"""
Nodes and variables.
"""
from __future__ import annotations
import logging
import weakref
from abc import ABC, abstractmethod
from collections.abc import Callable, Hashable, Iterable, Sequence
from functools import wraps
from itertools import chain
from types import MappingProxyType
from typing import IO, TYPE_CHECKING, Any, Literal, NamedTuple, TypeGuard, TypeVar
import jax
import tensorflow_probability.substrates.jax.bijectors as jb
import tensorflow_probability.substrates.jax.distributions as jd
import tensorflow_probability.substrates.numpy.bijectors as nb
import tensorflow_probability.substrates.numpy.distributions as nd
from ..distributions.nodist import NoDistribution
from .viz import plot_nodes, plot_vars
if TYPE_CHECKING:
from ..goose import MCMCSpec
from .model import Model
type InferenceTypes = None | MCMCSpec | dict[str, MCMCSpec] | Any
__all__ = [
"Array",
"Bijector",
"Calc",
"Value",
"Dist",
"Distribution",
"Group",
"InputGroup",
"Node",
"NodeState",
"TransientCalc",
"TransientDist",
"TransientIdentity",
"TransientNode",
"Var",
]
type Array = Any
type Distribution = jd.Distribution | nd.Distribution
type Bijector = jb.Bijector | nb.Bijector
T = TypeVar("T", bound=Hashable)
logger = logging.getLogger(__name__)
def _unique_tuple(*args: Iterable[T]) -> tuple[T, ...]:
return tuple(dict.fromkeys(chain(*args)))
def in_model_method(fn):
@wraps(fn)
def wrapped(self, *args, **kwargs):
if not self.model:
raise RuntimeError(
f"{repr(self)} is not part of a model, cannot call {fn.__name__}()"
)
return fn(self, *args, **kwargs)
return wrapped
def in_model_getter(fn):
@wraps(fn)
def wrapped(self, *args, **kwargs):
if not self.model:
raise RuntimeError(
f"{repr(self)} is not part of a model, cannot call '{fn.__name__}'"
)
return fn(self, *args, **kwargs)
return wrapped
def no_model_method(fn):
@wraps(fn)
def wrapped(self, *args, **kwargs):
if self.model:
raise RuntimeError(
f"{repr(self)} is part of a model, cannot call {fn.__name__}()"
)
return fn(self, *args, **kwargs)
return wrapped
def no_model_setter(fn):
@wraps(fn)
def wrapped(self, *args, **kwargs):
if self.model:
raise RuntimeError(
f"{repr(self)} is part of a model, cannot set '{fn.__name__}'"
)
return fn(self, *args, **kwargs)
return wrapped
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Nodes ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
[docs]
class NodeState(NamedTuple):
"""The state of a node."""
value: Any
"""The value of the node."""
outdated: bool
"""Whether the node is outdated."""
extra: Any = None
"""Optional extra information."""
[docs]
class Node(ABC):
"""
A node of a computational graph that can cache its value.
Liesel represents statistical models as directed acyclic graphs (DAGs) of
random variables (see :class:`.Var`) and computational nodes. The graph of
random variables is built on top of the computational graph. The nodes of the
computational graph will typically express computations in JAX returning arrays
or pytrees_, but in general, they can represent arbitrary operations in Python.
Nodes can cache the result of the operations they represent, improving
the efficiency of the graph. The cached values are part of the model state
(see :attr:`.Model.state`), and can be stored in a chain by Liesel's MCMC engine
Goose.
.. note::
This class is an abstract class that cannot be initialized without defining the
:meth:`.update` method. See below for the most important concrete node classes.
Parameters
----------
inputs
Non-keyword inputs. Any inputs that are not already nodes or :class:`.Var`
will be converted to :class:`.Value` nodes.
_name
The name of the node. If you do not specify a name, a unique name will be \
automatically generated upon initialization of a :class:`.Model`.
_needs_seed
Whether the node needs a seed / PRNG key.
See Also
--------
.Calc :
A node representing a general calculation/operation
in JAX or Python.
.Value :
A node representing some static data.
.Dist :
A node representing a ``tensorflow_probability``
:class:`~tfp.distributions.Distribution`.
.Var : A variable in a statistical model, typically with a probability
distribution.
.param :
A helper function to initialize a :class:`.Var` as a model parameter.
.obs :
A helper function to initialize a :class:`.Var` as an observed variable.
.. _pytrees: https://jax.readthedocs.io/en/latest/pytrees.html
.. _TensorFlow Probability: https://www.tensorflow.org/probability
"""
def __init__(
self,
*inputs: Any,
_name: str = "",
_needs_seed: bool = False,
**kwinputs: Any,
):
self._groups: dict[str, Group] = {}
self._inputs = tuple(self._to_node(_input) for _input in inputs)
self._kwinputs = {kw: self._to_node(_input) for kw, _input in kwinputs.items()}
self._model: weakref.ref[Model] | Callable[[], None] = lambda: None
self._name = _name
self._needs_seed = _needs_seed
self._outdated = True
self._outputs: tuple[Node, ...] = ()
self._value: Any = None
self._var: Var | None = None
self.monitor = False
"""Whether the node should be monitored by an inference algorithm."""
def _add_output(self, output: Node) -> Node:
self._outputs = _unique_tuple(self._outputs, [output])
return self
def _clear_outputs(self) -> Node:
self._outputs = ()
return self
def _set_model(self, model: Model) -> Node:
if self.model:
raise RuntimeError(f"{repr(self)} can only be part of one model")
self._model = weakref.ref(model)
return self
def _set_var(self, var: Var) -> Node:
if self.var:
raise RuntimeError(f"{repr(self)} can only be part of one var")
self._var = var
return self
@staticmethod
def _to_node(x: Any) -> Node:
if isinstance(x, Var):
return x.var_value_node
if not isinstance(x, Node):
return Value(x)
return x
def _unset_model(self) -> Node:
self._model = lambda: None
return self
def _unset_var(self) -> Node:
self._var = None
return self
[docs]
@in_model_method
def all_output_nodes(self) -> tuple[Node, ...]:
"""Returns all output nodes as a unique tuple."""
return self.outputs
[docs]
def clear_state(self) -> Node:
"""Clears the state of the node."""
self.state = NodeState(None, True)
return self
[docs]
@in_model_method
def flag_outdated(self) -> Node:
"""Flags the node and its recursive outputs as outdated."""
self._outdated = True
for node in self._outputs:
node.flag_outdated()
return self
@property
def groups(self) -> MappingProxyType[str, Group]:
"""The groups that this node is a part of."""
return MappingProxyType(self._groups)
@property
def inputs(self) -> tuple[Node, ...]:
"""The non-keyword input nodes."""
return self._inputs
@property
def kwinputs(self) -> MappingProxyType[str, Node]:
"""The keyword input nodes."""
return MappingProxyType(self._kwinputs)
@property
def model(self) -> Model | None:
"""The model the node is part of."""
return self._model()
@property
def name(self) -> str:
"""The name of the node."""
return self._name
@name.setter
@no_model_setter
def name(self, name: str):
self._name = name
@property
def needs_seed(self) -> bool:
"""Whether the node needs a seed / PRNG key."""
return self._needs_seed
@needs_seed.setter
@no_model_setter
def needs_seed(self, needs_seed: bool):
self._needs_seed = needs_seed
@property
def outdated(self) -> bool:
"""Whether the node is outdated."""
if not self.model:
return True
return self._outdated
@property
@in_model_getter
def outputs(self) -> tuple[Node, ...]:
"""The output nodes."""
return self._outputs
@property
def state(self) -> NodeState:
"""
The state of the node.
For the default node, a :class:`.NodeState` with the value and the
outdated flag, but subclasses can add extra information to the state.
"""
return NodeState(self.value, self.outdated)
@state.setter
def state(self, state: NodeState):
self._value = state.value
self._outdated = state.outdated
[docs]
@abstractmethod
def update(self) -> Node:
"""Updates the value of the node."""
