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add keywords for setting up dynesty and nautilus #389

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0f14f82
add keywords for setting up dynesty and nautilus
wangbingjie Jan 3, 2026
f7c9611
add checkpointing option for nautilus
wangbingjie Jan 6, 2026
86090eb
Merge branch 'bd-j:main' into bw-sampler
wangbingjie Jan 29, 2026
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43 changes: 42 additions & 1 deletion prospect/fitting/fitting.py
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Original file line number Diff line number Diff line change
Expand Up @@ -6,7 +6,7 @@
"""


import time
import time, sys
from functools import partial as argfix

import numpy as np
Expand Down Expand Up @@ -431,6 +431,19 @@ def run_nested(observations, model, sps,
nested_sampler="dynesty",
nested_nlive=1000,
nested_target_n_effective=1000,
nested_n_like_max=sys.maxsize,
nested_dlogz=0.01,
nested_nlive_batch=200,
nested_maxiter=sys.maxsize,
nested_maxcall=sys.maxsize,
nested_maxiter_init=sys.maxsize,
nested_maxcall_init=sys.maxsize,
nested_maxiter_batch=sys.maxsize,
nested_maxcall_batch=sys.maxsize,
nested_maxbatch=sys.maxsize,
nested_wt_kwargs={'pfrac': 1.0},
nested_filepath=None,
nested_resume=True,
verbose=False,
**kwargs):
"""Thin wrapper on :py:class:`prospect.fitting.nested.run_nested_sampler`
Expand Down Expand Up @@ -461,6 +474,24 @@ def run_nested(observations, model, sps,
Number of live points for the nested sampler. Meaning somewhat
dependent on the chosen sampler

nautilus-specific parameters
----------------------------
nested_n_like_max : int
Maximum number of likelihood evaluations for nautilus.
nested_filepath : str or None
Path to the checkpointing file. Must have a `.h5` or `.hdf5` extension. If None, no checkpointing are performed. Default is None.
nested_resume : bool
If True, resume from previous run if filepath exists. If False, start from scratch and overwrite any previous file. Default is True.

dynesty-specific parameters
----------------------------
nested_maxcall : int
Maximum number of likelihood evaluations for dynesty.
nested_maxiter : int
Maximum number of iterations for dynesty.

----------------------------

Returns
--------
result: Dictionary
Expand All @@ -481,6 +512,16 @@ def run_nested(observations, model, sps,
verbose=verbose,
nested_nlive=nested_nlive,
nested_neff=nested_target_n_effective,
nested_n_like_max=nested_n_like_max,
nested_wt_kwargs=nested_wt_kwargs,
nested_dlogz=nested_dlogz,
nested_nlive_batch=nested_nlive_batch,
nested_maxiter=nested_maxiter, nested_maxcall=nested_maxcall,
nested_maxiter_init=nested_maxiter_init, nested_maxcall_init=nested_maxcall_init,
nested_maxiter_batch=nested_maxiter_batch, nested_maxcall_batch=nested_maxcall_batch,
nested_maxbatch=nested_maxbatch,
nested_filepath=nested_filepath,
nested_resume=nested_resume,
**kwargs)
info, result_obj = output

Expand Down
50 changes: 45 additions & 5 deletions prospect/fitting/nested.py
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Open in desktop
Original file line number Diff line number Diff line change
@@ -1,6 +1,6 @@
import inspect
import numpy as np
import time
import time, sys
import warnings

__all__ = ["run_nested_sampler"]
Expand All @@ -11,6 +11,19 @@ def run_nested_sampler(model,
nested_sampler="dynesty",
nested_nlive=1000,
nested_neff=1000,
nested_n_like_max=sys.maxsize,
nested_dlogz=0.01,
nested_nlive_batch=200,
nested_maxiter=sys.maxsize,
nested_maxcall=sys.maxsize,
nested_maxiter_init=sys.maxsize,
nested_maxcall_init=sys.maxsize,
nested_maxiter_batch=sys.maxsize,
nested_maxcall_batch=sys.maxsize,
nested_maxbatch=sys.maxsize,
nested_wt_kwargs={'pfrac': 1.0},
nested_filepath=None,
nested_resume=True,
verbose=False,
**kwargs):
"""We give a model -- parameter discription and prior transform -- and a
Expand All @@ -26,6 +39,24 @@ def run_nested_sampler(model,
Number of live points.
nested_neff : float
Minimum effective sample size.

nautilus-specific parameters
----------------------------
nested_n_like_max : int
Maximum number of likelihood evaluations for nautilus.
nested_filepath : str or None
Path to the file where results are saved. Must have a `.h5` or `.hdf5` extension. If None, no results are written. Default is None.
nested_resume : bool
If True, resume from previous run if filepath exists. If False, start from scratch and overwrite any previous file. Default is True.

dynesty-specific parameters
----------------------------
nested_maxcall : int
Maximum number of likelihood evaluations for dynesty.
nested_maxiter : int
Maximum number of iterations for dynesty.

----------------------------
verbose : bool
Whether to output sampler progress.

Expand All @@ -48,7 +79,7 @@ def run_nested_sampler(model,
sampler_init = Sampler
init_args = (model.prior_transform, likelihood_function)
init_kwargs = dict(pass_dict=False, n_live=nested_nlive,
n_dim=model.ndim)
n_dim=model.ndim, filepath=nested_filepath, resume=nested_resume)
elif nested_sampler == 'ultranest':
from ultranest import ReactiveNestedSampler
sampler_init = ReactiveNestedSampler
Expand All @@ -59,7 +90,10 @@ def run_nested_sampler(model,
from dynesty import DynamicNestedSampler
sampler_init = DynamicNestedSampler
init_args = (likelihood_function, model.prior_transform, model.ndim)
init_kwargs = dict(nlive=nested_nlive)
init_kwargs = dict(nlive=nested_nlive,
dlogz=nested_dlogz,
maxcall=nested_maxcall_init,
maxiter=nested_maxiter_init, )
elif nested_sampler == 'nestle':
import nestle
init_kwargs = dict()
Expand All @@ -80,7 +114,7 @@ def run_nested_sampler(model,
if nested_sampler == 'nautilus':
sampler_run = sampler.run
run_args = ()
run_kwargs = dict(n_eff=nested_neff, verbose=verbose)
run_kwargs = dict(n_eff=nested_neff, n_like_max=nested_n_like_max, verbose=verbose)
elif nested_sampler == 'ultranest':
sampler_run = sampler.run
run_args = ()
Expand All @@ -90,7 +124,13 @@ def run_nested_sampler(model,
elif nested_sampler == 'dynesty':
sampler_run = sampler.run_nested
run_args = ()
run_kwargs = dict(n_effective=nested_neff, print_progress=verbose)
run_kwargs = dict(n_effective=nested_neff,
wt_kwargs=nested_wt_kwargs,
nlive_batch=nested_nlive_batch,
maxiter=nested_maxiter, maxcall=nested_maxcall,
maxiter_batch=nested_maxiter_batch,
maxcall_batch=nested_maxcall_batch, maxbatch=nested_maxbatch,
print_progress=verbose)
elif nested_sampler == 'nestle':
sampler_run = nestle.sample
run_args = (likelihood_function, model.prior_transform, model.ndim)
Expand Down