def get_ground_universe(program: Program) -> Set[Symbol]:
prg = program_to_string(program)
ctl = Control()
ctl.add("base", [], prg)
ctl.ground([("base", [])])
ground_universe = set(
[ground_atom.symbol for ground_atom in ctl.symbolic_atoms])
log(f"Ground universe: {ground_universe}")
return ground_universe
def test_ground(self):
'''
Test grounding with context and parameters.
'''
ctx = Context()
ctl = Control()
ctl.add('part', ['c'], 'p(@cb_num(c)).')
ctl.ground([('part', [Number(1)])], ctx)
symbols = [atom.symbol for atom in ctl.symbolic_atoms]
self.assertEqual(sorted(symbols), [Function('p', [Number(0)]), Function('p', [Number(2)])])
def test_lower(self):
'''
Test lower bounds reported during optimization.
'''
ctl = Control(['--opt-str=usc,oll,0', '--stats=2'])
ctl.add(
'base', [],
'1 { p(X); q(X) } 1 :- X=1..3. #minimize { 1,p,X: p(X); 1,q,X: q(X) }.'
)
ctl.ground([('base', [])])
lower = []
self.assertTrue(
cast(SolveResult, ctl.solve(on_unsat=lower.append)).satisfiable)
self.assertEqual(lower, [[1], [2], [3]])
self.assertEqual(ctl.statistics['summary']['lower'], [3.0])
def test_sum_of_salaries():
context = Context()
@context.valasp()
class Income:
company: str # String
amount: int # Integer
def __post_init__(self):
if not (self.amount > 0):
raise ValueError("amount must be positive")
self.__class__.amount_sum += self.amount
if self.__class__.amount_sum > Integer.max():
raise OverflowError(
f"sum of amount may exceed {Integer.max()}")
@classmethod
def before_grounding_init_amount_sum(cls):
cls.amount_sum = 0
@classmethod
def after_grounding_check_amount_sum(cls):
if cls.amount_sum < 10000:
raise ValueError(f"sum of amount cannot reach 10000")
if cls.amount_sum == 3000000000:
raise OverflowError(f"catch this!")
control = Control()
control.add(
"base", [],
'income("Acme ASP",1500000000). income("Yoyodyne YAML",1500000000).')
control.add("valasp", [], context.valasp_validators())
context.valasp_run_class_methods('before_grounding')
with pytest.raises(RuntimeError):
control.ground([("base", []), ("valasp", [])], context=context)
with pytest.raises(OverflowError):
context.valasp_run_class_methods('after_grounding')
def test_monkey(self):
from clingo import Control
from clorm import Predicate, IntegerField, StringField, FactBase, SymbolPredicateUnifier
spu = SymbolPredicateUnifier()
@spu.register
class Afact(Predicate):
num1 = IntegerField()
str1 = StringField()
@spu.register
class Bfact(Predicate):
num1 = IntegerField()
str1 = StringField()
af1 = Afact(1, "aaa")
af2 = Afact(2, "bbb")
af3 = Afact(3, "aaa")
bf1 = Bfact(1, "eee")
bf2 = Bfact(2, "fff")
bf2 = Bfact(3, "ggg")
fb2 = None
def on_model(model):
nonlocal fb2
fb2 = model.facts(spu, atoms=True)
fb1 = FactBase([af1, af2, af3, bf1, bf2])
ctrl = Control()
ctrl.add_facts(fb1)
ctrl.ground([("base", [])])
ctrl.solve(on_model=on_model)
s_a_all = fb2.select(Afact)
self.assertEqual(set([a for a in s_a_all.get()]), set([af1, af2, af3]))
def valasp_run(self, control: clingo.Control, on_validation_done: Callable = None, on_model: Callable = None,
aux_program: List[str] = None, with_validators: bool = True, with_solve: bool = True) -> None:
"""Run grounder on the given controller, possibly performing validation and searching for a model.
:param control: a controller
:param on_validation_done: a function invoked after grounding, if no validation error is reported
:param on_model: a callback function to process a model
:param aux_program: more ASP code to add to the program
:param with_validators: if True, validator constraints are added, and ``before_grounding*`` and ``after_grounding*`` class methods are called
:param with_solve: if True, a model is searched
"""
if with_validators:
control.add("valasp", [], self.valasp_validators())
self.valasp_run_class_methods('before_grounding')
if aux_program:
control.add("aux_program", [], '\n'.join(aux_program))
control.ground([("base", []), ("valasp", []), ("aux_program", [])], context=self)
if with_validators:
self.valasp_run_class_methods('after_grounding')
if on_validation_done:
on_validation_done()
if with_solve:
# noinspection PyUnresolvedReferences
control.solve(on_model=on_model)
class TestAtoms(TestCase):
'''
Tests for theory and symbolic atoms.
