"""
MCMC engine builder
The purpose of the engine builder is to provide a simple API to gradually assemble
the components needed by the MCMC engine. The builder is responsible of returning an
engine in a well-defined state. Furthermore, the builder can return different engine
implementations.
"""
import math
from collections.abc import Iterable
from typing import cast
import jax
import jax.numpy as jnp
from liesel.option import Option
from .engine import Engine
from .epoch import EpochConfig, EpochManager
from .kernel_sequence import KernelSequence
from .pytree import stack_leaves
from .types import Kernel, KeyArray, ModelInterface, ModelState, QuantityGenerator
from .warmup import stan_epochs
def _find_duplicate(xs: list[str]) -> Option[str]:
"""Checks if a list of strings contains any duplicates."""
set_ = set()
for x in xs:
if x in set_:
return Option(x)
else:
set_.add(x)
return Option(None)
[docs]class EngineBuilder:
"""
The :class:`.EngineBuilder` is used to construct an MCMC Engine.
Currently, the :class:`.EngineBuilder` builds an object of the class
:class:`.Engine`.
By default, every position key associated with an MCMC kernel is tracked.
This behavior can be adjusted with the fields :attr:`.positions_included`
and :attr:`.positions_excluded`.
Parameters
----------
seed
Used to initialize the PRNG for the building process. The PRNG state
num_chains
The number of chains to be used.
"""
def __init__(self, seed: int, num_chains: int):
keys = jax.random.split(jax.random.PRNGKey(seed))
self._prng_key: KeyArray = keys[0]
self._engine_key: KeyArray = keys[1]
self._num_chains: int = num_chains
self._kernels: list[Kernel] = []
self._quantity_generators: list[QuantityGenerator] = []
self._model_state: Option[ModelState] = Option(None)
self._model: Option[ModelInterface] = Option(None)
# public fields, only simple states
self.store_kernel_states: bool = False
self.minimize_transition_infos: bool = False
self.show_progress: bool = True
self.positions_included: list[str] = []
"""List of additional position keys that should be tracked."""
self.positions_excluded: list[str] = []
"""List of position keys that should not be tracked. Excluded keys override
additional keys."""
[docs] def set_engine_seed(self, seed: int | KeyArray):
"""Sets a seed used to initialize the MCMC engine."""
if jnp.isscalar(seed):
seed_int: int = cast(int, seed)
self._engine_key = jax.random.PRNGKey(seed_int)
else:
seed_keyarray = cast(KeyArray, seed)
self._engine_key = seed_keyarray
@property
def engine_seed(self) -> KeyArray:
"""The seed for the engine's pseudo-random number generation."""
return self._engine_key
[docs] def add_kernel(self, kernel: Kernel):
"""Adds a :class:`.Kernel`."""
self._kernels.append(kernel)
@property
def kernels(self) -> tuple[Kernel, ...]:
"""Tuple of all Kernels that are present in the builder."""
return tuple(self._kernels)
[docs] def add_quantity_generator(self, generator: QuantityGenerator):
"""Adds a :class:`.QuantityGenerator`."""
self._quantity_generators.append(generator)
@property
def quantity_generators(self) -> tuple[QuantityGenerator, ...]:
"""Tuple of all quantity generators present in the builder."""
return tuple(self._quantity_generators)
[docs] def set_initial_values(self, model_state: ModelState, multiple_chains=False):
"""
Sets the initial model state.
If :attr:`.multiple_chains` is true the :attr:`.model_state` will be used as is;
otherwise :attr:`.model_state` will be used as the initial values for each
chain. Note that if :attr:`.multiple_chains` is true, the first axis of each
leaf of :attr:`.model_state` refers to the chain.
"""
if not multiple_chains:
model_states = stack_leaves(model_state for _ in range(self._num_chains))
self._model_state = Option(model_states)
@property
def model_state(self) -> Option[ModelState]:
"""Model state."""
return self._model_state
[docs] def set_epochs(self, epochs: Iterable[EpochConfig]):
"""Sets epochs."""
self._epochs = EpochManager(epochs)
[docs] def set_duration(
self,
warmup_duration: int,
posterior_duration: int,
term_duration: int = 50,
thinning_posterior: int = 1,
thinning_warmup: int = 1,
):
"""
Sets epochs using the :func:`.stan_epochs` function.
Note that :attr:`.term_duration` needs to be long enough that tuning algorithms
like dual averaging can converge.
"""
epochs = stan_epochs(
warmup_duration,
posterior_duration,
term_duration=term_duration,
thinning_posterior=thinning_posterior,
thinning_warmup=thinning_warmup,
)
self._epochs = EpochManager(epochs)
@property
def epochs(self) -> tuple[EpochConfig, ...]:
"""Tuple of epoch configurations."""
return tuple(self._epochs._configs)
[docs] def set_model(self, model: ModelInterface):
"""Sets the model interface for all kernels and quantity generators."""
self._model = Option(model)
[docs] def build(self) -> Engine:
"""Builds the MCMC engine with the provided setup."""
# build list of position keys
pos_keys: list[str] = []
for ker in self._kernels:
pos_keys.extend(ker.position_keys)
dupl = _find_duplicate(pos_keys)
if dupl.is_some():
raise RuntimeError(
f"The position key {dupl.unwrap()} is claimed by multiple kernels"
)
pos_keys.extend(self.positions_included)
pos_keys = [key for key in pos_keys if key not in self.positions_excluded]
# find good jittable number
epochs = self._epochs._configs # FIXME: use of private field
durations = [e.duration for e in epochs[1:]]
jit_duration = math.gcd(*durations)
# seeds
seeds = self._engine_key
if seeds.shape == (2,):
# no multi-chain key
seeds = jax.random.split(seeds, self._num_chains)
if seeds.shape != (self._num_chains, 2):
raise RuntimeError(
f"MCMC seed has the wrong dimensions {seeds}. "
f"Expected is {(self._num_chains, 2)}"
)
# check for duplicated identifiers in self._quantity_generators
idents = []
for qg in self._quantity_generators:
idents.append(qg.identifier)
dupl = _find_duplicate(idents)
if dupl.is_some():
raise RuntimeError(
f"The identifier {dupl.unwrap()} is used by multiple "
"quantity generators"
)
# set model interface for all kernels and quantity generators
model = self._model.expect("Model interface must be set")
for ker in self.kernels:
if not ker.has_model():
ker.set_model(model)
for qg in self.quantity_generators:
if not qg.has_model():
qg.set_model(model)
# assign identifiers to kernels
for idx, ker in enumerate(self.kernels):
if not ker.identifier:
ker.identifier = f"kernel_{idx:02d}"
return Engine(
seeds=seeds,
model_states=self._model_state.expect("Model state must be set"),
kernel_sequence=KernelSequence(self.kernels),
epoch_configs=epochs,
jitted_sample_duration=jit_duration,
model=model,
position_keys=pos_keys,
minimize_transition_infos=self.minimize_transition_infos,
store_kernel_states=self.store_kernel_states,
quantity_generators=self.quantity_generators,
show_progress=self.show_progress,
)
