def __init__(self, formula, mrf, parent=None, assignment=None):
"""
Instantiates the formula grounding for a given
- formula: the formula grounded in this node
- mrf: the MRF associated to this problem
- parent: the formula grounding this fg has been created from
- assignment: dictionary mapping variables to their values
"""
self.mrf = mrf
self.formula = formula
self.parent = Ref(parent)
self.trueGroundings = Number(0.)
self.processed = Boolean(False)
if parent is None:
self.depth = 0
else:
self.depth = parent.depth + 1
self.children = List()
self.assignment = assignment
self.domains = ListDict()
if parent is None:
for var in self.formula.getVariables(self.mrf.mln):
self.domains.extend(
var,
list(self.mrf.domains[self.formula.getVarDomain(
var, self.mrf.mln)]))
else:
for (v, d) in parent.domains.iteritems():
self.domains.extend(v, list(d))
self.domains.epochEndsHere()
def __init__(self, formula, mrf, parent=None, assignment=None):
'''
Instantiates the formula grounding for a given
- formula: the formula grounded in this node
- mrf: the MRF associated to this problem
- parent: the formula grounding this fg has been created from
- assignment: dictionary mapping variables to their values
'''
self.mrf = mrf
self.formula = formula
self.parent = Ref(parent)
self.trueGroundings = Number(0.)
self.processed = Boolean(False)
if parent is None:
self.depth = 0
else:
self.depth = parent.depth + 1
self.children = List()
self.assignment = assignment
self.domains = ListDict()
if parent is None:
for var in self.formula.getVariables(self.mrf.mln):
self.domains.extend(var, list(self.mrf.domains[self.formula.getVarDomain(var, self.mrf.mln)]))
else:
for (v, d) in parent.domains.iteritems():
self.domains.extend(v, list(d))
self.domains.epochEndsHere()
def __init__(self, formula, mrf):
'''
formula might be a formula or a FormulaGrounding instance.
'''
self.mrf = mrf
self.costs = .0
if isinstance(formula, Logic.Formula):
self.formula = formula
self.root = FormulaGrounding(formula, mrf)
elif isinstance(formula, FormulaGrounding):
self.root = formula
self.formula = formula.formula
self.values_processed = ListDict()
self.variable_stack = List(None)
self.var2fgs = ListDict({None: [self.root]})
self.gndAtom2fgs = ListDict()
self.manipulatedFgs = List()
def __init__(self, formula, mrf):
"""
formula might be a formula or a FormulaGrounding instance.
"""
self.mrf = mrf
self.costs = .0
if isinstance(formula, Logic.Formula):
self.formula = formula
self.root = FormulaGrounding(formula, mrf)
elif isinstance(formula, FormulaGrounding):
self.root = formula
self.formula = formula.formula
self.values_processed = ListDict()
self.variable_stack = List(None)
self.var2fgs = ListDict({None: [self.root]})
self.gndAtom2fgs = ListDict()
self.manipulatedFgs = List()
class SmartGroundingFactory(object):
"""
Implements a factory for generating the groundings of one formula.
The groundings are created incrementally with one
particular ground atom being presented at a time.
fields:
- formula: the (ungrounded) formula representing the root of the
search tree
- mrf: the respective MRF
- root: a FormulaGrounding instance representing the root of the
tree, i.e. an ungrounded formula
- costs: the costs accumulated so far
- depth2fgs: mapping from a depth of the search tree to the
corresponding list of FormulaGroundings
- vars_processed: list of variable names that have already been
processed so far
- values_processed: mapping from a variable name to the list of values
of that vaiable that
have already been assigned so far.
This class maintains a stack of all its fields in order allow undoing
groundings that have been performed once.
"""
def __init__(self, formula, mrf):
"""
formula might be a formula or a FormulaGrounding instance.
