# ___________________________________________________________________________
#
# Pyomo: Python Optimization Modeling Objects
# Copyright (c) 2008-2024
# National Technology and Engineering Solutions of Sandia, LLC
# Under the terms of Contract DE-NA0003525 with National Technology and
# Engineering Solutions of Sandia, LLC, the U.S. Government retains certain
# rights in this software.
# This software is distributed under the 3-clause BSD License.
# ___________________________________________________________________________
import inspect
import logging
import sys
from copy import deepcopy
from collections import deque
logger = logging.getLogger('pyomo.core')
from pyomo.common.deprecation import deprecated, deprecation_warning
from pyomo.common.errors import DeveloperError, TemplateExpressionError
from pyomo.common.numeric_types import (
nonpyomo_leaf_types,
native_types,
native_numeric_types,
value,
)
import pyomo.core.expr.expr_common as common
from pyomo.core.expr.symbol_map import SymbolMap
try:
# sys._getframe is slightly faster than inspect's currentframe, but
# is not guaranteed to exist everywhere
currentframe = sys._getframe
except AttributeError:
currentframe = inspect.currentframe
def get_stack_depth():
n = -1 # skip *this* frame in the count
f = currentframe()
while f is not None:
n += 1
f = f.f_back
return n
# For efficiency, we want to run recursively, but don't want to hit
# Python's recursion limit (because that would be difficult to recover
# from cleanly). However, there is a non-trivial cost to determine the
# current stack depth - and we don't want to hit that for every call.
# Instead, we will assume that the walker is always called with at
# least RECURSION_LIMIT frames available on the stack. When we hit the
# end of that limit, we will actually check how much space is left on
# the stack and run recursively until only 2*RECURSION_LIMIT frames are
# left. For the vast majority of well-formed expressions this approach
# avoids a somewhat costly call to get_stack_depth, but still catches
# the vast majority of cases that could generate a recursion error.
RECURSION_LIMIT = 50
class RevertToNonrecursive(Exception):
pass
# NOTE: This module also has dependencies on numeric_expr; however, to
# avoid circular dependencies, we will NOT import them here. Instead,
# until we can resolve the circular dependencies, they will be injected
# into this module by the .current module (which must be imported
# *after* numeric_expr, logocal_expr, and this module.
# -------------------------------------------------------
#
# Visitor Logic
#
# -------------------------------------------------------
[docs]class StreamBasedExpressionVisitor(object):
"""This class implements a generic stream-based expression walker.
This visitor walks an expression tree using a depth-first strategy
and generates a full event stream similar to other tree visitors
(e.g., the expat XML parser). The following events are triggered
through callback functions as the traversal enters and leaves nodes
in the tree:
::
initializeWalker(expr) -> walk, result
enterNode(N1) -> args, data
{for N2 in args:}
beforeChild(N1, N2) -> descend, child_result
enterNode(N2) -> N2_args, N2_data
[...]
exitNode(N2, n2_data) -> child_result
acceptChildResult(N1, data, child_result) -> data
afterChild(N1, N2) -> None
exitNode(N1, data) -> N1_result
finalizeWalker(result) -> result
Individual event callbacks match the following signatures:
walk, result = initializeWalker(self, expr):
initializeWalker() is called to set the walker up and perform
any preliminary processing on the root node. The method returns
a flag indicating if the tree should be walked and a result. If
`walk` is True, then result is ignored. If `walk` is False,
then `result` is returned as the final result from the walker,
bypassing all other callbacks (including finalizeResult).
args, data = enterNode(self, node):
enterNode() is called when the walker first enters a node (from
above), and is passed the node being entered. It is expected to
return a tuple of child `args` (as either a tuple or list) and a
user-specified data structure for collecting results. If None
is returned for args, the node's args attribute is used for
expression types and the empty tuple for leaf nodes. Returning
None is equivalent to returning (None,None). If the callback is
not defined, the default behavior is equivalent to returning
(None, []).
node_result = exitNode(self, node, data):
exitNode() is called after the node is completely processed (as
the walker returns up the tree to the parent node). It is
passed the node and the results data structure (defined by
enterNode() and possibly further modified by
acceptChildResult()), and is expected to return the "result" for
this node. If not specified, the default action is to return
the data object from enterNode().
descend, child_result = beforeChild(self, node, child, child_idx):
beforeChild() is called by a node for every child before
entering the child node. The node, child node, and child index
(position in the args list from enterNode()) are passed as
arguments. beforeChild should return a tuple (descend,
child_result). If descend is False, the child node will not be
entered and the value returned to child_result will be passed to
the node's acceptChildResult callback. Returning None is
equivalent to (True, None). The default behavior if not
specified is equivalent to (True, None).
data = acceptChildResult(self, node, data, child_result, child_idx):
acceptChildResult() is called for each child result being
returned to a node. This callback is responsible for recording
the result for later processing or passing up the tree. It is
passed the node, result data structure (see enterNode()), child
result, and the child index (position in args from enterNode()).
The data structure (possibly modified or replaced) must be
returned. If acceptChildResult is not specified, it does
nothing if data is None, otherwise it calls data.append(result).
afterChild(self, node, child, child_idx):
afterChild() is called by a node for every child node
immediately after processing the node is complete before control
moves to the next child or up to the parent node. The node,
child node, an child index (position in args from enterNode())
are passed, and nothing is returned. If afterChild is not
specified, no action takes place.
finalizeResult(self, result):
finalizeResult() is called once after the entire expression tree
has been walked. It is passed the result returned by the root
node exitNode() callback. If finalizeResult is not specified,
the walker returns the result obtained from the exitNode
callback on the root node.