@property
def value(self) -> Any:
"""
The value of the node.
Can only be set for a :class:`.Value` node, but not a :class:`.Calc` or
:class:`.Dist` node. If the node is part of a :class:`.Model` ``m`` with
``m.auto_update == True``, setting the value of the node triggers an update
of the model. The auto-update can be disabled to improve the performance if
multiple model parameters are updated at once.
"""
return self._value
@property
def var(self) -> Var | None:
"""The variable the node is part of."""
return self._var
@property
def _iloc(self) -> tuple[Node | Var, ...]:
input_list: list[Node | Var] = []
for input_ in self.inputs:
if isinstance(input_, VarValue):
# This should not happen in practice, but the check makes mypy happy.
if input_.var is None:
raise RuntimeError(f"{input_}.var is None.")
input_list.append(input_.var)
else:
input_list.append(input_)
return tuple(input_list)
@property
def _loc(self) -> dict[str, Node | Var]:
input_dict: dict[str, Node | Var] = {}
for key, input_ in self.kwinputs.items():
if isinstance(input_, VarValue):
# This should not happen in practice, but the check makes mypy happy.
if input_.var is None:
raise RuntimeError(f"{input_}.var is None.")
input_dict[key] = input_.var
else:
input_dict[key] = input_
return input_dict
def __getitem__(self, key: int | str) -> Node | Var:
if isinstance(key, int):
try:
return self._iloc[key]
except IndexError as error:
available_indices = {
idx: self._iloc[idx] for idx in range(len(self._iloc))
}
available_keywords = str(self._loc).replace("'", '"')
msg = (
f"{key} is out of bounds. Available index-variable pairs:"
f" {available_indices}. Available keyword-variable pairs:"
f" {available_keywords}."
)
raise IndexError(msg) from error
elif isinstance(key, str):
try:
return self._loc[key]
except KeyError as error:
available_indices = {
idx: self._iloc[idx] for idx in range(len(self._iloc))
}
available_keywords = str(self._loc).replace("'", '"')
msg = (
f"{key} not found. Available index-variable pairs:"
f" {available_indices}. Available keyword-variable pairs:"
f" {available_keywords}."
)
raise KeyError(msg) from error
else:
raise ValueError(f"Key must be str or int, not {type(key)}.")
def _iloc_replace(self, key: int, value: Node | Var | Any) -> None:
inputs = list(self.inputs)
inputs[key] = self._to_node(value)
return self.set_inputs(*inputs, **self.kwinputs)
def _loc_replace(self, key: str, value: Node | Var | Any) -> None:
kwinputs = dict(self.kwinputs)
if key not in kwinputs:
raise KeyError(f"'{key}' is not the key of an existing keyword input.")
kwinputs[key] = self._to_node(value)
return self.set_inputs(*self.inputs, **kwinputs)
def __setitem__(self, key: int | str, value: Node | Var | Any) -> None:
if isinstance(key, int):
all_inputs = self.all_input_nodes()
node_to_replace = all_inputs[key]
for kwinputs_key, kwinputs_node in self.kwinputs.items():
if node_to_replace is kwinputs_node:
return self._loc_replace(kwinputs_key, value)
return self._iloc_replace(key, value)
elif isinstance(key, str):
return self._loc_replace(key, value)
else:
raise ValueError(f"Key must be str or int, not {type(key)}.")
def __getstate__(self):
state = self.__dict__.copy()
state["_model"] = self._model()
return state
def __setstate__(self, state):
self.__dict__.update(state)
if self._model is not None:
self._model = weakref.ref(self._model)
else:
self._model = lambda: None
def __repr__(self) -> str:
return f'{type(self).__name__}(name="{self.name}")'
[docs]
class TransientNode(Node):
"""
A node that does not cache its value.
A transient node is outdated if and only if at least one of its input nodes
is outdated. The :attr:`.outdated` property checks this condition on-the-fly.
"""
@property
def outdated(self) -> bool:
"""
Whether the node is outdated.
A transient node is outdated if and only if at least one of its input nodes
is outdated. This condition is checked on-the-fly.
"""
if not self.model:
return True
return any(_input.outdated for _input in self.all_input_nodes())
@property
def state(self) -> NodeState:
"""The state of the node with the value ``None``."""
return NodeState(None, self.outdated)
@state.setter
def state(self, state: NodeState):
self._value = state.value
self._outdated = state.outdated
[docs]
def update(self):
"""Does nothing."""
return self
@property
@abstractmethod
def value(self) -> Any:
"""
The value of the node.
Computed on-the-fly.
"""
class ArgGroup(NamedTuple):
"""A group of arguments as a named tuple of ``args`` and ``kwargs``."""
args: list[Any]
"""The non-keyword arguments."""
kwargs: dict[str, Any]
"""The keyword arguments."""
[docs]
class Value(Node):
r"""
A :class:`.Node` subclass that holds constant values.
Since the information represented by a value node does not change, it is
always up-to-date.
A common usecase for value nodes is to cache computed values.
- By default, value nodes *will* appear in the node graph created by
:func:`.viz.plot_nodes`, but they will *not* appear in the model graph created by
:func:`.viz.plot_vars`.
- You can wrap a value node in a :class:`.Var` to make it appear in the model
graph.
Parameters
----------
value
The value of the node.
_name
The name of the node. If you do not specify a name, a unique name will
be \ automatically generated upon initialization of a :class:`.Model`.
See Also
--------
.Calc :
A node representing a general calculation/operation in JAX or Python.
.Dist :
A node representing a ``tensorflow_probability``
:class:`~tfp.distributions.Distribution`.
.Var : A variable in a statistical model, typically with a probability
distribution.
.param :
A helper function to initialize a :class:`.Var` as a model parameter.
.obs :
A helper function to initialize a :class:`.Var` as an observed variable.
Examples
--------
A simple constant node representing a constant value without a name:
>>> nameless_node = lsl.Value(1.0)
>>> nameless_node
Value(name="")
Adding this node to a model leads to an automatically generated name:
>>> model = lsl.Model([nameless_node])
>>> nameless_node
Value(name="n0")
A constant node with a name:
>>> node = lsl.Value(1.0, _name="my_name")
>>> node
Value(name="my_name")
"""
def __init__(self, value: Any, _name: str = ""):
super().__init__(_name=_name)
self._value = value
[docs]
def flag_outdated(self) -> Value:
"""Stops the recursion setting outdated flags."""
return self
@property
def outdated(self) -> bool:
return False
[docs]
def update(self) -> Value:
"""Does nothing."""
return self
@property
def value(self) -> Any:
return self._value
@value.setter
def value(self, value: Any):
self._value = value
if self.model:
for node in self.outputs:
node.flag_outdated()
if self.model.auto_update:
self.model.update()
[docs]
class Data(Value):
"""
A :class:`.Node` subclass that holds constant data.
This is an alias for :class:`.Value`.
See Also
--------
.Value :
Alias for :class:`.Value`. For full documentation, please consult
:class:`.Value`.
"""
pass
[docs]
class Calc(Node):
"""
A :class:`.Node` subclass that calculates its value based on its inputs nodes.
Calculator nodes are a central element of the Liesel graph building toolkit.
They wrap arbitrary calculations in pure JAX functions.
- By default, calculator nodes *will* appear in the node graph created by
:func:`.viz.plot_nodes`, but they will *not* appear in the model graph created by
:func:`.viz.plot_vars`.
- You can use :meth:`~.Var.new_calc` if you want your calculation to be treated
as a model variable and thus be shown in :func:`.viz.plot_vars`.
.. tip::
The wrapped function must be jit-compilable by JAX. This mainly means that
it must be a pure function, i.e. it must not have any side effects and, given
the same input, it must always return the same output. Some special
consideration is also required for loops and conditionals.
Please consult the JAX docs_ for details.
Parameters
----------
function
The function to be wrapped. Must be jit-compilable by JAX.