"""
self.mrf = mrf
self.costs = .0
if isinstance(formula, Logic.Formula):
self.formula = formula
self.root = FormulaGrounding(formula, mrf)
elif isinstance(formula, FormulaGrounding):
self.root = formula
self.formula = formula.formula
self.values_processed = ListDict()
self.variable_stack = List(None)
self.var2fgs = ListDict({None: [self.root]})
self.gndAtom2fgs = ListDict()
self.manipulatedFgs = List()
def epochEndsHere(self):
for mem in (self.values_processed, self.variable_stack, self.var2fgs,
self.gndAtom2fgs, self.manipulatedFgs):
mem.epochEndsHere()
for fg in self.manipulatedFgs:
fg.epochEndsHere()
def undoEpoch(self):
for fg in self.manipulatedFgs:
fg.undoEpoch()
for mem in (self.values_processed, self.variable_stack, self.var2fgs,
self.gndAtom2fgs, self.manipulatedFgs):
mem.undoEpoch()
def ground(self, gndAtom):
"""
Expects a ground atom and creates all groundings
that can be derived by it in terms of FormulaGroundings.
"""
self.manipulatedFgs.clear()
# get all variable assignments of matching literals in the formula
var_assignments = {}
for lit in self.formula.iterLiterals():
assignment = self.gndAtom2Assignment(lit, gndAtom)
if assignment is not None:
unifyDicts(var_assignments, assignment)
cost = .0
# first evaluate formula groundings that contain
# this gnd atom as an artifact
min_depth = None
min_depth_fgs = []
for fg in self.gndAtom2fgs.get(gndAtom, []):
if len(self.variable_stack) <= fg.depth:
continue
if fg.processed.value:
continue
truth = fg.formula.isTrue(self.mrf.evidence)
if truth is not None:
cost -= fg.trueGroundings.value
if not fg in self.manipulatedFgs:
self.manipulatedFgs.append(fg)
fg.processed.set(True)
self.var2fgs.drop(self.variable_stack[fg.depth], fg)
if not fg.parent.obj in self.manipulatedFgs:
self.manipulatedFgs.append(fg.parent.obj)
fg.parent.obj.children.remove(
fg
) # this is just for the visualization/ no real functionality
if fg.depth == min_depth or min_depth is None:
min_depth_fgs.append(fg)
min_depth = fg.depth
if fg.depth < min_depth:
min_depth = fg.depth
min_depth_fgs = []
min_depth_fgs.append(fg)
for fg in min_depth_fgs:
# add the costs which are aggregated by the root of the subtree
if fg.formula.isTrue(fg.mrf.evidence) == False:
cost += fg.formula.weight * fg.countGroundings()
fg.trueGroundings.set(cost)
# straighten up the variable stack and formula groundings
# since they might have become empty
for var in list(self.variable_stack):
if self.var2fgs[var] is None:
self.variable_stack.remove(var)
for var, value in var_assignments.iteritems():
# skip the variables with values that have already been processed
if not var in self.variable_stack:
depth = len(self.variable_stack)
else:
depth = self.variable_stack.index(var)
queue = list(self.var2fgs[self.variable_stack[depth - 1]])
while len(queue) > 0:
fg = queue.pop()
# first hinge the new variable grounding to all possible
# parents, i.e. all FormulaGroundings with depth - 1...
if fg.depth < depth:
vars_and_values = [{var: value}]
# ...then hinge all previously seen subtrees to the newly
# created formula groundings...
elif fg.depth >= depth and fg.depth < len(
self.variable_stack) - 1:
vars_and_values = [{
self.variable_stack[fg.depth + 1]: v
} for v in self.values_processed[self.variable_stack[
fg.depth + 1]]]