Clients interact with this class by either deriving from it and
implementing the necessary callbacks (see above), assigning callable
functions to an instance of this class, or passing the callback
functions as arguments to this class' constructor.
"""
# The list of event methods that can either be implemented by
# derived classes or specified as callback functions to the class
# constructor.
#
# This is a dict mapping the callback name to a single character
# that we can use to classify the set of callbacks used by a
# particular Visitor (we define special-purpose node processors for
# certain common combinations). For example, a 'bex' visitor is one
# that supports beforeChild, enterNode, and exitNode, but NOT
# afterChild or acceptChildResult.
client_methods = {
'enterNode': 'e',
'exitNode': 'x',
'beforeChild': 'b',
'afterChild': 'a',
'acceptChildResult': 'c',
'initializeWalker': '',
'finalizeResult': '',
}
def __init__(self, **kwds):
# This is slightly tricky: We want derived classes to be able to
# override the "None" defaults here, and for keyword arguments
# to override both. The hasattr check prevents the "None"
# defaults from overriding attributes or methods defined on
# derived classes.
for field in self.client_methods:
if field in kwds:
setattr(self, field, kwds.pop(field))
elif not hasattr(self, field):
setattr(self, field, None)
if kwds:
raise RuntimeError("Unrecognized keyword arguments: %s" % (kwds,))
# Handle deprecated APIs
_fcns = (('beforeChild', 2), ('acceptChildResult', 3), ('afterChild', 2))
for name, nargs in _fcns:
fcn = getattr(self, name)
if fcn is None:
continue
_args = inspect.getfullargspec(fcn)
_self_arg = 1 if inspect.ismethod(fcn) else 0
if len(_args.args) == nargs + _self_arg and _args.varargs is None:
deprecation_warning(
"Note that the API for the StreamBasedExpressionVisitor "
"has changed to include the child index for the %s() "
"method. Please update your walker callbacks." % (name,),
version='5.7.0',
)
def wrap(fcn, nargs):
def wrapper(*args):
return fcn(*args[:nargs])
return wrapper
setattr(self, name, wrap(fcn, nargs))
self.recursion_stack = None
# Set up the custom recursive node handler function (customized
# for the specific set of callbacks that are defined for this
# class instance).
recursive_node_handler = '_process_node_' + ''.join(
sorted(
'' if getattr(self, f[0]) is None else f[1]
for f in self.client_methods.items()
)
)
self._process_node = getattr(
self, recursive_node_handler, self._process_node_general
)
[docs] def walk_expression(self, expr):
"""Walk an expression, calling registered callbacks.
This is the standard interface for running the visitor. It
defaults to using an efficient recursive implementation of the
visitor, falling back on :py:meth:`walk_expression_nonrecursive`
if the recursion stack gets too deep.
"""
if self.initializeWalker is not None:
walk, root = self.initializeWalker(expr)
if not walk:
return root
elif root is None:
root = expr
else:
root = expr
try:
result = self._process_node(root, RECURSION_LIMIT)
_nonrecursive = None
except RevertToNonrecursive:
ptr = (None,) + self.recursion_stack.pop()
while self.recursion_stack:
ptr = (ptr,) + self.recursion_stack.pop()
self.recursion_stack = None
_nonrecursive = self._nonrecursive_walker_loop, ptr
except RecursionError:
logger.warning(
'Unexpected RecursionError walking an expression tree.',
extra={'id': 'W1003'},
)
_nonrecursive = self.walk_expression_nonrecursive, expr
if _nonrecursive is not None:
return _nonrecursive[0](_nonrecursive[1])
if self.finalizeResult is not None:
return self.finalizeResult(result)
else:
return result
def _compute_actual_recursion_limit(self):
recursion_limit = (
sys.getrecursionlimit() - get_stack_depth() - 2 * RECURSION_LIMIT
)
if recursion_limit <= RECURSION_LIMIT:
self.recursion_stack = []
raise RevertToNonrecursive()
return recursion_limit
def _process_node_general(self, node, recursion_limit):
"""Recursive routine for processing nodes with general callbacks
This is the "general" implementation of the
StreamBasedExpressionVisitor node processor that can handle any
combination of registered callback functions.
"""
if not recursion_limit:
recursion_limit = self._compute_actual_recursion_limit()
else:
recursion_limit -= 1
if self.enterNode is not None:
tmp = self.enterNode(node)
if tmp is None:
args = data = None
else:
args, data = tmp
else:
args = None
data = []
if args is None:
if type(node) in nonpyomo_leaf_types or not node.is_expression_type():
args = ()
else:
args = node.args
# Because we do not require the args to be a context manager, we
# will mock up the "with args" using a try-finally.
context_manager = hasattr(args, '__enter__')
if context_manager:
args.__enter__()
try:
descend = True
child_idx = -1
# Note: this relies on iter(iterator) returning the
# iterator. This seems to hold for all common iterators
# (list, tuple, generator, etc)
arg_iter = iter(args)
for child in arg_iter:
child_idx += 1
if self.beforeChild is not None:
tmp = self.beforeChild(node, child, child_idx)
if tmp is None:
descend = True
else:
descend, child_result = tmp
if descend:
child_result = self._process_node(child, recursion_limit)
if self.acceptChildResult is not None:
data = self.acceptChildResult(node, data, child_result, child_idx)
elif data is not None:
data.append(child_result)
if self.afterChild is not None:
self.afterChild(node, child, child_idx)
except RevertToNonrecursive:
self._recursive_frame_to_nonrecursive_stack(locals())
context_manager = False
raise
finally:
if context_manager:
args.__exit__(None, None, None)
# We are done with this node. Call exitNode to compute
# any result
if self.exitNode is not None:
return self.exitNode(node, data)
else:
return data
def _process_node_bex(self, node, recursion_limit):
"""Recursive routine for processing nodes with only 'bex' callbacks
This is a special-case implementation of the "general"
StreamBasedExpressionVisitor node processor for the case that
only beforeChild, enterNode, and exitNode are defined (see
also the definition of the client_methods dict).
"""
if not recursion_limit:
recursion_limit = self._compute_actual_recursion_limit()
else:
recursion_limit -= 1
tmp = self.enterNode(node)
if tmp is None:
args = data = None
else:
args, data = tmp
if args is None:
if type(node) in nonpyomo_leaf_types or not node.is_expression_type():
args = ()
else:
args = node.args
# Because we do not require the args to be a context manager, we
# will mock up the "with args" using a try-finally.
context_manager = hasattr(args, '__enter__')
if context_manager:
args.__enter__()
try:
child_idx = -1
# Note: this relies on iter(iterator) returning the
# iterator. This seems to hold for all common iterators
# (list, tuple, generator, etc)
arg_iter = iter(args)
for child in arg_iter:
child_idx += 1
tmp = self.beforeChild(node, child, child_idx)
if tmp is None:
descend = True
else:
descend, child_result = tmp
if descend:
data.append(self._process_node(child, recursion_limit))
else:
data.append(child_result)
except RevertToNonrecursive:
self._recursive_frame_to_nonrecursive_stack(locals())
context_manager = False
raise
finally:
if context_manager:
args.__exit__(None, None, None)
# We are done with this node. Call exitNode to compute
# any result
return self.exitNode(node, data)
def _process_node_bx(self, node, recursion_limit):
"""Recursive routine for processing nodes with only 'bx' callbacks
This is a special-case implementation of the "general"
StreamBasedExpressionVisitor node processor for the case that
only beforeChild and exitNode are defined (see also the
definition of the client_methods dict).
"""
if not recursion_limit:
recursion_limit = self._compute_actual_recursion_limit()
else:
recursion_limit -= 1
if type(node) in nonpyomo_leaf_types or not node.is_expression_type():
args = ()
else:
args = node.args
data = []
try:
child_idx = -1
# Note: this relies on iter(iterator) returning the
# iterator. This seems to hold for all common iterators
# (list, tuple, generator, etc)
arg_iter = iter(args)
for child in arg_iter:
child_idx += 1
tmp = self.beforeChild(node, child, child_idx)
if tmp is None:
descend = True
else:
descend, child_result = tmp
if descend:
data.append(self._process_node(child, recursion_limit))
else:
data.append(child_result)
except RevertToNonrecursive:
self._recursive_frame_to_nonrecursive_stack(locals())
raise
finally:
pass
# We are done with this node. Call exitNode to compute
# any result
return self.exitNode(node, data)
def _recursive_frame_to_nonrecursive_stack(self, local):
child_idx = local['child_idx']
_arg_list = [None] * child_idx
_arg_list.append(local['child'])
_arg_list.extend(local['arg_iter'])
if not self.recursion_stack:
# For the deepest stack frame, the recursion limit hit
# as we started to enter the child. As we haven't
# started processing it yet, we need to decrement
# child_idx so that it is revisited
child_idx -= 1
self.recursion_stack.append(
(local['node'], _arg_list, len(_arg_list) - 1, local['data'], child_idx)
)
[docs] def walk_expression_nonrecursive(self, expr):
"""Nonrecursively walk an expression, calling registered callbacks.
This routine is safer than the recursive walkers for deep (or
unbalanced) trees. It is, however, slightly slower than the
recursive implementations.
"""
#
# This walker uses a linked list to store the stack (instead of
# an array). The nodes of the linked list are 6-member tuples:
#
# ( pointer to parent,
# expression node,
# tuple/list of child nodes (arguments),
# number of child nodes (arguments),
# data object to aggregate results from child nodes,
# current child node index )
#
# The walker only needs a single pointer to the end of the list
# (ptr). The beginning of the list is indicated by a None
# parent pointer.
#
if self.initializeWalker is not None:
walk, result = self.initializeWalker(expr)
if not walk:
return result
elif result is not None:
expr = result
if self.enterNode is not None:
tmp = self.enterNode(expr)
if tmp is None:
args = data = None
else:
args, data = tmp
else:
args = None
data = []
if args is None:
if type(expr) in nonpyomo_leaf_types or not expr.is_expression_type():
args = ()
else:
args = expr.args
if hasattr(args, '__enter__'):
args.__enter__()
node = expr
# Note that because we increment child_idx just before fetching
# the child node, it must be initialized to -1, and ptr[3] must
# always be *one less than* the number of arguments
return self._nonrecursive_walker_loop(
(None, node, args, len(args) - 1, data, -1)
)
def _nonrecursive_walker_loop(self, ptr):
_, node, args, _, data, child_idx = ptr
try:
while 1:
if child_idx < ptr[3]:
# Increment the child index pointer here for
# consistency. Note that this means that for the bulk
# of the time, 'child_idx' will not match the value of
# ptr[5]. This provides a modest performance
# improvement, as we only have to recreate the ptr tuple
# just before we descend further into the tree (i.e., we
# avoid recreating the tuples for the special case where
# beforeChild indicates that we should not descend
# further).
child_idx += 1
# This node still has children to process
child = ptr[2][child_idx]
# Notify this node that we are about to descend into a
# child.
if self.beforeChild is not None:
tmp = self.beforeChild(node, child, child_idx)
if tmp is None:
descend = True
child_result = None
else:
descend, child_result = tmp
if not descend:
# We are aborting processing of this child node.
# Tell this node to accept the child result and
# we will move along
if self.acceptChildResult is not None:
data = self.acceptChildResult(
node, data, child_result, child_idx
)
elif data is not None:
data.append(child_result)
# And let the node know that we are done with a
# child node
if self.afterChild is not None:
self.afterChild(node, child, child_idx)
# Jump to the top to continue processing the
# next child node
continue
# Update the child argument counter in the stack.
# Because we are using tuples, we need to recreate the
# "ptr" object (linked list node)
ptr = ptr[:4] + (data, child_idx)
# We are now going to actually enter this node. The
# node will tell us the list of its child nodes that we
# need to process
if self.enterNode is not None:
tmp = self.enterNode(child)
if tmp is None:
args = data = None
else:
args, data = tmp
else:
args = None
data = []
if args is None:
if (
type(child) in nonpyomo_leaf_types
or not child.is_expression_type()
):
# Leaves (either non-pyomo types or
# non-Expressions) have no child arguments, so
# are just put on the stack
args = ()
else:
args = child.args
if hasattr(args, '__enter__'):
args.__enter__()
node = child
child_idx = -1
ptr = (ptr, node, args, len(args) - 1, data, child_idx)
else: # child_idx == ptr[3]:
# We are done with this node. Call exitNode to compute
# any result
if hasattr(ptr[2], '__exit__'):
ptr[2].__exit__(None, None, None)
if self.exitNode is not None:
node_result = self.exitNode(node, data)
else:
node_result = data
# Pop the node off the linked list
ptr = ptr[0]
# If we have returned to the beginning, return the final
# answer
if ptr is None:
if self.finalizeResult is not None:
return self.finalizeResult(node_result)
else:
return node_result
# Not done yet, update node to point to the new active
# node
node, child = ptr[1], node
data = ptr[4]
child_idx = ptr[5]
# We need to alert the node to accept the child's result:
if self.acceptChildResult is not None:
data = self.acceptChildResult(
node, data, node_result, child_idx
)
elif data is not None:
data.append(node_result)
# And let the node know that we are done with a child node
if self.afterChild is not None:
self.afterChild(node, child, child_idx)
finally:
while ptr is not None:
if hasattr(ptr[2], '__exit__'):
ptr[2].__exit__(None, None, None)
ptr = ptr[0]
[docs]class SimpleExpressionVisitor(object):
"""
Note:
This class is a customization of the PyUtilib :class:`SimpleVisitor
<pyutilib.misc.visitor.SimpleVisitor>` class that is tailored
to efficiently walk Pyomo expression trees. However, this class
is not a subclass of the PyUtilib :class:`SimpleVisitor
<pyutilib.misc.visitor.SimpleVisitor>` class because all key methods
are reimplemented.
"""
[docs] def visit(self, node): # pragma: no cover
"""
Visit a node in an expression tree and perform some operation on
it.
This method should be over-written by a user
that is creating a sub-class.
Args:
node: a node in an expression tree
Returns:
nothing
"""
pass
[docs] def finalize(self): # pragma: no cover
"""
Return the "final value" of the search.
The default implementation returns :const:`None`, because
the traditional visitor pattern does not return a value.
Returns:
The final value after the search. Default is :const:`None`.
"""
pass
[docs] def xbfs(self, node):
"""
Breadth-first search of an expression tree,
except that leaf nodes are immediately visited.
Note:
This method has the same functionality as the
PyUtilib :class:`SimpleVisitor.xbfs <pyutilib.misc.visitor.SimpleVisitor.xbfs>`
method. The difference is that this method
is tailored to efficiently walk Pyomo expression trees.
Args:
node: The root node of the expression tree that is searched.
Returns:
The return value is determined by the :func:`finalize` function,
which may be defined by the user. Defaults to :const:`None`.
"""
dq = deque([node])
while dq:
current = dq.popleft()
self.visit(current)
# for c in self.children(current):
for c in current.args:
# if self.is_leaf(c):
if (
c.__class__ in nonpyomo_leaf_types
or not c.is_expression_type()
or c.nargs() == 0
):
self.visit(c)
else:
dq.append(c)
return self.finalize()
[docs] def xbfs_yield_leaves(self, node):
"""
Breadth-first search of an expression tree, except that
leaf nodes are immediately visited.
Note:
This method has the same functionality as the
PyUtilib :class:`SimpleVisitor.xbfs_yield_leaves <pyutilib.misc.visitor.SimpleVisitor.xbfs_yield_leaves>`
method. The difference is that this method
is tailored to efficiently walk Pyomo expression trees.
Args:
node: The root node of the expression tree
that is searched.