*inputs
Non-keyword inputs. Any inputs that are not already nodes or :class:`.Var`
will be converted to :class:`.Value` nodes. The values of these inputs will be
passed to the wrapped function in the same order they are entered here.
_name
The name of the node. If you do not specify a name, a unique name will be \
automatically generated upon initialization of a :class:`.Model`.
_needs_seed
Whether the node needs a seed / PRNG key.
_update_on_init
If ``True``, the calculator will try to evaluate its function upon \
initialization.
**kwinputs
Keyword inputs. Any inputs that are not already nodes or :class:`.Var`s
will be converted to :class:`.Value` nodes. The values of these inputs will be
passed to the wrapped function as keyword arguments.
See Also
--------
.Var.new_calc :
Initializes a weak variable that is a function of other variables.
.Var : A variable in a statistical model, typically with a probability
distribution.
.Var.new_param : Initializes a strong variable that acts as a model parameter.
.Var.new_obs : Initializes a strong variable that holds observed data.
.Var.new_value : Initializes a strong variable without a distribution.
.Value :
A node representing some static data.
.Dist :
A node representing a ``tensorflow_probability``
:class:`~tfp.distributions.Distribution`.
Examples
--------
A simple calculator node, taking the exponential value of an input parameter.
>>> log_scale = lsl.Var.new_param(0.0, name="log_scale")
>>> scale = lsl.Calc(jnp.exp, log_scale)
>>> print(scale.value)
1.0
The value of the calculator node is updated when :meth:`.Calc.update` is called.
>>> scale.update()
Calc(name="")
>>> print(scale.value)
1.0
You can also use your own functions as long as they are jit-compilable by JAX.
>>> def compute_variance(x):
... return jnp.exp(x)**2
>>> log_scale = lsl.Var.new_param(0.0, name="log_scale")
>>> variance = lsl.Calc(compute_variance, log_scale).update()
>>> print(variance.value)
1.0
.. _docs: https://jax.readthedocs.io/en/latest/notebooks/Common_Gotchas_in_JAX.html
"""
def __init__(
self,
function: Callable[..., Any],
*inputs: Any,
_name: str = "",
_needs_seed: bool = False,
_update_on_init: bool = True,
**kwinputs: Any,
):
super().__init__(*inputs, **kwinputs, _name=_name, _needs_seed=_needs_seed)
self._function = function
self._update_on_init = _update_on_init
if self._update_on_init:
try:
self.update()
except Exception as e:
logger.warning(
f"{self} was not updated during initialization, because the"
f" following exception occured: {repr(e)}. See debug log for the"
" full traceback."
)
logger.debug(
f"{self} was not updated during initialization, because the"
" following exception occured:",
exc_info=e,
)
@property
def function(self) -> Callable[..., Any]:
"""The wrapped function."""
return self._function
@function.setter
@no_model_setter
def function(self, function: Callable[..., Any]):
self._function = function
[docs]
def update(self) -> Calc:
args = [_input.value for _input in self.inputs]
kwargs = {kw: _input.value for kw, _input in self.kwinputs.items()}
try:
self._value = self.function(*args, **kwargs)
except Exception as e:
raise RuntimeError(f"Error while updating {self}.") from e
self._outdated = False
return self
[docs]
class TransientCalc(TransientNode, Calc):
"""A transient calculator node that does not cache its value."""
@property
def value(self) -> Any:
args = [_input.value for _input in self.inputs]
kwargs = {kw: _input.value for kw, _input in self.kwinputs.items()}
try:
value = self.function(*args, **kwargs)
except Exception as e:
raise RuntimeError(f"Error while updating {self}.") from e
return value
[docs]
class TransientIdentity(TransientCalc):
"""
A transient identity node that does not cache its value.
Essentially, this node "forwards" the value of its input to its outputs.
"""
def __init__(self, _input: Any, _name: str = ""):
super().__init__(lambda x: x, _input, _name=_name)
class VarValue(TransientIdentity):
"""
A proxy node for the value of a :class:`.Var`.
This node type is used to keep the references to a variable intact,
even if the underlying value node is replaced.
"""
[docs]
class Dist(Node):
"""
A :class:`.Node` subclass that wraps a probability distribution.
Distribution nodes wrap distribution classes that follow the
``tensorflow_probability`` :class:`~tfp.distributions.Distribution` interface.
They can be used to represent observation models and priors.
Distribution nodes *will* appear in the node graph created by
:func:`.viz.plot_nodes`, but they will *not* appear in the model graph created by
:func:`.viz.plot_vars`.
Parameters
----------
distribution
The wrapped distribution class that follows the ``tensorflow_probability``
:class:`~tfp.distributions.Distribution` interface.
*inputs
Non-keyword inputs. Any inputs that are not already nodes or :class:`.Var`
will be converted to :class:`.Value` nodes. The values of these inputs will be
passed to the wrapped distribution in the same order they are entered here.
_name
The name of the node. If you do not specify a name, a unique name will be \
automatically generated upon initialization of a :class:`.Model`.
_needs_seed
Whether the node needs a seed / PRNG key.
**kwinputs
Keyword inputs. Any inputs that are not already nodes or :class:`.Var`s
will be converted to :class:`.Value` nodes. The values of these inputs will be
passed to the wrapped distribution as keyword arguments.
See Also
--------
.Var : A variable in a statistical model, typically with a probability
distribution.
.param :
A helper function to initialize a :class:`.Var` as a model parameter.
.obs :
A helper function to initialize a :class:`.Var` as an observed variable.
.MultivariateNormalDegenerate : A custom distribution class that implements
a degenerate multivariate normal distribution in the ``tensorflow_probability``
:class:`~tfp.distributions.Distribution` interface.
Examples
--------
For the examples below, we import ``tensorflow_probability``:
>>> import tensorflow_probability.substrates.jax.distributions as tfd
Creating an observation model for a normally distributed variable with fixed
mean and scale. The log probability of the node ``y`` in the example below is
``None``, until the variable is updated.
>>> dist = lsl.Dist(tfd.Normal, loc=0.0, scale=1.0)
>>> y = lsl.Var.new_obs(jnp.array([-0.5, 0.0, 0.5]), dist, name="y")
>>> print(y.log_prob)
None
>>> y.update()
Var(name="y")
>>> y.log_prob
Array([-1.0439385, -0.9189385, -1.0439385], dtype=float32)
Now we define the same observation model, but include the location and scale
as parameters:
>>> loc = lsl.Var.new_param(0.0, name="loc")
>>> scale = lsl.Var.new_param(1.0, name="scale")
>>> dist = lsl.Dist(tfd.Normal, loc=loc, scale=scale)
>>> y = lsl.Var.new_obs(jnp.array([-0.5, 0.0, 0.5]), dist, name="y").update()
>>> y.log_prob
Array([-1.0439385, -0.9189385, -1.0439385], dtype=float32)
.. rubric:: Summed-up log-probability
You can set the ``per_obs`` attribute of a distribution node to ``False`` to
sum up the log-probability of the distribution over all observations.
