"""
The model and the graph builder.
"""
from __future__ import annotations
import logging
import re
from collections import Counter
from collections.abc import Iterable
from copy import deepcopy
from types import MappingProxyType
from typing import IO, Any, TypeVar
import dill
import jax
import jax.random
import networkx as nx
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 .nodes import (
ArgGroup,
Array,
Bijector,
Calc,
Data,
Dist,
InputGroup,
Node,
NodeState,
TransientIdentity,
Var,
)
from .viz import plot_nodes, plot_vars
__all__ = ["GraphBuilder", "Model", "load_model", "save_model"]
logger = logging.getLogger(__name__)
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Graph builder ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
NV = TypeVar("NV", Node, Var)
def _reduced_sum(*args: Array) -> Array:
"""Computes the sum after reducing arrays to scalars."""
reduced = (arg.sum() if hasattr(arg, "sum") else arg for arg in args)
return sum(reduced)
def _transform_back(var_transformed: Var) -> Calc:
"""
Creates a :class:`.Calc` mapping a transformed parameter back to
the original domain.
"""
if var_transformed.dist_node is None:
raise RuntimeError(
f"{repr(var_transformed)} must have a transformed distribution"
)
transformed_distribution = var_transformed.dist_node.distribution
def fn(at, *args, **kwargs):
bijector = transformed_distribution(*args, **kwargs).bijector
return bijector.inverse(at)
inputs = var_transformed.dist_node.inputs
kwinputs = var_transformed.dist_node.kwinputs
return Calc(fn, var_transformed.value_node, *inputs, **kwinputs) # type: ignore
[docs]class GraphBuilder:
"""
A graph builder to prepare a :class:`.Model`.
Constructs a model containing all nodes and variables that were added to the graph
builder and their recursive inputs. The outputs of the nodes are not added to the
model automatically, so the root nodes always need to be added explicitly.
The standard workflow is to create the nodes and variables, add them to a graph
builder, and construct a model from the graph builder. After the model has been
constructed, some methods of the graph builder are not available anymore.
Example
-------
>>> a = lsl.Var(1.0, name="a")
>>> b = lsl.Var(2.0, name="b")
>>> c = lsl.Var(lsl.Calc(lambda x, y: x + y, a, b), name="c")
>>> gb = lsl.GraphBuilder()
>>> gb.add(c)
GraphBuilder<0 nodes, 1 vars>
>>> model = gb.build_model()
>>> model
Model<9 nodes, 3 vars>
>>> c.value
3.0
>>> gb.vars
set()
"""
def __init__(self):
self.nodes: set[Node] = set()
"""The nodes that were explicitly added to the graph."""
self.vars: set[Var] = set()
"""The variables that were explicitly added to the graph."""
self._log_lik_node: Node | None = None
self._log_prior_node: Node | None = None
self._log_prob_node: Node | None = None
def _add_model_log_lik_node(self) -> GraphBuilder:
"""Adds the model log-likelihood node with the name ``_model_log_lik``."""
if self.log_lik_node:
self.add(TransientIdentity(self.log_lik_node, _name="_model_log_lik"))
return self
_, _vars = self._all_nodes_and_vars()
inputs = (v.dist_node for v in _vars if v.has_dist and v.observed)
node = Calc(_reduced_sum, *inputs, _name="_model_log_lik")
self.add(node)
return self
def _add_model_log_prior_node(self) -> GraphBuilder:
"""Adds the model log-prior node with the name ``_model_log_prior``."""
if self.log_prior_node:
self.add(TransientIdentity(self.log_prior_node, _name="_model_log_prior"))
return self
_, _vars = self._all_nodes_and_vars()
inputs = (v.dist_node for v in _vars if v.has_dist and v.parameter)
node = Calc(_reduced_sum, *inputs, _name="_model_log_prior")
self.add(node)
return self
def _add_model_log_prob_node(self) -> GraphBuilder:
"""Adds the model log-probability node with the name ``_model_log_prob``."""
if self.log_prob_node:
self.add(TransientIdentity(self.log_prob_node, _name="_model_log_prob"))
return self
nodes, _ = self._all_nodes_and_vars()
inputs = (n for n in nodes if isinstance(n, Dist))
node = Calc(_reduced_sum, *inputs, _name="_model_log_prob")
self.add(node)
return self
def _add_model_seed_nodes(self) -> GraphBuilder:
"""Adds the model seed nodes with the names ``_model_*_seed``."""
nodes, _ = self._all_nodes_and_vars()
for node in nodes:
if node.needs_seed:
seed = Data(jax.random.PRNGKey(0), _name=f"_model_{node.name}_seed")
node.set_inputs(*node.inputs, **{"seed": seed} | node.kwinputs)
return self
def _all_nodes_and_vars(self) -> tuple[list[Node], list[Var]]:
"""
Returns all nodes and variables that were explicitly or implicitly
(as recursive inputs) added to the graph.