Returns:
The return value is determined by the :func:`finalize` function,
which may be defined by the user. Defaults to :const:`None`.
"""
#
# If we start with a leaf, then yield it and stop iteration
#
if (
node.__class__ in nonpyomo_leaf_types
or not node.is_expression_type()
or node.nargs() == 0
):
ans = self.visit(node)
if not ans is None:
yield ans
return
#
# Iterate through the tree.
#
dq = deque([node])
while dq:
current = dq.popleft()
# self.visit(current)
# for c in self.children(current):
for c in current.args:
# if self.is_leaf(c):
if (
c.__class__ in nonpyomo_leaf_types
or not c.is_expression_type()
or c.nargs() == 0
):
ans = self.visit(c)
if not ans is None:
yield ans
else:
dq.append(c)
[docs]class ExpressionValueVisitor(object):
"""
Note:
This class is a customization of the PyUtilib :class:`ValueVisitor
<pyutilib.misc.visitor.ValueVisitor>` class that is tailored
to efficiently walk Pyomo expression trees. However, this class
is not a subclass of the PyUtilib :class:`ValueVisitor
<pyutilib.misc.visitor.ValueVisitor>` class because all key methods
are reimplemented.
"""
[docs] def visit(self, node, values): # pragma: no cover
"""
Visit a node in a tree and compute its value using
the values of its children.
This method should be over-written by a user
that is creating a sub-class.
Args:
node: a node in a tree
values: a list of values of this node's children
Returns:
The *value* for this node, which is computed using :attr:`values`
"""
pass
[docs] def visiting_potential_leaf(self, node): # pragma: no cover
"""
Visit a node and return its value if it is a leaf.
Note:
This method needs to be over-written for a specific
visitor application.
Args:
node: a node in a tree
Returns:
A tuple: ``(flag, value)``. If ``flag`` is False,
then the node is not a leaf and ``value`` is :const:`None`.
Otherwise, ``value`` is the computed value for this node.
"""
raise RuntimeError("The visiting_potential_leaf method needs to be defined.")
[docs] def finalize(self, ans): # pragma: no cover
"""
This method defines the return value for the search methods
in this class.
The default implementation returns the value of the
initial node (aka the root node), because
this visitor pattern computes and returns value for each
node to enable the computation of this value.
Args:
ans: The final value computed by the search method.
Returns:
The final value after the search. Defaults to simply
returning :attr:`ans`.
"""
return ans
[docs] def dfs_postorder_stack(self, node):
"""
Perform a depth-first search in postorder using a stack
implementation.
Note:
This method has the same functionality as the
PyUtilib :class:`ValueVisitor.dfs_postorder_stack <pyutilib.misc.visitor.ValueVisitor.dfs_postorder_stack>`
method. The difference is that this method
is tailored to efficiently walk Pyomo expression trees.
Args:
node: The root node of the expression tree
that is searched.
Returns:
The return value is determined by the :func:`finalize` function,
which may be defined by the user.
"""
flag, value = self.visiting_potential_leaf(node)
if flag:
return self.finalize(value)
# _stack = [ (node, self.children(node), 0, len(self.children(node)), [])]
_stack = [(node, node._args_, 0, node.nargs(), [])]
#
# Iterate until the stack is empty
#
# Note: 1 is faster than True for Python 2.x
#
while 1:
#
# Get the top of the stack
# _obj Current expression object
# _argList The arguments for this expression object
# _idx The current argument being considered
# _len The number of arguments
# _result The return values
#
_obj, _argList, _idx, _len, _result = _stack.pop()
#
# Iterate through the arguments
#
while _idx < _len:
_sub = _argList[_idx]
_idx += 1
flag, value = self.visiting_potential_leaf(_sub)
if flag:
_result.append(value)
else:
#
# Push an expression onto the stack
#
_stack.append((_obj, _argList, _idx, _len, _result))
_obj = _sub
# _argList = self.children(_sub)
_argList = _sub._args_
_idx = 0
_len = _sub.nargs()
_result = []
#
# Process the current node
#
ans = self.visit(_obj, _result)
if _stack:
#
# "return" the recursion by putting the return value on the end of the results stack
#
_stack[-1][-1].append(ans)
else:
return self.finalize(ans)
def replace_expressions(
expr,
substitution_map,
descend_into_named_expressions=True,
remove_named_expressions=True,
):
"""
Parameters
----------
expr : Pyomo expression
The source expression
substitution_map : dict
A dictionary mapping object ids in the source to the replacement objects.
descend_into_named_expressions : bool
True if replacement should go into named expression objects, False to halt at
a named expression
remove_named_expressions : bool
True if the named expressions should be replaced with a standard expression,
and False if the named expression should be left in place
Returns
-------
Pyomo expression : returns the new expression object
"""
return ExpressionReplacementVisitor(
substitute=substitution_map,
descend_into_named_expressions=descend_into_named_expressions,
remove_named_expressions=remove_named_expressions,
).walk_expression(expr)
[docs]class ExpressionReplacementVisitor(StreamBasedExpressionVisitor):
def __init__(
self,
substitute=None,
descend_into_named_expressions=True,
remove_named_expressions=True,
):
if substitute is None:
substitute = {}
# Note: preserving the attribute names from the previous
# implementation of the expression walker.
self.substitute = substitute
self.enter_named_expr = descend_into_named_expressions
self.rm_named_expr = remove_named_expressions
kwds = {}
if hasattr(self, 'visiting_potential_leaf'):
deprecation_warning(
"ExpressionReplacementVisitor: this walker has been ported "
"to derive from StreamBasedExpressionVisitor. "
"visiting_potential_leaf() has been replaced by beforeChild()"
"(note to implementers: the sense of the bool return value "
"has been inverted).",
version='6.2',
)
def beforeChild(node, child, child_idx):
is_leaf, ans = self.visiting_potential_leaf(child)
return not is_leaf, ans
kwds['beforeChild'] = beforeChild
if hasattr(self, 'visit'):
raise DeveloperError(
"ExpressionReplacementVisitor: this walker has been ported "
"to derive from StreamBasedExpressionVisitor. "
"overriding visit() has no effect (and is likely to generate "
"invalid expression trees)"
)
super().__init__(**kwds)
def initializeWalker(self, expr):
walk, result = self.beforeChild(None, expr, 0)
if not walk:
return False, result
return True, expr
def beforeChild(self, node, child, child_idx):
if id(child) in self.substitute:
return False, self.substitute[id(child)]
elif type(child) in native_types:
return False, child
elif not child.is_expression_type():
return False, child
elif child.is_named_expression_type():
if not self.enter_named_expr:
return False, child
return True, None
def enterNode(self, node):
args = list(node.args)
# [bool:args_have_changed, list:original_args, bool:node_is_constant]
return args, [False, args, True]
def acceptChildResult(self, node, data, child_result, child_idx):
if data[1][child_idx] is not child_result:
data[1][child_idx] = child_result
data[0] = True
if (
child_result.__class__ not in native_types
and not child_result.is_constant()
):
data[2] = False
return data
def exitNode(self, node, data):
if node.is_named_expression_type():
assert len(data[1]) == 1
if self.rm_named_expr:
return data[1][0]
elif data[0]:
node.set_value(data[1][0])
return node
elif data[0]:
if data[2]:
return node._apply_operation(data[1])
else:
return node.create_node_with_local_data(data[1])
return node
[docs] @deprecated(
"ExpressionReplacementVisitor: this walker has been ported "
"to derive from StreamBasedExpressionVisitor. "
"dfs_postorder_stack() has been replaced with walk_expression()",
version='6.2',
)
def dfs_postorder_stack(self, expr):
return self.walk_expression(expr)
def evaluate_fixed_subexpressions(
expr, descend_into_named_expressions=True, remove_named_expressions=True
):
return EvaluateFixedSubexpressionVisitor(
descend_into_named_expressions=descend_into_named_expressions,
remove_named_expressions=remove_named_expressions,
).walk_expression(expr)
class EvaluateFixedSubexpressionVisitor(ExpressionReplacementVisitor):
def __init__(
self, descend_into_named_expressions=False, remove_named_expressions=False
):
super().__init__(
descend_into_named_expressions=descend_into_named_expressions,
remove_named_expressions=remove_named_expressions,
)
def beforeChild(self, node, child, child_idx):
if type(child) in native_types:
return False, child
elif not child.is_expression_type():
if child.is_fixed():
return False, child()
else:
return False, child
elif child.is_named_expression_type():
if not self.enter_named_expr:
return False, child
return True, None
# -------------------------------------------------------
#
# Functions used to process expression trees
#
# -------------------------------------------------------
# =====================================================
# clone_expression
# =====================================================
[docs]def clone_expression(expr, substitute=None):
"""A function that is used to clone an expression.
Cloning is equivalent to calling ``copy.deepcopy`` with no Block
scope. That is, the expression tree is duplicated, but no Pyomo
components (leaf nodes *or* named Expressions) are duplicated.
Args:
expr: The expression that will be cloned.
substitute (dict): A dictionary mapping object ids to
objects. This dictionary has the same semantics as
the memo object used with ``copy.deepcopy``. Defaults
to None, which indicates that no user-defined
dictionary is used.
Returns:
The cloned expression.
"""
common.clone_counter._count += 1
memo = {'__block_scope__': {id(None): False}}
if substitute:
expr = replace_expressions(expr, substitute)
return deepcopy(expr, memo)
# =====================================================
# sizeof_expression
# =====================================================
def sizeof_expression(expr):
"""
Return the number of nodes in the expression tree.
Args:
expr: The root node of an expression tree.
Returns:
A non-negative integer that is the number of
interior and leaf nodes in the expression tree.
"""
def enter(node):
return None, 1
def accept(node, data, child_result, child_idx):
return data + child_result
return StreamBasedExpressionVisitor(
enterNode=enter, acceptChildResult=accept
).walk_expression(expr)
# =====================================================
# evaluate_expression
# =====================================================
class _EvaluationVisitor(ExpressionValueVisitor):
def __init__(self, exception):
self.exception = exception
def visit(self, node, values):
"""Visit nodes that have been expanded"""
return node._apply_operation(values)
def visiting_potential_leaf(self, node):
"""
Visiting a potential leaf.
Return True if the node is not expanded.
