# Copyright 2020 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 7, 2011
@author: johnsalvatier
"""
from abc import ABC, abstractmethod
from enum import IntEnum, unique
from typing import Any, Dict, List, Mapping, Sequence, Tuple, Union
import numpy as np
from pytensor.graph.basic import Variable
from pymc.blocking import PointType, StatsDict, StatsType
from pymc.model import modelcontext
__all__ = ("Competence", "CompoundStep")
@unique
class Competence(IntEnum):
"""Enum for characterizing competence classes of step methods.
Values include:
0: INCOMPATIBLE
1: COMPATIBLE
2: PREFERRED
3: IDEAL
"""
INCOMPATIBLE = 0
COMPATIBLE = 1
PREFERRED = 2
IDEAL = 3
class BlockedStep(ABC):
stats_dtypes: List[Dict[str, type]] = []
vars: List[Variable] = []
def __new__(cls, *args, **kwargs):
blocked = kwargs.get("blocked")
if blocked is None:
# Try to look up default value from class
blocked = getattr(cls, "default_blocked", True)
kwargs["blocked"] = blocked
model = modelcontext(kwargs.get("model"))
kwargs.update({"model": model})
# vars can either be first arg or a kwarg
if "vars" not in kwargs and len(args) >= 1:
vars = args[0]
args = args[1:]
elif "vars" in kwargs:
vars = kwargs.pop("vars")
else: # Assume all model variables
vars = model.value_vars
if not isinstance(vars, (tuple, list)):
vars = [vars]
if len(vars) == 0:
raise ValueError("No free random variables to sample.")
if not blocked and len(vars) > 1:
# In this case we create a separate sampler for each var
# and append them to a CompoundStep
steps = []
for var in vars:
step = super().__new__(cls)
# If we don't return the instance we have to manually
# call __init__
step.__init__([var], *args, **kwargs)
# Hack for creating the class correctly when unpickling.
step.__newargs = ([var],) + args, kwargs
steps.append(step)
return CompoundStep(steps)
else:
step = super().__new__(cls)
# Hack for creating the class correctly when unpickling.
step.__newargs = (vars,) + args, kwargs
return step
# Hack for creating the class correctly when unpickling.
def __getnewargs_ex__(self):
return self.__newargs
@abstractmethod
def step(self, point: PointType) -> Tuple[PointType, StatsType]:
"""Perform a single step of the sampler."""
@staticmethod
def competence(var, has_grad):
return Competence.INCOMPATIBLE
@classmethod
def _competence(cls, vars, have_grad):
vars = np.atleast_1d(vars)
have_grad = np.atleast_1d(have_grad)
competences = []
for var, has_grad in zip(vars, have_grad):
try:
competences.append(cls.competence(var, has_grad))
except TypeError:
competences.append(cls.competence(var))
return competences
def stop_tuning(self):
if hasattr(self, "tune"):
self.tune = False
[docs]class CompoundStep:
"""Step method composed of a list of several other step
methods applied in sequence."""
[docs] def __init__(self, methods):
self.methods = list(methods)
self.stats_dtypes = []
for method in self.methods:
self.stats_dtypes.extend(method.stats_dtypes)
self.name = (
f"Compound[{', '.join(getattr(m, 'name', 'UNNAMED_STEP') for m in self.methods)}]"
)
self.tune = True
[docs] def step(self, point) -> Tuple[PointType, StatsType]:
stats = []
for method in self.methods:
point, sts = method.step(point)
stats.extend(sts)
# Model logp can only be the logp of the _last_ stats,
# if there is one. Pop all others.
for sts in stats[:-1]:
sts.pop("model_logp", None)
return point, stats
[docs] def stop_tuning(self):
for method in self.methods:
method.stop_tuning()
[docs] def reset_tuning(self):
for method in self.methods:
if hasattr(method, "reset_tuning"):
method.reset_tuning()
@property
def vars(self):
return [var for method in self.methods for var in method.vars]
def flatten_steps(step: Union[BlockedStep, CompoundStep]) -> List[BlockedStep]:
"""Flatten a hierarchy of step methods to a list."""
if isinstance(step, BlockedStep):
return [step]
steps = []
if not isinstance(step, CompoundStep):
raise ValueError(f"Unexpected type of step method: {step}")
for sm in step.methods:
steps += flatten_steps(sm)
return steps
class StatsBijection:
"""Map between a `list` of stats to `dict` of stats."""
def __init__(self, sampler_stats_dtypes: Sequence[Mapping[str, type]]) -> None:
# Keep a list of flat vs. original stat names
self._stat_groups: List[List[Tuple[str, str]]] = [
[(f"sampler_{s}__{statname}", statname) for statname, _ in names_dtypes.items()]
for s, names_dtypes in enumerate(sampler_stats_dtypes)
]
def map(self, stats_list: Sequence[Mapping[str, Any]]) -> StatsDict:
"""Combine stats dicts of multiple samplers into one dict."""
stats_dict = {}
for s, sts in enumerate(stats_list):
for statname, sval in sts.items():
sname = f"sampler_{s}__{statname}"
stats_dict[sname] = sval
return stats_dict
def rmap(self, stats_dict: Mapping[str, Any]) -> StatsType:
"""Split a global stats dict into a list of sampler-wise stats dicts."""
stats_list = []
for namemap in self._stat_groups:
d = {statname: stats_dict[sname] for sname, statname in namemap}
stats_list.append(d)
return stats_list