"""
nodes = list(self.nodes)
nodes.extend(node for var in self.vars for node in var.nodes)
if self.log_lik_node:
nodes.append(self.log_lik_node)
if self.log_prior_node:
nodes.append(self.log_prior_node)
if self.log_prob_node:
nodes.append(self.log_prob_node)
all_nodes: list[Node] = []
all_vars: list[Var] = []
while nodes:
node = nodes.pop()
if node in all_nodes:
continue
nodes.extend(node.all_input_nodes())
all_nodes.append(node)
if node.var:
if node.var in all_vars:
continue
nodes.extend(node.var.nodes)
all_vars.append(node.var)
return all_nodes, all_vars
@staticmethod
def _do_set_missing_names(nodes_or_vars: Iterable[NV], prefix: str) -> None:
"""Sets the missing names for the given nodes or variables."""
other = [nv.name for nv in nodes_or_vars if nv.name]
counter = -1
for nv in nodes_or_vars:
if not nv.name:
name = f"{prefix}{(counter := counter + 1)}"
while name in other:
name = f"{prefix}{(counter := counter + 1)}"
nv.name = name
other.append(name)
def _set_missing_names(self) -> GraphBuilder:
"""Sets the missing node and variable names."""
nodes, _vars = self._all_nodes_and_vars()
self._do_set_missing_names(_vars, prefix="v")
self._do_set_missing_names(nodes, prefix="n")
return self
[docs] def add(self, *args: Node | Var | GraphBuilder) -> GraphBuilder:
"""Adds nodes, variables or other graph builders to the graph."""
for arg in args:
if isinstance(arg, Node):
self.nodes.add(arg)
elif isinstance(arg, Var):
self.vars.add(arg)
elif isinstance(arg, GraphBuilder):
self.nodes.update(arg.nodes)
self.vars.update(arg.vars)
else:
raise RuntimeError(f"Cannot add {type(arg).__name__} to graph builder")
return self
[docs] def add_group(self, name: str, **kwargs: Node | Var) -> GraphBuilder:
"""
Adds a group to the graph.
Also assigns the nodes and variables to the group,
see :attr:`liesel.model.nodes.Node.groups`.
Parameters
----------
name
The name of the group.
kwargs
The nodes and variables in the group with their keys in the group
as keywords.
"""
for key, arg in kwargs.items():
arg.groups.add((name, key))
self.add(*kwargs.values())
return self
[docs] def build_model(self, copy: bool = False) -> Model:
"""
Builds a model from the graph.
Constructs a model containing all nodes and variables that were added to the
graph builder and their recursive inputs. The outputs of the nodes are not
added to the model automatically, so the root nodes always need to be added
explicitly.
The standard workflow is to create the nodes and variables, add them to a graph
builder, and construct a model from the graph builder. After the model has been
constructed, some methods of the graph builder are not available anymore.
Notes
-----
If this method is called with the argument ``copy=False``, all nodes and
variables are removed from the graph builder, because most methods of the
graph builder do not work with nodes that are part of a model.
Example
-------
>>> a = lsl.Var(1.0, name="a")
>>> b = lsl.Var(2.0, name="b")
>>> c = lsl.Var(lsl.Calc(lambda x, y: x + y, a, b), name="c")
>>> gb = lsl.GraphBuilder()
>>> gb.add(c)
GraphBuilder<0 nodes, 1 vars>
>>> model = gb.build_model()
>>> model
Model<9 nodes, 3 vars>
>>> c.value
3.0
>>> gb.vars
set()
Parameters
----------
copy
Whether the nodes and variables should be copied when building the model.