"""
if node.__class__ in nonpyomo_leaf_types:
return True, node
if node.is_expression_type():
return False, None
if node.is_numeric_type():
return True, value(node, exception=self.exception)
elif node.is_logical_type():
return True, value(node, exception=self.exception)
else:
return True, node
class FixedExpressionError(Exception):
def __init__(self, *args, **kwds):
super(FixedExpressionError, self).__init__(*args, **kwds)
class NonConstantExpressionError(Exception):
def __init__(self, *args, **kwds):
super(NonConstantExpressionError, self).__init__(*args, **kwds)
class _EvaluateConstantExpressionVisitor(ExpressionValueVisitor):
def visit(self, node, values):
"""Visit nodes that have been expanded"""
return node._apply_operation(values)
def visiting_potential_leaf(self, node):
"""
Visiting a potential leaf.
Return True if the node is not expanded.
"""
if node.__class__ in nonpyomo_leaf_types:
return True, node
if node.is_expression_type():
return False, None
if node.is_numeric_type():
# Get the object value. This will also cause templates to
# raise TemplateExpressionErrors
try:
val = value(node)
except TemplateExpressionError:
raise
except:
# Uninitialized Var/Param objects should be given the
# opportunity to map the error to a NonConstant / Fixed
# expression error
if not node.is_fixed():
raise NonConstantExpressionError()
if not node.is_constant():
raise FixedExpressionError()
raise
if not node.is_fixed():
raise NonConstantExpressionError()
if not node.is_constant():
raise FixedExpressionError()
return True, val
return True, node
[docs]def evaluate_expression(exp, exception=True, constant=False):
"""Evaluate the value of the expression.
Args:
expr: The root node of an expression tree.
exception (bool): A flag that indicates whether
exceptions are raised. If this flag is
:const:`False`, then an exception that
occurs while evaluating the expression
is caught and the return value is :const:`None`.
Default is :const:`True`.
constant (bool): If True, constant expressions are
evaluated and returned but nonconstant expressions
raise either FixedExpressionError or
NonconstantExpressionError (default=False).
Returns:
A floating point value if the expression evaluates
normally, or :const:`None` if an exception occurs
and is caught.
"""
clear_active = False
if constant:
visitor = _EvaluateConstantExpressionVisitor()
else:
if evaluate_expression.visitor_active:
visitor = _EvaluationVisitor(exception=exception)
else:
visitor = evaluate_expression.visitor_cache
visitor.exception = exception
evaluate_expression.visitor_active = True
clear_active = True
try:
ans = visitor.dfs_postorder_stack(exp)
except (
TemplateExpressionError,
ValueError,
TypeError,
NonConstantExpressionError,
FixedExpressionError,
):
# Errors that we want to be able to suppress:
#
# TemplateExpressionError: raised when generating expression
# templates
# FixedExpressionError, NonConstantExpressionError: raised
# when processing expressions that are expected to be fixed
# (e.g., indices)
# ValueError: "standard" expression value errors
# TypeError: This can be raised in Python3 when evaluating a
# operation returns a complex number (e.g., sqrt(-1))
if exception:
raise
return None
finally:
if clear_active:
evaluate_expression.visitor_active = False
if ans.__class__ not in native_types and ans.is_numeric_type() is True:
return value(ans)
return ans
evaluate_expression.visitor_cache = _EvaluationVisitor(True)
evaluate_expression.visitor_active = False
# =====================================================
# identify_components
# =====================================================
class _ComponentVisitor(SimpleExpressionVisitor):
def __init__(self, types):
self.seen = set()
if types.__class__ is set:
self.types = types
else:
self.types = set(types)
def visit(self, node):
if node.__class__ in self.types:
if id(node) in self.seen:
return
self.seen.add(id(node))
return node
[docs]def identify_components(expr, component_types):
"""
A generator that yields a sequence of nodes
in an expression tree that belong to a specified set.
Args:
expr: The root node of an expression tree.
component_types (set or list): A set of class
types that will be matched during the search.
Yields:
Each node that is found.
"""
#
# OPTIONS:
# component_types - set (or list) if class types to find
# in the expression.
#
visitor = _ComponentVisitor(component_types)
yield from visitor.xbfs_yield_leaves(expr)
# =====================================================
# identify_variables
# =====================================================
class _VariableVisitor(SimpleExpressionVisitor):
def __init__(self):
self.seen = set()
def visit(self, node):
if node.__class__ in nonpyomo_leaf_types:
return
if node.is_variable_type():
if id(node) in self.seen:
return
self.seen.add(id(node))
return node
[docs]def identify_variables(expr, include_fixed=True):
"""
A generator that yields a sequence of variables
in an expression tree.
Args:
expr: The root node of an expression tree.
include_fixed (bool): If :const:`True`, then
this generator will yield variables whose
value is fixed. Defaults to :const:`True`.
Yields:
Each variable that is found.
"""
visitor = _VariableVisitor()
if include_fixed:
for v in visitor.xbfs_yield_leaves(expr):
if isinstance(v, tuple):
yield from v
else:
yield v
else:
for v in visitor.xbfs_yield_leaves(expr):
if isinstance(v, tuple):
for v_i in v:
if not v_i.is_fixed():
yield v_i
else:
if not v.is_fixed():
yield v
# =====================================================
# identify_mutable_parameters
# =====================================================
class _MutableParamVisitor(SimpleExpressionVisitor):
def __init__(self):
self.seen = set()
def visit(self, node):
if node.__class__ in nonpyomo_leaf_types:
return
# TODO: Confirm that this has the right semantics
if not node.is_variable_type() and node.is_fixed() and not node.is_constant():
if id(node) in self.seen:
return
self.seen.add(id(node))
return node
def identify_mutable_parameters(expr):
"""
A generator that yields a sequence of mutable
parameters in an expression tree.
