# Copyright 2024 - present The PyMC Developers
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
Created on Mar 12, 2011.
@author: johnsalvatier
"""
import warnings
from collections.abc import Sequence
import numpy as np
import pytensor.gradient as tg
from numpy import isfinite
from pytensor.graph.basic import Variable
from pytensor.utils import lazy_scipy_module
from rich.console import Console
from rich.progress import Progress, TextColumn
import pymc as pm
from pymc.blocking import DictToArrayBijection, RaveledVars
from pymc.initial_point import make_initial_point_fn
from pymc.model import modelcontext
from pymc.progress_bar import CustomProgress, default_progress_theme
from pymc.pytensorf import floatX, inputvars
from pymc.util import (
get_default_varnames,
get_value_vars_from_user_vars,
)
from pymc.vartypes import discrete_types, typefilter
optimize = lazy_scipy_module("optimize")
__all__ = ["find_MAP"]
[docs]
def find_MAP(
start=None,
vars: Sequence[Variable] | None = None,
method="L-BFGS-B",
return_raw=False,
include_transformed=True,
progressbar=True,
progressbar_theme=default_progress_theme,
maxeval=5000,
model=None,
*args,
seed: int | None = None,
**kwargs,
):
"""Find 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`)
These values will be fixed and used for any free RandomVariables that are
not being optimized.
vars: list of TensorVariable
List of free RandomVariables to optimize the posterior with respect to.
Defaults to all continuous RVs in a model. The respective value variables
may also be passed instead.
method: string or callable, optional
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, optional defaults to False
Whether to return the full output of scipy.optimize.minimize
include_transformed: bool, optional defaults to True
Flag for reporting automatically unconstrained transformed values in addition
to the constrained values
progressbar: bool, optional defaults to True
Whether to display a progress bar in the command line.
progressbar_theme: Theme, optional
Custom theme for the progress bar.
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.continuous_value_vars
if not vars:
raise ValueError("Model has no unobserved continuous variables.")
else:
try:
vars = get_value_vars_from_user_vars(vars, model)
except ValueError as exc:
# Accommodate case where user passed non-pure RV nodes
vars = inputvars(model.replace_rvs_by_values(vars))
if vars:
warnings.warn(
"Intermediate variables (such as Deterministic or Potential) were passed. "
"find_MAP will optimize the underlying free_RVs instead.",
UserWarning,
)
else:
raise exc
disc_vars = list(typefilter(vars, discrete_types))
ipfn = make_initial_point_fn(
model=model,
jitter_rvs=set(),
return_transformed=True,
overrides=start,
)
start = ipfn(seed)
model.check_start_vals(start)
vars_dict = {var.name: var for var in vars}
x0 = DictToArrayBijection.map(
{var_name: value for var_name, value in start.items() if var_name in vars_dict}
)
# 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)) # noqa: E731
rvs = [model.values_to_rvs[vars_dict[name]] for name, _, _, _ in 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)
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)) # noqa: E731
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 and method != "Powell":
cost_func = CostFuncWrapper(maxeval, progressbar, progressbar_theme, logp_func, dlogp_func)
else:
cost_func = CostFuncWrapper(maxeval, progressbar, progressbar_theme, logp_func)
compute_gradient = False
with cost_func.progress:
try:
opt_result = optimize.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:
cost_func.progress.update(cost_func.task, completed=cost_func.n_eval, refresh=True)
mx0 = RaveledVars(mx0, x0.point_map_info)
unobserved_vars = get_default_varnames(model.unobserved_value_vars, include_transformed)
unobserved_vars_values = model.compile_fn(inputs=model.value_vars, outs=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
def allfinite(x):
return np.all(isfinite(x))
class CostFuncWrapper:
def __init__(
self,
maxeval=5000,
progressbar=True,
progressbar_theme=default_progress_theme,
logp_func=None,
dlogp_func=None,
):
self.n_eval = 0
self.maxeval = maxeval
self.logp_func = logp_func
if dlogp_func is None:
self.use_gradient = False
self.desc = "logp = {:,.5g}"
else:
self.dlogp_func = dlogp_func
self.use_gradient = True
self.desc = "logp = {:,.5g}, ||grad|| = {:,.5g}"
self.previous_x = None
self.progressbar = progressbar
self.progress = CustomProgress(
*Progress.get_default_columns(),
TextColumn("{task.fields[loss]}"),
console=Console(theme=progressbar_theme),
disable=not progressbar,
)
self.task = self.progress.add_task("MAP", total=maxeval, loss="")
def __call__(self, x):
neg_value = np.float64(self.logp_func(floatX(x)))
value = -1.0 * neg_value
if self.use_gradient:
neg_grad = self.dlogp_func(floatX(x))
if np.all(np.isfinite(neg_grad)):
self.previous_x = x
grad = -1.0 * neg_grad
grad = grad.astype(np.float64)
else:
self.previous_x = x
grad = None
if self.n_eval % 10 == 0:
self.progress.update(self.task, loss=self.update_progress_desc(neg_value, grad))
if self.n_eval > self.maxeval:
self.progress.update(self.task, loss=self.update_progress_desc(neg_value, grad))
raise StopIteration
self.n_eval += 1
self.progress.update(self.task, completed=self.n_eval)
if self.use_gradient:
return value, grad
else:
return value
def update_progress_desc(self, neg_value: float, grad: np.float64 = None) -> None:
if self.progressbar:
if grad is None:
return self.desc.format(neg_value)
else:
norm_grad = np.linalg.norm(grad)
return self.desc.format(neg_value, norm_grad)