>>> dist.per_obs = False
>>> y.update().log_prob
Array(-3.0068154, dtype=float32)
"""
def __init__(
self,
distribution: Callable[..., Distribution],
*inputs: Any,
_name: str = "",
_needs_seed: bool = False,
**kwinputs: Any,
):
super().__init__(*inputs, **kwinputs, _name=_name, _needs_seed=_needs_seed)
self._at: Node | None = None
self._distribution = distribution
self._per_obs = True
@property
def at(self) -> Node | None:
"""Where to evaluate the distribution."""
return self._at
@at.setter
@no_model_setter
def at(self, at: Node | None):
if self.var and at is not self.var.var_value_node:
raise RuntimeError(
f"{repr(self)} is part of a var, cannot set property `at`"
)
self._at = at
@property
def distribution(self) -> Callable[..., Distribution]:
"""The wrapped distribution."""
return self._distribution
@distribution.setter
@no_model_setter
def distribution(self, distribution: Callable[..., Distribution]):
self._distribution = distribution
[docs]
def init_dist(self) -> Distribution:
"""Initializes the distribution."""
args = [_input.value for _input in self.inputs]
kwargs = {kw: _input.value for kw, _input in self.kwinputs.items()}
dist = self.distribution(*args, **kwargs)
return dist
@property
def log_prob(self) -> Array:
"""The log-probability of the distribution."""
return self.value
@property
def per_obs(self) -> bool:
"""Whether the log-probability is stored per observation or summed up."""
return self._per_obs
@per_obs.setter
@no_model_setter
def per_obs(self, per_obs: bool):
self._per_obs = per_obs
[docs]
def update(self) -> Dist:
if not self.at:
raise RuntimeError(
f"{repr(self)} cannot evaluate log-prob, property `at` not set"
)
log_prob = self.init_dist().log_prob(self.at.value)
if not self.per_obs and hasattr(log_prob, "sum"):
log_prob = log_prob.sum()
self._value = log_prob
self._outdated = False
return self
[docs]
class TransientDist(TransientNode, Dist):
"""A transient distribution node that does not cache its value."""
@property
def value(self) -> Any:
if not self.at:
raise RuntimeError(
f"{repr(self)} cannot evaluate log-prob, property `at` not set"
)
log_prob = self.init_dist().log_prob(self.at.value)
if not self.per_obs and hasattr(log_prob, "sum"):
log_prob = log_prob.sum()
return log_prob
class NoDist(Dist):
def __init__(self):
super().__init__(NoDistribution)
def all_input_nodes(self) -> tuple[Node, ...]:
return ()
def all_output_nodes(self) -> tuple[Node, ...]:
return ()
@property
def outputs(self) -> tuple[Node, ...]:
return ()
def update(self) -> NoDist:
return self
@property
def value(self) -> float:
return 0.0
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Variable ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
def is_bijector_class(obj) -> TypeGuard[type[Any]]:
return isinstance(obj, type) and issubclass(obj, jb.Bijector)
[docs]
class Var:
"""
A variable in a statistical model.
A variable in Liesel is often a random variable, e.g. an observed or
latent variable with a probability distribution (see :meth:`~.Var.new_obs`),
or a model parameter with a prior distribution (see :meth:`~.Var.new_param`).
Other quantities can also be declared as variables, e.g. fixed data like
hyperparameters or design matrices (see :meth:`~.Var.new_value`),
or quantities that are computed from other nodes, e.g. structured additive
predictors in semi-parametric regression models (see :meth:`~.Var.new_calc`).
.. tip::
You should initialize variables through one of the four constructors:
:meth:`.new_param`, :meth:`.new_obs`, :meth:`.new_calc`, and :meth:`.new_value`.
.. rubric:: Accessing inputs
:class:`.Calc` and :class:`.Dist` objects support access to their inputs via
square-bracket syntax. Thus, with a :class:`.Var` object, you can use square bracket
indexing on its attributes :attr:`.Var.value_node` and :attr:`.Var.dist_node`.
You can access both keyword and positional arguments this way.
>>> import tensorflow_probability.substrates.jax.distributions as tfd
Access keyword inputs to a calculator :attr:`.Var.value_node`:
>>> a = lsl.Var.new_value(2.0, name="a")
>>> b = lsl.Var.new_calc(lambda x: x + 1.0, x=a)
>>> b.value_node["x"]
Var(name="a")
Access positional inputs to a calculator :attr:`.Var.value_node`:
>>> a = lsl.Var.new_value(2.0, name="a")
>>> b = lsl.Var.new_calc(lambda x: x + 1.0, a)
>>> b.value_node[0]
Var(name="a")
Access keyword inputs to a distribution :attr:`.Var.dist_node`:
>>> a = lsl.Var.new_value(2.0, name="a")
>>> b = lsl.Var.new_obs(1.0, lsl.Dist(tfd.Normal, loc=a, scale=1.0))
>>> b.dist_node["loc"]
Var(name="a")
Access positional inputs to a distribution :attr:`.Var.dist_node`:
>>> a = lsl.Var.new_value(2.0, name="a")
>>> b = lsl.Var.new_obs(1.0, lsl.Dist(tfd.Normal, a, scale=1.0))
>>> b.dist_node[0]
Var(name="a")
.. note::
Note that, for accessing keyword arguments, you do *not* use the
:attr:`.Var.name`
attribute of the looked-for input variable or node, but the *argument name*.
Consider this case from above::
a = lsl.Var.new_value(2.0, name="a")
b = lsl.Var.new_obs(1.0, lsl.Dist(tfd.Normal, loc=a, scale=1.0))
b.dist_node["loc"]
Here, we retrieve the variable ``a`` with the name ``"a"``. But for the
indexing, we use the *argument name* ``"loc"`` from the call to ``lsl.Dist`.
.. rubric:: Swapping out inputs
You can also use square-bracket indexing on :attr:`.Var.value_node` and
:attr:`.Var.dist_node` to swap out existing inputs. This allows you to easily make
changes to your model.
Swap out inputs to a calculator via :attr:`.Var.value_node`:
>>> a = lsl.Var.new_value(2.0, name="a")
>>> b = lsl.Var.new_calc(lambda x: x + 1.0, x=a)
>>> c = lsl.Var.new_value(3.0, name="c")
>>> b.value_node["x"] = c
>>> b.value_node["x"]
Var(name="c")
Swap out inputs to a distribution via :attr:`.Var.dist_node`:
>>> a = lsl.Var.new_value(2.0, name="a")
>>> b = lsl.Var.new_obs(1.0, lsl.Dist(tfd.Normal, loc=a, scale=1.0))
>>> c = lsl.Var.new_value(3.0, name="c")
>>> b.dist_node["loc"] = c
>>> b.dist_node["loc"]
Var(name="c")
Parameters
----------
value
The value of the variable.
distribution
The probability distribution of the variable.
name
The name of the variable. If you do not specify a name, a unique name will be \
automatically generated upon initialization of a :class:`.Model`.
inference
Additional information that can be used to set up inference algorithms.
See Also
--------
.Var.new_obs : Initializes a strong variable that holds observed data.
.Var.new_param : Initializes a strong variable that acts as a model parameter.
.Var.new_calc :
Initializes a weak variable that is a function of other variables.
.Var.new_value : Initializes a strong variable without a distribution.
:meth:`.Var.transform` : Transforms a variable by adding a new transformed
variable as an input. This is useful for variables that are constrained to a
certain domain, e.g. positive values.
.Calc :
A node representing a general calculation/operation in JAX or Python. Use this
instead of :meth:`~.Var.new_calc` if you want to hide your calculation in the
model graph produced by :func:`.plot_vars`.
.Value :
A node representing a static value. Use this
instead of :meth:`~.Var.new_value` if you want to hide your value in the
model graph produced by :func:`.plot_vars`.
.Dist :
A node representing a ``tensorflow_probability``
:class:`~tfp.distributions.Distribution`.
"""
__slots__ = (
"info",
"inference",
"_auto_transform",
"_dist_node",
"_groups",
"_name",
"_observed",
"_parameter",
"_role",
"_value_node",
"_var_value_node",
)
def __init__(
self,
value: Any,
distribution: Dist | None = None,
name: str = "",
inference: InferenceTypes = None,
):
self._name = name
self._value_node: Node = Value(None)
self._dist_node: Dist = NoDist()
self._var_value_node: VarValue = VarValue(
self._value_node, _name=f"{self._name}_var_value"
)
self._var_value_node._set_var(self)
# use setters
self.value_node = value # type: ignore # unfrozen
self.dist_node = distribution # type: ignore # unfrozen
self._auto_transform = False
self._groups: dict[str, Group] = {}
self._observed = False
self._parameter = False
self._role = ""
self.info: dict[str, Any] = {}
"""Additional meta-information about the variable as a dict."""
self.inference = inference
[docs]
@classmethod
def new_param(
cls,
value: Any,
distribution: Dist | None = None,
name: str = "",
inference: InferenceTypes = None,
) -> Var:
"""
Initializes a strong variable that acts as a model parameter.