# ...and finally all variable values that are not part of
# the subtrees i.e. variables that are currently NOT in the
# variable_stack (since they have been removed due to falsity
# of a formula grounding).
else:
vars_and_values = []
varNotInTree = None
for varNotInTree in [
v for v in self.values_processed.keys()
if v not in self.variable_stack
]:
break
if varNotInTree is None: continue
values = self.values_processed[varNotInTree]
for v in values:
vars_and_values.append({varNotInTree: v})
for var_value in vars_and_values:
for var_name, val in var_value.iteritems():
break
if not fg.domains.contains(var_name, val): continue
gnd_result = fg.ground(var_value)
if not fg in self.manipulatedFgs:
self.manipulatedFgs.append(fg)
# if the truth value of a grounding cannot be determined...
if isinstance(gnd_result, FormulaGrounding):
# collect all ground atoms that have been created as
# as artifacts for future evaluation
artifactGndAtoms = [
a for a in gnd_result.formula.getGroundAtoms()
if not a == gndAtom
]
for artGndAtom in artifactGndAtoms:
self.gndAtom2fgs.put(artGndAtom, gnd_result)
if not var_name in self.variable_stack:
self.variable_stack.append(var_name)
self.var2fgs.put(self.variable_stack[gnd_result.depth],
gnd_result)
queue.append(gnd_result)
else:
# ...otherwise it's true/false; add its costs and
# discard it.
if self.formula.isHard and gnd_result > 0.:
gnd_result = float('inf')
cost += gnd_result
self.values_processed.put(var, value)
return cost
def printTree(self):
queue = [self.root]
print '---'
while len(queue) > 0:
n = queue.pop()
space = ''
for _ in range(n.depth):
space += '--'
print space + str(n)
queue.extend(n.children.list)
print '---'
def gndAtom2Assignment(self, lit, atom):
"""
Returns None if the literal and the atom do not match.
"""
if type(lit) is Logic.Equality or \
lit.predName != atom.predName:
return None
assignment = {}
for p1, p2 in zip(lit.params, atom.params):
if self.mrf.mln.logic.isVar(p1):
assignment[p1] = p2
elif p1 != p2:
return None
return assignment
class FormulaGrounding(object):
"""
Represents a particular (partial) grounding of a formula with respect to
_one_ predicate and in terms of disjoint sets of variables occurring in
that formula. A grounding of the formula is represented as a list of
assignments of the independent variable sets.
It represents a node in the search tree for weighted SAT solving.
Additional fields:
- depth: the depth of this formula grounding (node) in the search tree
The root node (the formula with no grounded variable has
depth 0.
- children: list of formula groundings that have been generated from this
fg.
"""
def __init__(self, formula, mrf, parent=None, assignment=None):
"""
Instantiates the formula grounding for a given
- formula: the formula grounded in this node
- mrf: the MRF associated to this problem
- parent: the formula grounding this fg has been created from
- assignment: dictionary mapping variables to their values
"""
self.mrf = mrf
self.formula = formula
self.parent = Ref(parent)
self.trueGroundings = Number(0.)
self.processed = Boolean(False)
if parent is None:
self.depth = 0
else:
self.depth = parent.depth + 1
self.children = List()
self.assignment = assignment
self.domains = ListDict()
if parent is None:
for var in self.formula.getVariables(self.mrf.mln):
self.domains.extend(
var,
list(self.mrf.domains[self.formula.getVarDomain(
var, self.mrf.mln)]))
else:
for (v, d) in parent.domains.iteritems():
self.domains.extend(v, list(d))
self.domains.epochEndsHere()
def epochEndsHere(self):
for mem in (self.parent, self.trueGroundings, self.children,
self.domains, self.processed):
mem.epochEndsHere()
def undoEpoch(self):
for mem in (self.parent, self.trueGroundings, self.children,
self.domains, self.processed):
mem.undoEpoch()
def countGroundings(self):
"""
Computes the number of ground formulas subsumed by this
FormulaGrounding based on the domain sizes of the free (unbound)
variables.
