def step(self, point):
for name, shared_var in self.shared.items():
shared_var.set_value(point[name])
q = DictToArrayBijection.map(
{v.name: point[v.name]
for v in self.vars})
step_res = self.astep(q)
if self.generates_stats:
apoint, stats = step_res
else:
apoint = step_res
if not isinstance(apoint, RaveledVars):
# We assume that the mapping has stayed the same
apoint = RaveledVars(apoint, q.point_map_info)
new_point = DictToArrayBijection.rmap(apoint, start_point=point)
if self.generates_stats:
return new_point, stats
return new_point
def step(self, point: PointType):
partial_funcs_and_point = [
DictToArrayBijection.mapf(x, start_point=point) for x in self.fs
]
if self.allvars:
partial_funcs_and_point.append(point)
apoint = DictToArrayBijection.map(
{v.name: point[v.name]
for v in self.vars})
step_res = self.astep(apoint, *partial_funcs_and_point)
if self.generates_stats:
apoint_new, stats = step_res
else:
apoint_new = step_res
if not isinstance(apoint_new, RaveledVars):
# We assume that the mapping has stayed the same
apoint_new = RaveledVars(apoint_new, apoint.point_map_info)
point_new = DictToArrayBijection.rmap(apoint_new, start_point=point)
if self.generates_stats:
return point_new, stats
return point_new
def astep(self, q0):
"""Perform a single HMC iteration."""
perf_start = time.perf_counter()
process_start = time.process_time()
p0 = self.potential.random()
p0 = RaveledVars(p0, q0.point_map_info)
start = self.integrator.compute_state(q0, p0)
if not np.isfinite(start.energy):
model = self._model
check_test_point = model.point_logps()
error_logp = check_test_point.loc[
(np.abs(check_test_point) >= 1e20) | np.isnan(check_test_point)
]
self.potential.raise_ok(q0.point_map_info)
message_energy = (
"Bad initial energy, check any log probabilities that "
"are inf or -inf, nan or very small:\n{}".format(error_logp.to_string())
)
warning = SamplerWarning(
WarningType.BAD_ENERGY,
message_energy,
"critical",
self.iter_count,
)
self._warnings.append(warning)
raise SamplingError("Bad initial energy")
adapt_step = self.tune and self.adapt_step_size
step_size = self.step_adapt.current(adapt_step)
self.step_size = step_size
if self._step_rand is not None:
step_size = self._step_rand(step_size)
hmc_step = self._hamiltonian_step(start, p0.data, step_size)
perf_end = time.perf_counter()
process_end = time.process_time()
self.step_adapt.update(hmc_step.accept_stat, adapt_step)
self.potential.update(hmc_step.end.q, hmc_step.end.q_grad, self.tune)
if hmc_step.divergence_info:
info = hmc_step.divergence_info
point = None
point_dest = None
info_store = None
if self.tune:
kind = WarningType.TUNING_DIVERGENCE
else:
kind = WarningType.DIVERGENCE
self._num_divs_sample += 1
# We don't want to fill up all memory with divergence info
if self._num_divs_sample < 100 and info.state is not None:
point = DictToArrayBijection.rmap(info.state.q)
if self._num_divs_sample < 100 and info.state_div is not None:
point = DictToArrayBijection.rmap(info.state_div.q)
if self._num_divs_sample < 100:
info_store = info
warning = SamplerWarning(
kind,
info.message,
"debug",
self.iter_count,
info.exec_info,
divergence_point_source=point,
divergence_point_dest=point_dest,
divergence_info=info_store,
)
self._warnings.append(warning)
self.iter_count += 1
if not self.tune:
self._samples_after_tune += 1
stats = {
"tune": self.tune,
"diverging": bool(hmc_step.divergence_info),
"perf_counter_diff": perf_end - perf_start,
"process_time_diff": process_end - process_start,
"perf_counter_start": perf_start,
}
stats.update(hmc_step.stats)
stats.update(self.step_adapt.stats())
return hmc_step.end.q, [stats]
def __call__(self, q0: RaveledVars) -> RaveledVars:
"""Returns proposed sample given the current sample
in dictionary form (q0_dict)."""
# Logging is reduced to avoid extensive console output
# during multiple recursive calls of subsample()
_log = logging.getLogger("pymc")
_log.setLevel(logging.ERROR)
# Convert current sample from RaveledVars ->
# dict before feeding to subsample.
q0_dict = DictToArrayBijection.rmap(q0)
with self.model_below:
# Check if the tuning flag has been set to False
# in which case tuning is stopped. The flag is set
# to False (by MLDA's astep) when the burn-in
# iterations of the highest-level MLDA sampler run out.
# The change propagates to all levels.
if self.tune:
# Subsample in tuning mode
trace = subsample(
draws=0,
step=self.step_method_below,
start=q0_dict,
tune=self.subsampling_rate,
)
else:
# Subsample in normal mode without tuning
# If DEMetropolisZMLDA is the base sampler a flag is raised to
# make sure that history is edited after tuning ends
if self.tuning_end_trigger:
if isinstance(self.step_method_below, DEMetropolisZMLDA):
self.step_method_below.tuning_end_trigger = True
self.tuning_end_trigger = False
trace = subsample(
draws=self.subsampling_rate,
step=self.step_method_below,
start=q0_dict,
tune=0,
)
# set logging back to normal
_log.setLevel(logging.NOTSET)
# return sample with index self.subchain_selection from the generated
# sequence of length self.subsampling_rate. The index is set within
# MLDA's astep() function
q_dict = trace.point(self.subchain_selection)
# Make sure output dict is ordered the same way as the input dict.
q_dict = Point(
{key: q_dict[key]
for key in q0_dict.keys()},
model=self.model_below,
filter_model_vars=True,
)
return DictToArrayBijection.map(q_dict)
def find_MAP(start=None,
vars=None,
method="L-BFGS-B",
return_raw=False,
include_transformed=True,
progressbar=True,
maxeval=5000,
model=None,
*args,
seed: Optional[int] = None,
**kwargs):
"""Finds the local maximum a posteriori point given a model.
`find_MAP` should not be used to initialize the NUTS sampler. Simply call
``pymc.sample()`` and it will automatically initialize NUTS in a better
way.
Parameters
----------
start: `dict` of parameter values (Defaults to `model.initial_point`)
vars: list
List of variables to optimize and set to optimum (Defaults to all continuous).
method: string or callable
Optimization algorithm (Defaults to 'L-BFGS-B' unless
discrete variables are specified in `vars`, then
`Powell` which will perform better). For instructions on use of a callable,
refer to SciPy's documentation of `optimize.minimize`.
return_raw: bool
Whether to return the full output of scipy.optimize.minimize (Defaults to `False`)
include_transformed: bool, optional defaults to True
Flag for reporting automatically transformed variables in addition
to original variables.
progressbar: bool, optional defaults to True
Whether or not to display a progress bar in the command line.
maxeval: int, optional, defaults to 5000
The maximum number of times the posterior distribution is evaluated.
model: Model (optional if in `with` context)
*args, **kwargs
Extra args passed to scipy.optimize.minimize
Notes
-----
Older code examples used `find_MAP` to initialize the NUTS sampler,
but this is not an effective way of choosing starting values for sampling.
As a result, we have greatly enhanced the initialization of NUTS and
wrapped it inside ``pymc.sample()`` and you should thus avoid this method.
"""
model = modelcontext(model)
if vars is None:
vars = model.cont_vars
if not vars:
raise ValueError("Model has no unobserved continuous variables.")
vars = inputvars(vars)
disc_vars = list(typefilter(vars, discrete_types))
allinmodel(vars, model)
ipfn = make_initial_point_fn(
model=model,
jitter_rvs={},
return_transformed=True,
overrides=start,
)
if seed is None:
seed = model.rng_seeder.randint(2**30, dtype=np.int64)
start = ipfn(seed)
model.check_start_vals(start)
x0 = DictToArrayBijection.map(start)
# TODO: If the mapping is fixed, we can simply create graphs for the
# mapping and avoid all this bijection overhead
def logp_func(x):
return DictToArrayBijection.mapf(model.fastlogp_nojac)(RaveledVars(
x, x0.point_map_info))
try:
# This might be needed for calls to `dlogp_func`
# start_map_info = tuple((v.name, v.shape, v.dtype) for v in vars)
def dlogp_func(x):
return DictToArrayBijection.mapf(model.fastdlogp_nojac(vars))(
RaveledVars(x, x0.point_map_info))
compute_gradient = True
except (AttributeError, NotImplementedError, tg.NullTypeGradError):
compute_gradient = False
if disc_vars or not compute_gradient:
pm._log.warning(
"Warning: gradient not available." +
"(E.g. vars contains discrete variables). MAP " +
"estimates may not be accurate for the default " +
"parameters. Defaulting to non-gradient minimization " +
"'Powell'.")
method = "Powell"
if compute_gradient:
cost_func = CostFuncWrapper(maxeval, progressbar, logp_func,
dlogp_func)
else:
cost_func = CostFuncWrapper(maxeval, progressbar, logp_func)
try:
opt_result = minimize(cost_func,
x0.data,
method=method,
jac=compute_gradient,
*args,
**kwargs)
mx0 = opt_result["x"] # r -> opt_result
except (KeyboardInterrupt, StopIteration) as e:
mx0, opt_result = cost_func.previous_x, None
if isinstance(e, StopIteration):
pm._log.info(e)
finally:
last_v = cost_func.n_eval
if progressbar:
assert isinstance(cost_func.progress, ProgressBar)
cost_func.progress.total = last_v
cost_func.progress.update(last_v)
print(file=sys.stdout)
mx0 = RaveledVars(mx0, x0.point_map_info)
vars = get_default_varnames(model.unobserved_value_vars,
include_transformed)
mx = {
var.name: value
for var, value in zip(
vars,
model.fastfn(vars)(DictToArrayBijection.rmap(mx0)))
}
if return_raw:
return mx, opt_result
else:
return mx