A parameter is a strong variable that can have a distribution. If it does have a
distribution, its :attr:`~.Var.log_prob` is counted in a model's log prior, i.e.
:attr:`~.Model.log_prior`.
Parameters
----------
value
The value of the variable.
distribution
The probability distribution of the variable.
name
The name of the variable. If you do not specify a name, a unique name will \
be automatically generated upon initialization of a :class:`.Model`.
inference
Additional information that can be used to set up inference algorithms.
See Also
--------
.Var.new_obs : Initializes a strong variable that holds observed data.
.Var.new_calc :
Initializes a weak variable that is a function of other variables.
.Var.new_value : Initializes a strong variable without a distribution.
Examples
--------
A simple parameter without a distribution and without a name:
>>> x = lsl.Var.new_param(1.0)
>>> x
Var(name="")
A simple parameter with a normal prior:
>>> prior = lsl.Dist(tfd.Normal, loc=0.0, scale=1.0)
>>> x = lsl.Var.new_param(1.0, distribution=prior)
>>> x
Var(name="")
"""
var = cls(value, distribution, name, inference=inference)
var.value_node.monitor = True
var.parameter = True
return var
[docs]
@classmethod
def new_obs(
cls, value: Any, distribution: Dist | None = None, name: str = ""
) -> Var:
"""
Initializes a strong variable that holds observed data.
An observed variables is a strong variable that can have a distribution.
If it does have a distribution, its :attr:`~.Var.log_prob` is counted in
a model's log likelihood, i.e. :attr:`~.Model.log_lik`.
Parameters
----------
value
The value of the variable.
distribution
The probability distribution of the variable.
name
The name of the variable. If you do not specify a name, a unique name will \
be automatically generated upon initialization of a :class:`.Model`.
See Also
--------
.Var.new_param : Initializes a strong variable that acts as a model parameter.
.Var.new_calc :
Initializes a weak variable that is a function of other variables.
.Var.new_value : Initializes a strong variable without a distribution.
Examples
--------
A simple observed variable without a distribution and without a name:
>>> x = lsl.Var.new_obs(1.0)
>>> x
Var(name="")
A simple observed variable with a normal distribution:
>>> prior = lsl.Dist(tfd.Normal, loc=0.0, scale=1.0)
>>> x = lsl.Var.new_param(1.0, distribution=prior)
>>> x
Var(name="")
"""
var = cls(value, distribution, name)
var.observed = True
return var
[docs]
@classmethod
def new_calc(
cls,
function: Callable[..., Any],
*inputs: Any,
distribution: Dist | None = None,
name: str = "",
_needs_seed: bool = False,
_update_on_init: bool = True,
**kwinputs: Any,
) -> Var:
"""
Initializes a weak variable that is a function of other variables.
A calculating variable can wrap arbitrary calculations in pure JAX functions.
.. tip::
The wrapped function must be jit-compilable by JAX. This mainly means that
it must be a pure function, i.e. it must not have any side effects and,
given the same input, it must always return the same output. Some special
consideration is also required for loops and conditionals.
Please consult the JAX docs_ for details.
Parameters
----------
function
The function to be wrapped. Must be jit-compilable by JAX.
*inputs
Non-keyword inputs. Any inputs that are not already nodes or :class:`.Var` \
will be converted to :class:`.Value` nodes. The values of these inputs \
will be passed to the wrapped function in the same order they are entered \
here.
distribution
The probability distribution of the variable.
name
The name of the node. If you do not specify a name, a unique name will be \
automatically generated upon initialization of a :class:`.Model`.
_needs_seed
Whether the node needs a seed / PRNG key.
_update_on_init
If ``True``, the calculator will try to evaluate its function upon \
initialization.
**kwinputs
Keyword inputs. Any inputs that are not already nodes or :class:`.Var`s
will be converted to :class:`.Data` nodes. The values of these inputs will \
be passed to the wrapped function as keyword arguments.
Notes
-----
Internally, this constructor initializes and wraps a :class:`.Calc` node.
See Also
--------
.Var.new_param : Initializes a strong variable that acts as a model parameter.
.Var.new_obs : Initializes a strong variable that holds observed data.
.Var.new_value : Initializes a strong variable without a distribution.
.Calc : The calculator node class.
Examples
--------
A simple calculator node, taking the exponential value of an input parameter.
>>> log_scale = lsl.Var.new_param(0.0, name="log_scale")
>>> scale = lsl.Var.new_calc(jnp.exp, log_scale, name="scale")
>>> print(scale.value)
1.0
You can also use your own functions as long as they are jit-compilable by JAX.
>>> def compute_variance(x):
... return jnp.exp(x)**2
>>> log_scale = lsl.Var.new_param(0.0, name="log_scale")
>>> variance = lsl.Var.new_calc(compute_variance, log_scale, name="scale")
>>> print(variance.value)
1.0
The value of the calculating variable is updated when :meth:`~.Var.update` is
called.
>>> log_scale = lsl.Var.new_param(0.0, name="log_scale")
>>> scale = lsl.Var.new_calc(jnp.exp, log_scale, name="scale")
>>> print(scale.value)
1.0
>>> log_scale.value = 1.0
>>> print(scale.value)
1.0
>>> print(scale.update().value)
2.7182817
.. _docs: https://jax.readthedocs.io/en/latest/notebooks/Common_Gotchas_in_JAX.html
""" # noqa: E501
calc = Calc(
function,
*inputs,
_name=f"{name}_calc",
_needs_seed=_needs_seed,
_update_on_init=_update_on_init,
**kwinputs,
)
var = cls(calc, distribution=distribution, name=name)
return var
[docs]
@classmethod
def new_value(
cls, value: Any, name: str = "", inference: InferenceTypes = None
) -> Var:
"""
Initializes a strong variable without a distribution.
Parameters
----------
value
The value of the variable.
distribution
The probability distribution of the variable.
name
The name of the variable. If you do not specify a name, a unique name will \
be automatically generated upon initialization of a :class:`.Model`.
inference
Additional information that can be used to set up inference algorithms.
See Also
--------
.Var.new_param : Initializes a strong variable that acts as a model parameter.
.Var.new_obs : Initializes a strong variable that acts as an observed variable.
.Var.new_calc :
Initializes a weak variable that is a function of other variables.
Examples
--------
A simple value variable without a name:
>>> x = lsl.Var.new_value(1.0)
>>> x
Var(name="")
"""
var = cls(value, name=name, inference=inference)
return var
[docs]
def get_inference(self, key: str | None) -> InferenceTypes:
if isinstance(self.inference, dict):
if key is None:
raise ValueError(
f"{key=} is invalid. Possible keys: {list(self.inference)}."
)
return self.inference[key]
return self.inference
[docs]
@in_model_method
def all_output_nodes(self) -> tuple[Node, ...]:
"""Returns all output *nodes* as a unique tuple."""
nodes = list(self.value_node.all_output_nodes())
nodes.extend(self.var_value_node.all_output_nodes())
nodes.extend(self._dist_node.all_output_nodes())
nodes = [node for node in nodes if node is not self.var_value_node]
return _unique_tuple(nodes)
[docs]
@in_model_method
def all_output_vars(self) -> tuple[Var, ...]:
"""
Returns all output *variables* as a unique tuple.
The returned tuple also contains output variables that are indirect outputs
of this variable through nodes without variables.
"""
nodes = list(self.all_output_nodes())
visited = []
_vars = []
while nodes:
node = nodes.pop()
if node not in visited:
if node.var and node.var is not self:
_vars.append(node.var)
else:
nodes.extend(node.all_output_nodes())
visited.append(node)
return _unique_tuple(_vars)
@property
def auto_transform(self) -> bool:
"""
Whether the variable should automatically be transformed to the unconstrained
space ``R**n`` upon model initialization.