"""
nodes, _vars = self._all_nodes_and_vars()
if not nodes:
logger.warning("No nodes in graph builder, building an empty model")
for node in nodes:
if node.name.startswith("_model"):
raise RuntimeError(f"{repr(node)} has reserved name '_model*'")
gb = self.copy()
gb._set_missing_names()
gb._add_model_log_lik_node()
gb._add_model_log_prior_node()
gb._add_model_log_prob_node()
gb._add_model_seed_nodes()
nodes, _vars = gb._all_nodes_and_vars()
nodes_and_vars = nodes + _vars
model = Model(nodes_and_vars, grow=False, copy=copy)
if not copy:
self.nodes.clear()
self.vars.clear()
self._log_lik_node = None
self._log_prior_node = None
self._log_prob_node = None
return model
[docs] def copy(self) -> GraphBuilder:
"""Returns a shallow copy of the graph builder."""
gb = GraphBuilder()
gb.nodes = self.nodes.copy()
gb.vars = self.vars.copy()
gb.log_lik_node = self.log_lik_node
gb.log_prior_node = self.log_prior_node
gb.log_prob_node = self.log_prob_node
return gb
[docs] def count_node_names(self) -> dict[str, int]:
"""Counts the number of times each node name occurs in the graph."""
nodes, _ = self._all_nodes_and_vars()
counter = Counter(node.name for node in nodes if node.name)
return dict(counter.most_common())
[docs] def count_var_names(self) -> dict[str, int]:
"""Counts the number of times each variable name occurs in the graph."""
_, _vars = self._all_nodes_and_vars()
counter = Counter(var.name for var in _vars if var.name)
return dict(counter.most_common())
@property
def log_lik_node(self) -> Node | None:
"""The user-defined log-likelihood node."""
return self._log_lik_node
@log_lik_node.setter
def log_lik_node(self, log_lik_node: Node | None):
if log_lik_node and not isinstance(log_lik_node, Node):
raise RuntimeError("The log-likelihood node must be a node, not var")
self._log_lik_node = log_lik_node
@property
def log_prior_node(self) -> Node | None:
"""The user-defined log-prior node."""
return self._log_prior_node
@log_prior_node.setter
def log_prior_node(self, log_prior_node: Node | None):
if log_prior_node and not isinstance(log_prior_node, Node):
raise RuntimeError("The log-prior node must be a node, not var")
self._log_prior_node = log_prior_node
@property
def log_prob_node(self) -> Node | None:
"""The user-defined log-probability node."""
return self._log_prob_node
@log_prob_node.setter
def log_prob_node(self, log_prob_node: Node | None):
if log_prob_node and not isinstance(log_prob_node, Node):
raise RuntimeError("The log-probability node must be a node, not var")
self._log_prob_node = log_prob_node
[docs] def plot_nodes(self) -> GraphBuilder:
"""Plots all nodes in the graph."""
nodes, _vars = self._all_nodes_and_vars()
nodes_and_vars = nodes + _vars
self._set_missing_names()
model = Model(nodes_and_vars, grow=False)
plot_nodes(model)
model.pop_nodes_and_vars()
return self
[docs] def plot_vars(self) -> GraphBuilder:
"""Plots all variables in the graph."""
nodes, _vars = self._all_nodes_and_vars()
nodes_and_vars = nodes + _vars
self._set_missing_names()
model = Model(nodes_and_vars, grow=False)
plot_vars(model)
model.pop_nodes_and_vars()
return self
[docs] def rename(self, pattern: str, replacement: str) -> GraphBuilder:
"""Renames all nodes and variables in the graph."""
self.rename_nodes(pattern, replacement)
self.rename_vars(pattern, replacement)
return self
[docs] def rename_nodes(self, pattern: str, replacement: str) -> GraphBuilder:
"""Renames all nodes in the graph."""
nodes, _ = self._all_nodes_and_vars()
for node in nodes:
if node.name:
node.name = re.sub(pattern, replacement, node.name)
return self
[docs] def rename_vars(self, pattern: str, replacement: str) -> GraphBuilder:
"""Renames all variables in the graph."""
_, _vars = self._all_nodes_and_vars()
for var in _vars:
if var.name:
var.name = re.sub(pattern, replacement, var.name)
return self
[docs] def replace_node(self, old: Node, new: Node) -> GraphBuilder:
"""Replaces the ``old`` with the ``new`` node."""
self.nodes = {new if x is old else x for x in self.nodes}
nodes, _ = self._all_nodes_and_vars()
for node in nodes:
inputs = [new if x is old else x for x in node.inputs]
kwinputs = {k: new if v is old else v for k, v in node.kwinputs.items()}
node.set_inputs(*inputs, **kwinputs)
return self
[docs] def replace_var(self, old: Var, new: Var) -> GraphBuilder:
"""Replaces the ``old`` with the ``new`` variable."""
self.vars = {new if x is old else x for x in self.vars}
self.replace_node(old.var_value_node, new.var_value_node)
self.replace_node(old.value_node, new.value_node)
if old.dist_node:
if not new.dist_node:
raise RuntimeError(
f"Cannot replace {repr(old)} with distribution "
f"with {repr(new)} without distribution"
)
self.replace_node(old.dist_node, new.dist_node)
return self
[docs] def update(self) -> GraphBuilder:
"""Updates all nodes in the graph."""