Args:
expr: The root node of an expression tree.
Yields:
Each mutable parameter that is found.
"""
visitor = _MutableParamVisitor()
yield from visitor.xbfs_yield_leaves(expr)
# =====================================================
# polynomial_degree
# =====================================================
class _PolynomialDegreeVisitor(ExpressionValueVisitor):
def visit(self, node, values):
"""Visit nodes that have been expanded"""
return node._compute_polynomial_degree(values)
def visiting_potential_leaf(self, node):
"""
Visiting a potential leaf.
Return True if the node is not expanded.
"""
if node.__class__ in nonpyomo_leaf_types:
return True, 0
if node.is_expression_type():
return False, None
if node.is_numeric_type():
return True, 0 if node.is_fixed() else 1
else:
return True, node
def polynomial_degree(node):
"""
Return the polynomial degree of the expression.
Args:
node: The root node of an expression tree.
Returns:
A non-negative integer that is the polynomial
degree if the expression is polynomial, or :const:`None` otherwise.
"""
visitor = _PolynomialDegreeVisitor()
return visitor.dfs_postorder_stack(node)
# =====================================================
# _expression_is_fixed
# =====================================================
class _IsFixedVisitor(ExpressionValueVisitor):
"""
NOTE: This doesn't check if combiner logic is
all or any and short-circuit the test. It's
not clear that that is an important optimization.
"""
def visit(self, node, values):
"""Visit nodes that have been expanded"""
return node._is_fixed(values)
def visiting_potential_leaf(self, node):
"""
Visiting a potential leaf.
Return True if the node is not expanded.
"""
if node.__class__ in nonpyomo_leaf_types:
return True, True
elif node.is_expression_type():
return False, None
elif node.is_numeric_type():
return True, node.is_fixed()
return True, node
def _expression_is_fixed(node):
"""Return bool indicating if this expression is fixed (non-variable)
Args:
node: The root node of an expression tree.
Returns: bool
"""
visitor = _IsFixedVisitor()
return visitor.dfs_postorder_stack(node)
# =====================================================
# expression_to_string
# =====================================================
LEFT_TO_RIGHT = common.OperatorAssociativity.LEFT_TO_RIGHT
RIGHT_TO_LEFT = common.OperatorAssociativity.RIGHT_TO_LEFT
class _ToStringVisitor(ExpressionValueVisitor):
_expression_handlers = None
def __init__(self, verbose, smap):
super(_ToStringVisitor, self).__init__()
self.verbose = verbose
self.smap = smap
def visit(self, node, values):
"""Visit nodes that have been expanded"""
for i, val in enumerate(values):
arg = node._args_[i]
if arg is None:
values[i] = 'Undefined'
elif arg.__class__ in native_numeric_types:
pass
elif arg.__class__ in nonpyomo_leaf_types:
values[i] = f"'{val}'"
else:
parens = False
if (
not self.verbose
and arg.is_expression_type()
and node.PRECEDENCE is not None
):
if arg.PRECEDENCE is None:
pass
elif node.PRECEDENCE < arg.PRECEDENCE:
parens = True
elif node.PRECEDENCE == arg.PRECEDENCE:
if i == 0:
parens = node.ASSOCIATIVITY != LEFT_TO_RIGHT
elif i == len(node._args_) - 1:
parens = node.ASSOCIATIVITY != RIGHT_TO_LEFT
else:
parens = True
if parens:
values[i] = f"({val})"
if self._expression_handlers and node.__class__ in self._expression_handlers:
return self._expression_handlers[node.__class__](self, node, values)
return node._to_string(values, self.verbose, self.smap)
def visiting_potential_leaf(self, node):
"""
Visiting a potential leaf.
Return True if the node is not expanded.
"""
if node is None:
return True, None
if node.__class__ in nonpyomo_leaf_types:
return True, str(node)
if node.is_expression_type():
return False, None
if hasattr(node, 'to_string'):
return True, node.to_string(verbose=self.verbose, smap=self.smap)
else:
return True, str(node)
[docs]def expression_to_string(
expr, verbose=None, labeler=None, smap=None, compute_values=False
):
"""Return a string representation of an expression.
Parameters
----------
expr: ExpressionBase
The root node of an expression tree.
verbose: bool
If :const:`True`, then the output is a nested functional form.
Otherwise, the output is an algebraic expression. Default is
retrieved from :py:attr:`common.TO_STRING_VERBOSE`
labeler: Callable
If specified, this labeler is used to generate the string
representation for leaves (Var / Param objects) in the
expression.
smap: SymbolMap
If specified, this :class:`SymbolMap
<pyomo.core.expr.symbol_map.SymbolMap>` is used to cache labels.
compute_values: bool
If :const:`True`, then parameters and fixed variables are
evaluated before the expression string is generated. Default is
:const:`False`.
Returns:
A string representation for the expression.
"""
verbose = common.TO_STRING_VERBOSE if verbose is None else verbose
#
# Setup the symbol map
#
if labeler is not None:
if smap is None:
smap = SymbolMap()
smap.default_labeler = labeler
#
# TODO: should we deprecate the compute_values option?
#
if compute_values:
expr = evaluate_fixed_subexpressions(expr)
#
# Create and execute the visitor pattern
#
visitor = _ToStringVisitor(verbose, smap)
return visitor.dfs_postorder_stack(expr)