"""
return self._auto_transform
@auto_transform.setter
def auto_transform(self, auto_transform: bool):
self._auto_transform = auto_transform
@property
def dist_node(self) -> Dist | None:
"""The distribution node of the variable."""
return self._dist_node if self.has_dist else None
@dist_node.setter
@no_model_setter
def dist_node(self, dist_node: Dist | None):
if not dist_node:
dist_node = NoDist()
if dist_node.model:
raise RuntimeError(
f"{repr(dist_node)} is part of a model, cannot be set as dist node"
)
if self.name and not dist_node.name:
dist_node.name = f"{self.name}_log_prob" # type: ignore # unfrozen
self._dist_node._unset_var()
dist_node._set_var(self)
dist_node.at = self.var_value_node # type: ignore # unfrozen
self._dist_node = dist_node
@property
def groups(self) -> MappingProxyType[str, Group]:
"""The groups that this variable is a part of."""
return MappingProxyType(self._groups)
@property
def has_dist(self) -> bool:
"""Whether the variable has a probability distribution."""
return not isinstance(self._dist_node, NoDist)
@property
def log_prob(self) -> Array:
"""
The log-probability of the variable.
A variable without a probability distribution has a log-probability of 0.0.
"""
return self._dist_node.value
@property
def model(self) -> Model | None:
"""The model the variable is part of."""
return self.value_node.model
@property
def name(self) -> str:
"""The name of the variable."""
return self._name
@name.setter
@no_model_setter
def name(self, name: str):
if name and self.value_node.name in ("", f"{self.name}_value"):
self.value_node.name = f"{name}_value" # type: ignore # unfrozen
self.var_value_node.name = f"{name}_var_value" # type: ignore # unfrozen
if name and self._dist_node.name in ("", f"{self.name}_log_prob"):
self._dist_node.name = f"{name}_log_prob" # type: ignore # unfrozen
self._name = name
@property
def nodes(self) -> list[Node]:
"""The nodes of the variable as a list."""
nodes = [self.value_node, self.var_value_node]
if self.dist_node:
nodes.append(self.dist_node)
return nodes
@property
def observed(self) -> bool:
"""
Whether the variable is observed.
If a variable is observed and has an associated
probability distribution, its log-probability is automatically added to the
model log-likelihood (see :attr:`.Model.log_lik`).
See Also
--------
.obs : Helper function to declare a variable as a parameter.
.Model.log_prior : The log-prior of a Liesel model.
.Var.parameter : Whether the variable is a parameter. If a variable is \
a parameter, it is not observed.
Notes
-----
We recommend to use the :func:`.obs` helper function to declare an observed
variable.
"""
return self._observed
@observed.setter
@no_model_setter
def observed(self, observed: bool):
self._observed = observed
@property
def parameter(self) -> bool:
"""
Whether the variable is a parameter.
If the variable is a parameter and has an associated
probability distribution, its log-probability is added to the
model's :attr:`~.Model.log_prior`.
See Also
--------
.param : Helper function to declare a variable as a parameter.
.Model.log_prior : The log-prior of a Liesel model.
.Var.observed : Whether the variable is observed. If a variable is \
a parameter, it is not observed.
Notes
-----
We recommend to use the :func:`.param` helper function to declare a variable
as a parameter.
"""
return self._parameter
@parameter.setter
@no_model_setter
def parameter(self, parameter: bool):
self._parameter = parameter
@property
def role(self) -> str:
"""The role of the variable."""
return self._role
@role.setter
def role(self, role: str):
self._role = role
@property
def strong(self) -> bool:
"""
Whether the variable is strong.
A strong node is a node whose value is defined outside of the model, for
example, if the node represents some observed data or a parameter (parameters
are usually set by an inference algorithm such as an optimizer or sampler). In
contrast, a weak node is a node whose value is defined within the model, that
is, it is a deterministic function of some other nodes. An exp-transformation
mapping a real-valued parameter to a positive number, for example, would be a
weak node.
See Also
--------
.weak : Whether the variable is weak. In general, ``strong = not weak``.
"""
return isinstance(self.value_node, Value)
[docs]
def update(self) -> Var:
"""Updates the variable."""
self.value_node.update()
self._dist_node.update()
return self
@property
def value(self) -> Any:
"""
The value of the variable.
Can only be set if the variable is strong. If the variable is part of
a :class:`.Model` ``m`` with ``m.auto_update == True``, setting the value of
the variable triggers an update of the model. The auto-update can be disabled
to improve the performance if multiple model parameters are updated at once.
"""
return self.value_node.value
@value.setter
def value(self, value: Any):
if self.weak:
raise RuntimeError(f"{repr(self)} is weak, cannot set value")
self.value_node.value = value # type: ignore # data node
@property
def value_node(self) -> Node:
"""The value node of the variable."""
return self._value_node
@value_node.setter
@no_model_setter
def value_node(self, value_node: Any):
if isinstance(value_node, Var):
value_node = Calc(lambda x: x, value_node)
if not isinstance(value_node, Node):
value_node = Value(value_node)
if value_node.model:
raise RuntimeError(
f"{repr(value_node)} is part of a model, cannot be set as value node"
)
if self.name and not value_node.name:
value_node.name = f"{self.name}_value"
self.value_node._unset_var()
value_node._set_var(self)
self._value_node = value_node
self._var_value_node.set_inputs(self._value_node)
@property
def var_value_node(self) -> VarValue:
"""The proxy node for the value of the variable."""
return self._var_value_node
@property
def weak(self) -> bool:
"""
Whether the variable is weak.
A weak variable is a variable whose value is defined within the model, that
is, it is a deterministic function of some other nodes.
See Also
--------
.strong : Whether the variable is strong. In general, ``weak = not strong``.
"""
return not self.strong
def __repr__(self) -> str:
return f'{type(self).__name__}(name="{self.name}")'
def _plot(
self, which: Literal["vars", "nodes"] = "vars", verbose: bool = False, **kwargs
) -> None:
if self.model is not None:
match which:
case "vars":
subgraph = self.model.var_parental_subgraph(self)
return plot_vars(subgraph, **kwargs)
case "nodes":
self_nodes = [self.value_node, self.dist_node, self.var_value_node]
filtered_nodes = [nd for nd in self_nodes if nd is not None]
subgraph = self.model.node_parental_subgraph(*filtered_nodes)
return plot_nodes(subgraph, **kwargs)
from liesel.model.model import TemporaryModel
try:
to_float32 = not jax.config.jax_enable_x64 # type: ignore
except Exception: # just to be really sure in case anything changes
# this is an implicit test of whether x64 flag is enabled
import jax.numpy as jnp
to_float32 = jnp.array(1.0).dtype == jnp.dtype("float32")
with TemporaryModel(self, verbose=verbose, to_float32=to_float32) as model:
match which:
case "vars":
subgraph = model.var_parental_subgraph(self)
plot_vars(subgraph, **kwargs)
case "nodes":
self_nodes = [self.value_node, self.dist_node, self.var_value_node]
filtered_nodes = [nd for nd in self_nodes if nd is not None]
subgraph = model.node_parental_subgraph(*filtered_nodes)
plot_nodes(subgraph, **kwargs)
[docs]
def plot_vars(
self,
show: bool = True,
save_path: str | None | IO = None,
width: int = 14,
height: int = 10,
prog: Literal[
"dot", "circo", "fdp", "neato", "osage", "patchwork", "sfdp", "twopi"
] = "dot",
verbose: bool = False,
) -> None:
"""
Plots the variables of the Liesel sub-model that terminates in this variable.
Wraps :func:`~.viz.plot_vars`.
Parameters
----------
verbose
If ``True``, logs a message if unnamed variables or nodes are temporarily \
named for plotting.
show
Whether to show the plot in a new window.
save_path
Path to save the plot. If not provided, the plot will not be saved.
width
Width of the plot in inches.
height
Height of the plot in inches.
prog
Layout parameter. Available layouts: circo, dot (the default), fdp, neato, \
osage, patchwork, sfdp, twopi.
verbose
If ``True``, the message that will be logged if unnamed nodes are \
automatically named for plotting contains a list of the automatically \
assigned names.