nodes, _vars = self._all_nodes_and_vars()
nodes_and_vars = nodes + _vars
self._set_missing_names()
model = Model(nodes_and_vars, grow=False)
model.pop_nodes_and_vars()
return self
def __repr__(self) -> str:
brackets = f"<{len(self.nodes)} nodes, {len(self.vars)} vars>"
return type(self).__name__ + brackets
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Model ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
[docs]class Model:
"""
A model with a static graph.
Parameters
----------
nodes_and_vars
The nodes and variables to include in the model.
grow
Whether a :class:`.GraphBuilder` should be used to grow the model (finding \
the recursive inputs of the nodes and variables), and to add the model nodes.
copy
Whether the nodes and variables should be copied upon initialization.
"""
def __init__(
self,
nodes_and_vars: Iterable[Node | Var],
grow: bool = True,
copy: bool = False,
):
if grow:
model = GraphBuilder().add(*nodes_and_vars).build_model()
nodes_and_vars = [*model.nodes.values(), *model.vars.values()]
model.pop_nodes_and_vars()
self._nodes = {nv.name: nv for nv in nodes_and_vars if isinstance(nv, Node)}
self._vars = {nv.name: nv for nv in nodes_and_vars if isinstance(nv, Var)}
if len(self._nodes) < sum(isinstance(nv, Node) for nv in nodes_and_vars):
raise RuntimeError("Model received nodes with duplicate names")
if len(self._vars) < sum(isinstance(nv, Var) for nv in nodes_and_vars):
raise RuntimeError("Model received vars with duplicate names")
if copy:
self._nodes, self._vars = deepcopy((self._nodes, self._vars))
for node in self._nodes.values():
node._clear_outputs()
node._set_model(self)
for node in self._nodes.values():
for _input in node.all_input_nodes():
_input._add_output(node)
self._node_graph = self._build_node_graph(self._nodes.values())
self._var_graph = self._build_var_graph(self._vars.values())
self._sorted_nodes = list(nx.topological_sort(self._node_graph))
self._auto_update = True
self._seed_nodes = []
for node in self._sorted_nodes:
if node.name.startswith("_model_") and node.name.endswith("_seed"):
self._seed_nodes.append(node)
node.update()
@staticmethod
def _build_node_graph(nodes: Iterable[Node]) -> nx.DiGraph:
"""Builds the directed graph of the model nodes."""
edges: list[tuple[Node, Node]] = []
for node in nodes:
edges.extend((_input, node) for _input in node.all_input_nodes())
graph = nx.DiGraph(edges)
graph.add_nodes_from(nodes)
return graph
@staticmethod
def _build_var_graph(_vars: Iterable[Var]) -> nx.DiGraph:
"""Builds the directed graph of the model variables."""
edges: list[tuple[Var, Var]] = []
for var in _vars:
edges.extend((_input, var) for _input in var.all_input_vars())
graph = nx.DiGraph(edges)
graph.add_nodes_from(_vars)
return graph
def _copy_computational_model(self) -> Model:
"""Returns a deep copy of the model with all node states cleared."""
backup = self.state
for node in self._nodes.values():
node.clear_state()
empty = deepcopy(self)
self.state = backup
return empty
def _recursive_inputs(self, name: str) -> list[Node]:
"""Returns the recursive inputs of a model node."""
nodes = [self._nodes[name]]
visited = []
while nodes:
node = nodes.pop()
if node in visited:
continue
nodes.extend(node.all_input_nodes())
visited.append(node)
return visited
@property
def auto_update(self) -> bool:
"""
Whether to update the model automatically if the value of a node is modified.
The auto-update can be disabled to improve the performance if multiple model
parameters are updated at once.
"""
return self._auto_update
@auto_update.setter
def auto_update(self, auto_update: bool):
self._auto_update = auto_update
[docs] def groups(self) -> dict[str, dict[str, Node | Var]]:
"""Composes the groups defined in the model nodes and variables."""
result: dict[str, dict[str, Node | Var]] = {}
for node in self._nodes.values():
for group, key in node.groups:
if group not in result:
result[group] = {}
result[group][key] = node
for var in self._vars.values():
for group, key in var.groups:
if group not in result:
result[group] = {}
result[group][key] = var
return result
[docs] def copy_nodes_and_vars(self) -> tuple[dict[str, Node], dict[str, Var]]:
"""Returns an unfrozen deep copy of the model nodes and variables."""
nodes, _vars = deepcopy((self._nodes, self._vars))
for node in nodes.values():
node._unset_model()
nodes = {nm: nd for nm, nd in nodes.items() if not nm.startswith("_model")}
return nodes, _vars
@property
def log_lik(self) -> Array:
"""
The log-likelihood of the model.
