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 dlogp_func(x):
return DictToArrayBijection.mapf(model.fastdlogp_nojac(vars))(
RaveledVars(x, x0.point_map_info))
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.")
else:
vars = [model.rvs_to_values.get(var, var) for var in vars]
vars = inputvars(vars)
disc_vars = list(typefilter(vars, discrete_types))
allinmodel(vars, model)
ipfn = make_initial_point_fn(
model=model,
jitter_rvs=set(),
return_transformed=True,
overrides=start,
)
start = ipfn(seed)
model.check_start_vals(start)
var_names = {var.name for var in vars}
x0 = DictToArrayBijection.map(
{var_name: value for var_name, value in start.items() if var_name in var_names}
)
# TODO: If the mapping is fixed, we can simply create graphs for the
# mapping and avoid all this bijection overhead
compiled_logp_func = DictToArrayBijection.mapf(model.compile_logp(jacobian=False), start)
logp_func = lambda x: compiled_logp_func(RaveledVars(x, x0.point_map_info))
rvs = [model.values_to_rvs[value] for value in vars]
try:
# This might be needed for calls to `dlogp_func`
# start_map_info = tuple((v.name, v.shape, v.dtype) for v in vars)
compiled_dlogp_func = DictToArrayBijection.mapf(
model.compile_dlogp(rvs, jacobian=False), start
)
dlogp_func = lambda x: compiled_dlogp_func(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)
unobserved_vars = get_default_varnames(model.unobserved_value_vars, include_transformed)
unobserved_vars_values = model.compile_fn(unobserved_vars)(
DictToArrayBijection.rmap(mx0, start)
)
mx = {var.name: value for var, value in zip(unobserved_vars, unobserved_vars_values)}
if return_raw:
return mx, opt_result
else:
return mx