See Also
--------
.Var.plot_vars : Plots the variables of the Liesel sub-model that terminates in
this variable.
.Var.plot_nodes : Plots the nodes of the Liesel sub-model that terminates in
this variable.
.Model.plot_vars : Plots the variables of a Liesel model.
.Model.plot_nodes : Plots the nodes of a Liesel model.
.viz.plot_vars : Plots the variables of a Liesel model.
.viz.plot_nodes : Plots the nodes of a Liesel model.
"""
return self._plot(
which="vars",
verbose=verbose,
show=show,
save_path=save_path,
width=width,
height=height,
prog=prog,
)
[docs]
@in_model_method
def predict(
self,
samples: dict[str, jax.typing.ArrayLike],
newdata: dict[str, jax.typing.ArrayLike] | None = None,
) -> Array:
"""
Returns an array of predictions for this variable.
Parameters
----------
samples
Dictionary of samples at which to evaluate predictions. All values of the \
dictionary are assumed to have two leading dimensions corresponding to \
``(nchains, niteration)``.
newdata
Dictionary of new data at which to evaluate predictions. The keys should \
correspond to variable or node names in the model whose values should be \
set to the given values before evaluating predictions.
"""
assert self.model is not None
submodel = self.model.parental_submodel(self)
newdata = newdata if newdata is not None else {}
newdata = newdata.copy()
for key in list(newdata.keys()):
if key not in self.model.vars or (key in self.model.nodes):
raise KeyError(f"{key} is not part of the model.")
if key not in submodel.vars or (key in submodel.nodes):
newdata.pop(key, None)
pred = submodel.predict(samples=samples, predict=[self.name], newdata=newdata)
return pred[self.name]
[docs]
def sample(
self,
shape: Sequence[int],
seed: jax.Array,
posterior_samples: dict[str, jax.typing.ArrayLike] | None = None,
fixed: Sequence[str] = (),
newdata: dict[str, jax.typing.ArrayLike] | None = None,
dists: dict[str, Dist] | None = None,
) -> dict[str, Array]:
"""
Draws samples from the parental model for this variable.
Parameters
----------
shape
Sample shape.
seed
The seed is split and distributed to the seed nodes of the model. \
Must be a jax RNG key array that satisfies \
``jnp.issubdtype(key.dtype, jax.dtypes.prng_key)``. \
See :mod:`jax.random` and \
https://docs.jax.dev/en/latest/jep/9263-typed-keys.html for more details.
posterior_samples
Dictionary of samples at which to evaluate predictions. All values of the \
dictionary are assumed to have two leading dimensions corresponding to \
``(nchains, niteration)``.
fixed
The names of the nodes or variables to be excluded from the simulation. \
By default, no nodes or variables are skipped.
newdata
Dictionary of new data at which to produce samples. The keys should \
correspond to variable or node names in the model whose values should be \
set to the given values before sampling. If ``None`` \
(default), the current variable values are used.
dists
Can be used to provide a dictionary of variable names and :class:`.Dist` \
instances to use in sampling. If ``None`` (default), samples are drawn for \
each variable using their :attr:`.Var.dist_node`.
Notes
-----
When compiling this function with ``jax.jit``, the arguments ``shape``,
``fixed``, and ``dists`` must be static.
Returns
-------
A dictionary of variable and node names and their sampled values. Includes
only sampled variables.
"""
if self.model:
submodel = self.model.parental_submodel(self)
drawn_samples = submodel.sample(
shape=shape,
seed=seed,
posterior_samples=posterior_samples,
fixed=fixed,
newdata=newdata,
dists=dists,
)
return drawn_samples
from .model import TemporaryModel
try:
to_float32 = not jax.config.jax_enable_x64 # type: ignore
except Exception: # just to be really sure in case anything changes
# this is an implicit test of whether x64 flag is enabled
import jax.numpy as jnp
to_float32 = jnp.array(1.0).dtype == jnp.dtype("float32")
with TemporaryModel(self, silent=True, to_float32=to_float32) as model:
drawn_samples = model.sample(
shape=shape,
seed=seed,
posterior_samples=posterior_samples,
fixed=fixed,
newdata=newdata,
dists=dists,
)
return drawn_samples
[docs]
def plot_nodes(
self,
show: bool = True,
save_path: str | None | IO = None,
width: int = 14,
height: int = 10,
prog: Literal[
"dot", "circo", "fdp", "neato", "osage", "patchwork", "sfdp", "twopi"
] = "dot",
verbose: bool = False,
) -> None:
"""
Plots the nodes of the Liesel sub-model that terminates in this variable.
Wraps :func:`~.viz.plot_nodes`.
Parameters
----------
verbose
If ``True``, logs a message if unnamed variables or nodes are temporarily \
named for plotting.
show
Whether to show the plot in a new window.
save_path
Path to save the plot. If not provided, the plot will not be saved.
width
Width of the plot in inches.
height
Height of the plot in inches.
prog
Layout parameter. Available layouts: circo, dot (the default), fdp, neato, \
osage, patchwork, sfdp, twopi.
verbose
If ``True``, the message that will be logged if unnamed nodes are \
automatically named for plotting contains a list of the automatically \
assigned names.
See Also
--------
.Var.plot_vars : Plots the variables of the Liesel sub-model that terminates in
this variable.
.Var.plot_nodes : Plots the nodes of the Liesel sub-model that terminates in
this variable.
.Model.plot_vars : Plots the variables of a Liesel model.
.Model.plot_nodes : Plots the nodes of a Liesel model.
.viz.plot_vars : Plots the variables of a Liesel model.
.viz.plot_nodes : Plots the nodes of a Liesel model.
"""
return self._plot(
which="nodes",
verbose=verbose,
show=show,
save_path=save_path,
width=width,
height=height,
prog=prog,
)
def _transform_var_with_bijector_instance(var: Var, bijector_inst: jb.Bijector) -> Var:
if var.dist_node is None: # type: ignore
raise RuntimeError(f"{var} has no distribution")
InputDist = var.dist_node.distribution
inputs = var.dist_node.inputs
kwinputs = var.dist_node.kwinputs
bijector_inv = jb.Invert(bijector_inst)
def transform_dist(*args, **kwargs):
return jd.TransformedDistribution(InputDist(*args, **kwargs), bijector_inv)
transformed_dist = Dist(
transform_dist,
*inputs,
**kwinputs,
_name="",
_needs_seed=var.dist_node.needs_seed,
)
transformed_dist.per_obs = var.dist_node.per_obs
if var.weak:
try:
value_function = var.value_node.function # type: ignore
except AttributeError as e:
raise AttributeError(
"Trying to transform a weak variable without calculator node."
