def sample(
self,
initial_state,
iterations=1,
thin_by=1,
progress=False,
mapper=None,
**kwds
):
"""
Runs the PTSampler for a given number of iterations.
Parameters
----------
initial_state: emcee.state.State
Holds the coordinates of the initial state
iterations: int
Number of steps to save into the chain
thin_by: int
The saved steps are separated by this many discarded steps.
progress: bool
Display a progress bar.
mapper: map-like callable
For parallelisation
kwds: dict
Unknown keywords
Yields
-------
state: emcee.state.State
The coordinates of the current state
"""
if isinstance(initial_state, State):
init_x = initial_state.coords
rstate0 = initial_state.random_state
else:
init_x = initial_state
rstate0 = np.random.RandomState().get_state()
if self._ptchain is None:
self._ptchain = self.sampler.chain(init_x)
else:
self._ptchain.ensemble.x = init_x
# set random state of stateful chain
self.random_state = rstate0
self._ptchain.thin_by = thin_by
if mapper is not None:
self._ptchain.ensemble._mapper = mapper
try:
with get_progress_bar(progress, iterations * thin_by) as pbar:
for e in self._ptchain.iterate(iterations):
self._state = State(e.x, log_prob=e.logl + e.logP)
yield self._state
pbar.update(thin_by)
finally:
self._ptchain.ensemble._mapper = map
def fit(self, method="L-BFGS-B", target="nll", verbose=True, **kws):
"""
Obtain the maximum log-likelihood, or log-posterior, estimate (mode)
of the objective. Maximising the log-likelihood is equivalent to
minimising chi2 in a least squares fit.
Parameters
----------
method : str
which method to use for the optimisation. One of:
- `'least_squares'`: :func:`scipy.optimize.least_squares`.
- `'L-BFGS-B'`: L-BFGS-B.
- `'differential_evolution'`:
:func:`scipy.optimize.differential_evolution`
- `'dual_annealing'`:
:func:`scipy.optimize.dual_annealing` (SciPy >= 1.2.0)
- `'shgo'`: :func:`scipy.optimize.shgo` (SciPy >= 1.2.0)
You can also choose many of the minimizers from
:func:`scipy.optimize.minimize`.
target : {'nll', 'nlpost'}, optional
Minimize the negative log-likelihood (`'nll'`) or the negative
log-posterior (`'nlpost'`). This is equivalent to maximising the
likelihood or posterior probabilities respectively.
Maximising the likelihood is equivalent to minimising chi^2 in a
least-squares fit.
This option only applies to the `differential_evolution`, `shgo`,
`dual_annealing` or `L-BFGS-B` methods.
These optimisers require lower and upper (box) bounds for each
parameter. If the `Bounds` on a parameter are not an `Interval`,
but a `PDF` specifying a statistical distribution, then the lower
and upper bounds are approximated as
``PDF.rv.ppf([0.005, 0.995])``, covering 99 % of the statistical
distribution.
verbose : bool, optional
Gives fitting progress. To see a progress bar tqdm has to be
installed.
kws : dict
Additional arguments are passed to the underlying minimization
method.
Returns
-------
result, covar : :class:`scipy.optimize.OptimizeResult`, np.ndarray
`result.x` contains the best fit parameters
`result.covar` is the covariance matrix for the fit.
`result.stderr` is the uncertainties on each of the fit parameters.
Notes
-----
If the `objective` supplies a `residuals` method then `least_squares`
can be used. Otherwise the `nll` method of the `objective` is
minimised. Use this method just before a sampling run.
If `self.objective.parameters` is a `Parameters` instance, then each
of the varying parameters has its value updated by the fit, and each
`Parameter` has a `stderr` attribute which represents the uncertainty
on the fit parameter.
The use of `dual annealing` and `shgo` requires that `scipy >= 1.2.0`
be installed.
"""
_varying_parameters = self.objective.varying_parameters()
init_pars = np.array(_varying_parameters)
_min_kws = {}
_min_kws.update(kws)
_bounds = bounds_list(self.objective.varying_parameters())
_min_kws["bounds"] = _bounds
# setup callback default
_min_kws.setdefault("callback", None)
cost = self.objective.nll
if target == "nlpost":
cost = self.objective.nlpost
# a decorator for the progress bar updater
def _callback_wrapper(callback_func, pbar):
def callback(*args, **kwds):
pbar.update(1)
if callback_func is None:
return None
else:
return callback_func(*args, **kwds)
return callback
# least_squares Trust Region Reflective by default
if method == "least_squares":
b = np.array(_bounds)
_min_kws["bounds"] = (b[..., 0], b[..., 1])
# least_squares doesn't have a callback
_min_kws.pop("callback", None)
res = least_squares(self.objective.residuals, init_pars,
**_min_kws)
# differential_evolution, dual_annealing, shgo require lower and upper
# bounds
elif method in ["differential_evolution", "dual_annealing", "shgo"]:
mini = getattr(sciopt, method)
with get_progress_bar(verbose, None) as pbar:
_min_kws["callback"] = _callback_wrapper(
_min_kws["callback"], pbar)
res = mini(cost, **_min_kws)
else:
# otherwise stick it to minimizer. Default being L-BFGS-B
_min_kws["method"] = method
_min_kws["bounds"] = _bounds
with get_progress_bar(verbose, None) as pbar:
_min_kws["callback"] = _callback_wrapper(
_min_kws["callback"], pbar)
res = minimize(cost, init_pars, **_min_kws)
# OptimizeResult.success may not be present (dual annealing)
if hasattr(res, "success") and res.success:
self.objective.setp(res.x)
# Covariance matrix estimation
covar = self.objective.covar()
errors = np.sqrt(np.diag(covar))
res["covar"] = covar
res["stderr"] = errors
# check if the parameters are all Parameter instances.
flat_params = list(f_unique(flatten(self.objective.parameters)))
if np.all([is_parameter(param) for param in flat_params]):
# zero out all the old parameter stderrs
for param in flat_params:
param.stderr = None
param.chain = None
for i, param in enumerate(_varying_parameters):
param.stderr = errors[i]
# need to touch up the output to check we leave
# parameters as we found them
self.objective.setp(res.x)
return res