"""
gf_count = 1
for var in self.formula.getVariables(self.mrf):
domain = self.mrf.domains[self.formula.getVarDomain(var, self.mrf)]
gf_count *= len(domain)
return gf_count
def ground(self, assignment=None):
"""
Takes an assignment of _one_ particular variable and
returns a new FormulaGrounding with that assignment. If
the assignment renders the formula false true, then
the number of groundings rendered true is returned.
"""
# calculate the number of ground formulas resulting from
# the remaining set of free variables
if assignment is None:
assignment = {}
gf_count = 1
for var in set(self.formula.getVariables(self.mrf.mln)).difference(
assignment.keys()):
domain = self.domains[var]
if domain is None: return 0.
gf_count *= len(domain)
gf = self.formula.ground(self.mrf,
assignment,
allowPartialGroundings=True)
gf.weight = self.formula.weight
for var_name, val in assignment.iteritems():
break
self.domains.drop(var_name, val)
# if the simplified gf reduces to a TrueFalse instance, then
# we return the no of groundings if it's true, or 0 otherwise.
truth = gf.isTrue(self.mrf.evidence)
if truth in (True, False):
if not truth:
trueGFCounter = 0.0
else:
trueGFCounter = gf_count
self.trueGroundings += trueGFCounter
return trueGFCounter
# if the truth value cannot be determined yet, we return
# a new formula grounding with the given assignment
else:
new_grounding = FormulaGrounding(gf,
self.mrf,
parent=self,
assignment=assignment)
self.children.append(new_grounding)
return new_grounding
def __str__(self):
return str(self.assignment) + '->' + str(self.formula) + str(
self.domains) # str(self.assignment)
def __repr__(self):
return str(self)
class SmartGroundingFactory(object):
'''
Implements a factory for generating the groundings of one formula.
The groundings are created incrementally with one
particular ground atom being presented at a time.
fields:
- formula: the (ungrounded) formula representing the root of the
search tree
- mrf: the respective MRF
- root: a FormulaGrounding instance representing the root of the tree,
i.e. an ungrounded formula
- costs: the costs accumulated so far
- depth2fgs: mapping from a depth of the search tree to the corresponding list
of FormulaGroundings
- vars_processed: list of variable names that have already been processed so far
- values_processed: mapping from a variable name to the list of values of that vaiable that
have already been assigned so far.
This class maintains a stack of all its fields in order allow undoing groundings
that have been performed once.
'''
def __init__(self, formula, mrf):
'''
formula might be a formula or a FormulaGrounding instance.
'''
self.mrf = mrf
self.costs = .0
if isinstance(formula, Logic.Formula):
self.formula = formula
self.root = FormulaGrounding(formula, mrf)
elif isinstance(formula, FormulaGrounding):
self.root = formula
self.formula = formula.formula
self.values_processed = ListDict()
self.variable_stack = List(None)
self.var2fgs = ListDict({None: [self.root]})
self.gndAtom2fgs = ListDict()
self.manipulatedFgs = List()
def epochEndsHere(self):
for mem in (self.values_processed, self.variable_stack, self.var2fgs, self.gndAtom2fgs, self.manipulatedFgs):
mem.epochEndsHere()
for fg in self.manipulatedFgs:
fg.epochEndsHere()
def undoEpoch(self):
for fg in self.manipulatedFgs:
fg.undoEpoch()
for mem in (self.values_processed, self.variable_stack, self.var2fgs, self.gndAtom2fgs, self.manipulatedFgs):
mem.undoEpoch()
def ground(self, gndAtom):
'''
Expects a ground atom and creates all groundings
that can be derived by it in terms of FormulaGroundings.