) from e
def forward(*args, **kwargs):
return bijector_inv.forward(value_function(*args, **kwargs))
value_inputs = var.value_node.inputs
value_kwinputs = var.value_node.kwinputs
value_node_needs_seed = var.value_node.needs_seed
try:
value_node_upadte_on_init = var.value_node._update_on_init # type: ignore
except AttributeError as e:
raise e
transformed_var = Var(
Calc(
forward,
*value_inputs,
_name="",
_needs_seed=value_node_needs_seed,
_update_on_init=value_node_upadte_on_init,
**value_kwinputs,
),
transformed_dist,
name=f"{var.name}_transformed",
)
else:
transformed_var = Var(
bijector_inv.forward(var.value),
transformed_dist,
name=f"{var.name}_transformed",
)
var.value_node = Calc(bijector_inst.forward, transformed_var)
return transformed_var
def _transform_var_with_bijector_class(
var: Var, bijector_cls: type[jb.Bijector] | None, *args, **kwargs
) -> Var:
if var.dist_node is None: # type: ignore
raise RuntimeError(f"{var} has no distribution")
InputDist = var.dist_node.distribution
dist_inputs = InputGroup(
*var.dist_node.inputs,
**var.dist_node.kwinputs, # type: ignore
)
bijector_inputs = InputGroup(*args, **kwargs)
# define distribution "class" for the transformed var
def transform_dist(dist_args: ArgGroup, bijector_args: ArgGroup):
tfp_dist = InputDist(*dist_args.args, **dist_args.kwargs)
bjargs, bjkwargs = bijector_args.args, bijector_args.kwargs
if bijector_cls is None:
default_bijector_cls = tfp_dist.experimental_default_event_space_bijector
bijector_inst = default_bijector_cls(*bjargs, **bjkwargs)
else:
bijector_inst = bijector_cls(*bjargs, **bjkwargs)
bijector_inv = jb.Invert(bijector_inst)
transformed_dist = jd.TransformedDistribution(
tfp_dist, bijector_inv, validate_args=tfp_dist.validate_args
)
return transformed_dist
dist_node_transformed = Dist(
transform_dist,
dist_inputs,
bijector_inputs,
_name="",
_needs_seed=var.dist_node.needs_seed,
)
dist_node_transformed.per_obs = var.dist_node.per_obs
bijector_inv = dist_node_transformed.init_dist().bijector
if var.weak:
try:
value_function = var.value_node.function # type: ignore
except AttributeError as e:
raise AttributeError(
"Trying to transform a weak variable without calculator node."
) from e
def forward(*args, **kwargs):
return bijector_inv.forward(value_function(*args, **kwargs))
value_inputs = var.value_node.inputs
value_kwinputs = var.value_node.kwinputs
value_node_needs_seed = var.value_node.needs_seed
try:
value_node_upadte_on_init = var.value_node._update_on_init # type: ignore
except AttributeError as e:
raise e
transformed_var = Var(
Calc(
forward,
*value_inputs,
_name="",
_needs_seed=value_node_needs_seed,
_update_on_init=value_node_upadte_on_init,
**value_kwinputs,
),
dist_node_transformed,
name=f"{var.name}_transformed",
)
else:
transformed_var = Var(
bijector_inv.forward(var.value),
dist_node_transformed,
name=f"{var.name}_transformed",
)
def bijector_fn(value, dist_inputs, bijector_inputs):
bijector = transform_dist(dist_inputs, bijector_inputs).bijector
return bijector.inverse(value)
var.value_node = Calc(bijector_fn, transformed_var, dist_inputs, bijector_inputs)
return transformed_var
def _transform_var_without_dist_with_bijector_instance(
var: Var, bijector_inst: jb.Bijector
) -> Var:
if var.strong:
transformed_var = Var(
bijector_inst.inverse(var.value),
name=f"{var.name}_transformed",
)
else:
transformed_var = Var.new_calc(
bijector_inst.inverse,
var.value_node,
name=f"{var.name}_transformed",
)
var.value_node = Calc(bijector_inst.forward, transformed_var)
return transformed_var
def _transform_var_without_dist_with_bijector_class(
var: Var, bijector_cls: type[jb.Bijector] | None, *args, **kwargs
) -> Var:
def bijection_inverse(x, *bjargs, **bjkwargs):
# this somewhat over-complicated functionality accounts for bijector
# arguments being passed directly as values, or as Liesel Vars and Nodes.
# This inverse is executed only once in the initialization of the transformed
# variable.
arg_values = []
for arg in bjargs:
try:
arg_values.append(arg.value)
except AttributeError:
arg_values.append(arg)
kwarg_values = {}
for key, val in bjkwargs.items():
try:
kwarg_values[key] = val.value
except AttributeError:
kwarg_values[key] = val
bijector_inst = bijector_cls(*arg_values, **kwarg_values)
bijector_inv = jb.Invert(bijector_inst)
return bijector_inv(x)
def bijection_forward(x, *bjargs, **bjkwargs):
bijector_inst = bijector_cls(*bjargs, **bjkwargs)
return bijector_inst(x)
if var.strong:
transformed_var = Var(
bijection_inverse(var.value, *args, **kwargs),
name=f"{var.name}_transformed",
)
else:
transformed_var = Var.new_calc(
bijection_inverse,
var.value_node,
*args,
**kwargs,
name=f"{var.name}_transformed",
)
var.value_node = Calc(bijection_forward, transformed_var, *args, **kwargs)
return transformed_var
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Group ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
[docs]
class Group:
"""
A group holds a collection of related :class:`.Var` and/or :class:`.Node` objects.
They allow you to do three basic things:
1. Store related nodes together for easier access.
2. Access their member nodes and variables via ``group["name"]``, where ``"name"``
is the group-specific name, which can be different from the :attr:`.Var.name` /
:attr:`.Node.name`.
3. Easily retrieve a variable's or a node's value from a :attr:`.Model.state` based
on their group-specific name via :meth:`.value_from`.
Parameters
----------
name
The group's name. Must be unique among the groups of its members, and within
a model.
**nodes_and_vars
An arbitrary number of nodes or variables. The keywords will be used as the
group-specific names of the respective objects.
See Also
--------
* :attr:`.Node.groups` and :attr:`.Var.groups` are :obj:`MappingProxyType` objects
(basically read-only dictionaries) of the groups whose member the respective
object is.
* :meth:`.GraphBuilder.groups` and :meth:`.Model.groups` are methods that collect
and return all groups within the graph/model.
Notes
-----
Note the following:
- Groups can only be filled upon initialization.
- After initialization, variables and nodes cannot be removed from a group.
Examples
--------
Add a variable to a group:
>>> my_var = lsl.Var(0.0, name="long_unique_variable_name")
>>> grp = lsl.Group(name="demo_group", short_name=my_var)
>>> grp
Group(name="demo_group")
Access the variable by its group-specific name:
>>> grp["short_name"]
Var(name="long_unique_variable_name")
Retrieve the value of a variable from a model state:
>>> model_state = {my_var.value_node.name: lsl.NodeState(10.0, False)}
>>> grp.value_from(model_state, "short_name")
10.0
"""
def __init__(self, name: str, **nodes_and_vars: Node | Var) -> None:
self._name = name
self._nodes_and_vars = nodes_and_vars
for member in self._nodes_and_vars.values():
if name in member.groups:
raise RuntimeError(
f"{repr(member)} is already a member of a group "
f"with the name {repr(name)}"
)
member._groups[name] = self
self._nodes = {
name: obj
for name, obj in self._nodes_and_vars.items()
if isinstance(obj, Node)
}
self._vars = {
name: obj
for name, obj in self._nodes_and_vars.items()
if isinstance(obj, Var)
}
@property
def name(self) -> str:
"""The group's name."""
return self._name
[docs]
def value_from(self, model_state: dict[str, NodeState], name: str) -> Array:
"""
Retrieves the value of a node or variable that is a member of the group from
a model state.
Parameters
----------
model_state
The state of a Liesel model, i.e. a :class:`~.Model.state`.
name
The name of the node or variable within this group.
Returns
-------
The value of the node or variable.
"""
member = self[name]
if isinstance(member, Var):
value_name = member.value_node.name
else:
value_name = member.name
return model_state[value_name].value
@property
def vars(self) -> MappingProxyType[str, Var]:
"""A mapping of the variables in the group with their names as keys."""
return MappingProxyType(self._vars)
@property
def nodes(self) -> MappingProxyType[str, Node]:
"""A mapping of the nodes in the group with their names as keys."""
return MappingProxyType(self._nodes)
@property
def nodes_and_vars(self) -> MappingProxyType[str, Node | Var]:
"""A mapping of all group members with their names as keys."""
return MappingProxyType(self._nodes_and_vars)
def __contains__(self, key) -> bool:
return key in self._nodes_and_vars
def __getitem__(self, key) -> Var | Node:
return self._nodes_and_vars[key]
def __repr__(self) -> str:
return f'{type(self).__name__}(name="{self.name}")'
