside(self.fe), gw_theta_old, gw_phi_old)

fe_old_point = self.fe[f_idx_old, hp_idx_old]
if fe_old_point>fe_limit:
fe_old_point = fe_limit

log10_h_old = x[self.pimap['log10_h']]
phase0_old = x[self.pimap['phase0']]
psi_old = x[self.pimap['psi']]
cos_inc_old = x[self.pimap['cos_inc']]

hastings_extra_factor = self.params[self.pimap['log10_h']].get_pdf(log10_h_old)
hastings_extra_factor *= 1/self.params[self.pimap['log10_h']].get_pdf(log10_h)
hastings_extra_factor = self.params[self.pimap['phase0']].get_pdf(phase0_old)
hastings_extra_factor *= 1/self.params[self.pimap['phase0']].get_pdf(phase0)
hastings_extra_factor = self.params[self.pimap['psi']].get_pdf(psi_old)
hastings_extra_factor *= 1/self.params[self.pimap['psi']].get_pdf(psi)
hastings_extra_factor = self.params[self.pimap['cos_inc']].get_pdf(cos_inc_old)
hastings_extra_factor *= 1/self.params[self.pimap['cos_inc']].get_pdf(cos_inc)
hastings_extra_factor *= 1/self.params[self.pimap['cos_inc']].get_pdf(cos_inc)

lqxy = np.log(fe_old_point/fe_new_point * hastings_extra_factor)

return q, float(lqxy)
Expand Down Expand Up @@ -753,6 +843,13 @@ def get_parameter_groups(pta):

return groups

def get_psr_groups(pta):
groups = []
for psr in pta.pulsars:
grp = [pta.param_names.index(par)
for par in pta.param_names if psr in par]
groups.append(grp)
return groups

def get_cw_groups(pta):
"""Utility function to get parameter groups for CW sampling.
Expand All @@ -779,7 +876,7 @@ def group_from_params(pta, params):
return gr


def setup_sampler(pta, outdir='chains', resume=False, empirical_distr=None):
def setup_sampler(pta, outdir='chains', resume=False, empirical_distr=None, groups=None):
"""
Sets up an instance of PTMCMC sampler.

Expand All @@ -804,10 +901,14 @@ def setup_sampler(pta, outdir='chains', resume=False, empirical_distr=None):
ndim = len(params)

# initial jump covariance matrix
cov = np.diag(np.ones(ndim) * 0.1**2)
if os.path.exists(outdir+'/cov.npy'):
cov = np.load(outdir+'/cov.npy')
else:
cov = np.diag(np.ones(ndim) * 0.1**2)

# parameter groupings
groups = get_parameter_groups(pta)
if groups is None:
groups = get_parameter_groups(pta)

sampler = ptmcmc(ndim, pta.get_lnlikelihood, pta.get_lnprior, cov, groups=groups,
outDir=outdir, resume=resume)
Expand All @@ -818,6 +919,7 @@ def setup_sampler(pta, outdir='chains', resume=False, empirical_distr=None):

# additional jump proposals
jp = JumpProposal(pta, empirical_distr=empirical_distr)
sampler.jp = jp

# always add draw from prior
sampler.addProposalToCycle(jp.draw_from_prior, 5)
Expand Down Expand Up @@ -863,7 +965,7 @@ def setup_sampler(pta, outdir='chains', resume=False, empirical_distr=None):
sampler.addProposalToCycle(jp.draw_from_ephem_prior, 10)

# GWB uniform distribution draw
if 'gw_log10_A' in pta.param_names:
if 'gw_log10_A' in pta.param_names or 'gw_crn_log10_A' in pta.param_names:
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Does this need to be even more general, @Hazboun6? This will work if the ORF is None such that the process is a crn, but in model_general the processes get named for their ORF behavior: https://github.com/nanograv/enterprise_extensions/blob/master/enterprise_extensions/models.py#L788. So a H&D process would have gw_hd_log10_A.

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You make a good point @stevertaylor . I've changed the jump proposal to cycle through any signals with "gw" and "log10_A" in the parameter names and now sampler.setup_sampler and HyperModel.setup_ampler do searches for the same string pieces in order to add the jump proposal.

See lines 369 and 968 of sampler.py and line 256 of hypermodel.py.

print('Adding GWB uniform distribution draws...\n')
sampler.addProposalToCycle(jp.draw_from_gwb_log_uniform_distribution, 10)

Expand Down