'''
self.manipulatedFgs.clear()
# get all variable assignments of matching literals in the formula
var_assignments = {}
for lit in self.formula.iterLiterals():
assignment = self.gndAtom2Assignment(lit, gndAtom)
if assignment is not None:
unifyDicts(var_assignments, assignment)
cost = .0
# first evaluate formula groundings that contain
# this gnd atom as an artifact
min_depth = None
min_depth_fgs = []
for fg in self.gndAtom2fgs.get(gndAtom, []):
if len(self.variable_stack) <= fg.depth:
continue
if fg.processed.value:
continue
truth = fg.formula.isTrue(self.mrf.evidence)
if truth is not None:
cost -= fg.trueGroundings.value
if not fg in self.manipulatedFgs:
self.manipulatedFgs.append(fg)
fg.processed.set(True)
self.var2fgs.drop(self.variable_stack[fg.depth], fg)
if not fg.parent.obj in self.manipulatedFgs:
self.manipulatedFgs.append(fg.parent.obj)
fg.parent.obj.children.remove(fg) # this is just for the visualization/ no real functionality
if fg.depth == min_depth or min_depth is None:
min_depth_fgs.append(fg)
min_depth = fg.depth
if fg.depth < min_depth:
min_depth = fg.depth
min_depth_fgs = []
min_depth_fgs.append(fg)
for fg in min_depth_fgs:
# add the costs which are aggregated by the root of the subtree
if fg.formula.isTrue(fg.mrf.evidence) == False:
cost += fg.formula.weight * fg.countGroundings()
fg.trueGroundings.set(cost)
# straighten up the variable stack and formula groundings
# since they might have become empty
for var in list(self.variable_stack):
if self.var2fgs[var] is None:
self.variable_stack.remove(var)
for var, value in var_assignments.iteritems():
# skip the variables with values that have already been processed
if not var in self.variable_stack:
depth = len(self.variable_stack)
else:
depth = self.variable_stack.index(var)
queue = list(self.var2fgs[self.variable_stack[depth - 1]])
while len(queue) > 0:
fg = queue.pop()
# first hinge the new variable grounding to all possible parents,
# i.e. all FormulaGroundings with depth - 1...
if fg.depth < depth:
vars_and_values = [{var: value}]
# ...then hinge all previously seen subtrees to the newly created formula groundings...
elif fg.depth >= depth and fg.depth < len(self.variable_stack) - 1:
vars_and_values = [{self.variable_stack[fg.depth + 1]: v}
for v in self.values_processed[self.variable_stack[fg.depth + 1]]]
# ...and finally all variable values that are not part of the subtrees
# i.e. variables that are currently NOT in the variable_stack
# (since they have been removed due to falsity of a formula grounding).
else:
vars_and_values = []
varNotInTree = None
for varNotInTree in [v for v in self.values_processed.keys() if v not in self.variable_stack]: break
if varNotInTree is None: continue
values = self.values_processed[varNotInTree]
for v in values:
vars_and_values.append({varNotInTree: v})
for var_value in vars_and_values:
for var_name, val in var_value.iteritems(): break
if not fg.domains.contains(var_name, val): continue
gnd_result = fg.ground(var_value)
if not fg in self.manipulatedFgs:
self.manipulatedFgs.append(fg)
# if the truth value of a grounding cannot be determined...
if isinstance(gnd_result, FormulaGrounding):
# collect all ground atoms that have been created as
# as artifacts for future evaluation
artifactGndAtoms = [a for a in gnd_result.formula.getGroundAtoms() if not a == gndAtom]
for artGndAtom in artifactGndAtoms:
self.gndAtom2fgs.put(artGndAtom, gnd_result)
if not var_name in self.variable_stack:
self.variable_stack.append(var_name)
self.var2fgs.put(self.variable_stack[gnd_result.depth], gnd_result)
queue.append(gnd_result)
else: # ...otherwise it's true/false; add its costs and discard it.
if self.formula.isHard and gnd_result > 0.:
gnd_result = float('inf')
cost += gnd_result
self.values_processed.put(var, value)
return cost
def printTree(self):
queue = [self.root]
print '---'
while len(queue) > 0:
n = queue.pop()
space = ''
for _ in range(n.depth): space += '--'
print space + str(n)
queue.extend(n.children.list)
print '---'
def gndAtom2Assignment(self, lit, atom):
'''
Returns None if the literal and the atom do not match.