Defined as the sum of the log-probabilities of all observed variables
with a probability distribution.
"""
return self._nodes["_model_log_lik"].value
@property
def log_prior(self) -> Array:
"""
The log-prior of the model.
Defined as the sum of the log-probabilities of all parameter variables
with a probability distribution.
"""
return self._nodes["_model_log_prior"].value
@property
def log_prob(self) -> Array:
"""
The (unnormalized) log-probability / log-posterior of the model.
Defined as the sum of all distribution nodes.
"""
return self._nodes["_model_log_prob"].value
@property
def node_graph(self) -> nx.DiGraph:
"""The directed graph of the model nodes."""
return self._node_graph
@property
def nodes(self) -> MappingProxyType[str, Node]:
"""A mapping of the model nodes with their names as keys."""
return MappingProxyType(self._nodes)
[docs] def pop_nodes_and_vars(self) -> tuple[dict[str, Node], dict[str, Var]]:
"""
Pops the nodes and variables out of this model.
All nodes and variables are unfrozen and their reference to this model
is removed. This model becomes invalid and cannot be used anymore.
"""
nodes = self._nodes.copy()
_vars = self._vars.copy()
for node in nodes.values():
node._unset_model()
nodes = {nm: nd for nm, nd in nodes.items() if not nm.startswith("_model")}
# clear the model
self._nodes.clear()
self._vars.clear()
self._node_graph.clear()
self._var_graph.clear()
self._sorted_nodes.clear()
self._seed_nodes.clear()
return nodes, _vars
[docs] def set_seed(self, seed: jax.random.KeyArray) -> Model:
"""
Splits and sets the seed / PRNG key.
Parameters
----------
seed
The seed is split and distributed to the seed nodes of the model.
Must be a ``KeyArray``, i.e. an array of shape (2,) and dtype ``uint32``.
See :mod:`jax.random` for more details.
"""
seeds = jax.random.split(seed, len(self._seed_nodes))
for node, seed in zip(self._seed_nodes, seeds):
node.value = seed # type: ignore # data node
return self
@property
def state(self) -> dict[str, NodeState]:
"""The state of the model as a dict of node names and states."""
return {name: node.state for name, node in self._nodes.items()}
@state.setter
def state(self, state: dict[str, NodeState]):
for name, node_state in state.items():
self._nodes[name].state = node_state
[docs] def update(self, *names: str) -> Model:
"""
Updates the target nodes and their recursive inputs if they are outdated.
The update is performed in a topological order, restoring a consistent state
of the model. This method is called automatically by the nodes if their value
is modified (unless :attr:`.auto_update` is ``False``).
Parameters
----------
names
The names of the target nodes to be updated.
"""
if not names:
for node in self._sorted_nodes:
if node.outdated:
node.update()
else:
inputs = set().union(*(self._recursive_inputs(name) for name in names))
for node in self._sorted_nodes:
if node in inputs and node.outdated:
node.update()
return self
@property
def var_graph(self) -> nx.DiGraph:
"""The directed graph of the model variables."""
return self._var_graph
@property
def vars(self) -> MappingProxyType[str, Var]:
"""A mapping of the model variables with their names as keys."""
return MappingProxyType(self._vars)
def __repr__(self) -> str:
brackets = f"<{len(self._nodes)} nodes, {len(self._vars)} vars>"
return type(self).__name__ + brackets
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Save and load models ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
[docs]def save_model(model: Any, file: str | IO[bytes]) -> None:
"""
Saves a model to a `dill <https://github.com/uqfoundation/dill>`_ file.
Parameters
----------
model
The model to be saved.
file
The file handler or path to save the model to.
"""
if isinstance(file, str):
with open(file, "wb") as handle:
dill.dump(model, handle)
else:
dill.dump(model, file)
[docs]def load_model(file: str | IO[bytes]) -> Any:
"""
Loads a model from a `dill <https://github.com/uqfoundation/dill>`_ file.
Parameters
----------
file
The file handler or path to load the model from.
"""
if isinstance(file, str):
with open(file, "rb") as handle:
model = dill.load(handle)
else:
model = dill.load(file)
return model