'''
if type(lit) is Logic.Equality or lit.predName != atom.predName: return None
assignment = {}
for p1, p2 in zip(lit.params, atom.params):
if self.mrf.mln.logic.isVar(p1):
assignment[p1] = p2
elif p1 != p2: return None
return assignment
class FormulaGrounding(object):
'''
Represents a particular (partial) grounding of a formula with respect to _one_ predicate
and in terms of disjoint sets of variables occurring in that formula. A grounding of
the formula is represented as a list of assignments of the independent variable sets.
It represents a node in the search tree for weighted SAT solving.
Additional fields:
- depth: the depth of this formula grounding (node) in the search tree
The root node (the formula with no grounded variable has depth 0.
- children: list of formula groundings that have been generated from this fg.
'''
def __init__(self, formula, mrf, parent=None, assignment=None):
'''
Instantiates the formula grounding for a given
- formula: the formula grounded in this node
- mrf: the MRF associated to this problem
- parent: the formula grounding this fg has been created from
- assignment: dictionary mapping variables to their values
'''
self.mrf = mrf
self.formula = formula
self.parent = Ref(parent)
self.trueGroundings = Number(0.)
self.processed = Boolean(False)
if parent is None:
self.depth = 0
else:
self.depth = parent.depth + 1
self.children = List()
self.assignment = assignment
self.domains = ListDict()
if parent is None:
for var in self.formula.getVariables(self.mrf.mln):
self.domains.extend(var, list(self.mrf.domains[self.formula.getVarDomain(var, self.mrf.mln)]))
else:
for (v, d) in parent.domains.iteritems():
self.domains.extend(v, list(d))
self.domains.epochEndsHere()
def epochEndsHere(self):
for mem in (self.parent, self.trueGroundings, self.children, self.domains, self.processed):
mem.epochEndsHere()
def undoEpoch(self):
for mem in (self.parent, self.trueGroundings, self.children, self.domains, self.processed):
mem.undoEpoch()
def countGroundings(self):
'''
Computes the number of ground formulas subsumed by this FormulaGrounding
based on the domain sizes of the free (unbound) variables.
'''
gf_count = 1
for var in self.formula.getVariables(self.mrf):
domain = self.mrf.domains[self.formula.getVarDomain(var, self.mrf)]
gf_count *= len(domain)
return gf_count
def ground(self, assignment=None):
'''
Takes an assignment of _one_ particular variable and
returns a new FormulaGrounding with that assignment. If
the assignment renders the formula false true, then
the number of groundings rendered true is returned.
'''
# calculate the number of ground formulas resulting from
# the remaining set of free variables
if assignment is None:
assignment = {}
gf_count = 1
for var in set(self.formula.getVariables(self.mrf.mln)).difference(assignment.keys()):
domain = self.domains[var]
if domain is None: return 0.
gf_count *= len(domain)
gf = self.formula.ground(self.mrf, assignment, allowPartialGroundings=True)
gf.weight = self.formula.weight
for var_name, val in assignment.iteritems(): break
self.domains.drop(var_name, val)
# if the simplified gf reduces to a TrueFalse instance, then
# we return the no of groundings if it's true, or 0 otherwise.
truth = gf.isTrue(self.mrf.evidence)
if truth in (True, False):
if not truth: trueGFCounter = 0.0
else: trueGFCounter = gf_count
self.trueGroundings += trueGFCounter
return trueGFCounter
# if the truth value cannot be determined yet, we return
# a new formula grounding with the given assignment
else:
new_grounding = FormulaGrounding(gf, self.mrf, parent=self, assignment=assignment)
self.children.append(new_grounding)
return new_grounding
def __str__(self):
return str(self.assignment) + '->' + str(self.formula) + str(self.domains)#str(self.assignment)
def __repr__(self):
return str(self)