'''
def setUp(self):
self.ctl = Control()
def test_symbolic_atom(self):
'''
Test symbolic atom.
'''
self.ctl.add('base', [], 'p(1). {p(2)}. #external p(3).')
self.ctl.ground([('base', [])])
atoms = self.ctl.symbolic_atoms
p1 = atoms[Function('p', [Number(1)])]
self.assertIsNotNone(p1)
self.assertTrue(p1.is_fact)
self.assertFalse(p1.is_external)
self.assertTrue(p1.literal >= 1)
self.assertEqual(p1.symbol, Function('p', [Number(1)]))
self.assertTrue(p1.match('p', 1, True))
self.assertFalse(p1.match('p', 2, True))
p2 = atoms[Function('p', [Number(2)])]
self.assertIsNotNone(p2)
self.assertFalse(p2.is_fact)
self.assertFalse(p2.is_external)
self.assertTrue(p2.literal >= 2)
self.assertEqual(p2.symbol, Function('p', [Number(2)]))
self.assertTrue(p2.match('p', 1, True))
self.assertFalse(p2.match('p', 2, True))
p3 = atoms[Function('p', [Number(3)])]
self.assertIsNotNone(p3)
self.assertFalse(p3.is_fact)
self.assertTrue(p3.is_external)
self.assertTrue(p3.literal >= 2)
self.assertEqual(p3.symbol, Function('p', [Number(3)]))
self.assertTrue(p3.match('p', 1, True))
self.assertFalse(p3.match('p', 2, True))
p4 = atoms[Function('p', [Number(4)])]
self.assertIsNone(p4)
def test_symbolic_atoms(self):
'''
Test symbolic atoms.
'''
self.ctl.add(
'base', [],
'p(1). {p(2)}. #external p(3). q(1). -p(1). {q(2)}. #external q(3).'
)
self.ctl.ground([('base', [])])
atoms = self.ctl.symbolic_atoms
self.assertEqual(sorted(atoms.signatures), [('p', 1, False),
('p', 1, True),
('q', 1, True)])
ps = list(atoms.by_signature('p', 1, True))
self.assertEqual(len(ps), 3)
for p in ps:
self.assertEqual(p.symbol.name, 'p')
self.assertTrue(p.symbol.positive)
self.assertEqual(len(p.symbol.arguments), 1)
nps = list(atoms.by_signature('p', 1, False))
self.assertEqual(len(nps), 1)
for p in nps:
self.assertEqual(p.symbol.name, 'p')
self.assertTrue(p.symbol.negative)
self.assertEqual(p.symbol.arguments, [Number(1)])
self.assertEqual(len(atoms), 7)
self.assertEqual(len(list(atoms)), 7)
self.assertIn(Function('p', [Number(1)], True), atoms)
self.assertIn(Function('p', [Number(1)], False), atoms)
self.assertIn(Function('q', [Number(2)], True), atoms)
self.assertNotIn(Function('q', [Number(2)], False), atoms)
def test_theory_term(self):
'''
Test theory term.
'''
self.ctl.add('base', [], THEORY)
self.ctl.add('base', [], '&a { 1,a,f(a),{1},(1,),[1] }.')
self.ctl.ground([('base', [])])
terms = next(self.ctl.theory_atoms).elements[0].terms
self.assertEqual([str(term) for term in terms],
['1', 'a', 'f(a)', '{1}', '(1,)', '[1]'])
num, sym, fun, set_, tup, lst = terms
self.assertEqual(num.type, TheoryTermType.Number)
self.assertEqual(num.number, 1)
self.assertEqual(sym.type, TheoryTermType.Symbol)
self.assertEqual(sym.name, 'a')
self.assertEqual(fun.type, TheoryTermType.Function)
self.assertEqual(fun.name, 'f')
self.assertEqual(fun.arguments, [sym])
self.assertEqual(set_.type, TheoryTermType.Set)
self.assertEqual(set_.arguments, [num])
self.assertEqual(tup.type, TheoryTermType.Tuple)
self.assertEqual(tup.arguments, [num])
self.assertEqual(lst.type, TheoryTermType.List)
self.assertEqual(lst.arguments, [num])
self.assertNotEqual(hash(num), hash(sym))
self.assertEqual(hash(num), hash(lst.arguments[0]))
self.assertNotEqual(num < sym, sym < num)
self.assertRegex(repr(num), 'TheoryTerm(.*)')
def test_theory_element(self):
'''
Test theory element.
'''
self.ctl.add('base', [], THEORY)
self.ctl.add('base', [], '{a; b}.')
self.ctl.add('base', [], '&a { 1; 2,3: a,b }.')
self.ctl.ground([('base', [])])
atom = next(self.ctl.theory_atoms)
elements = sorted(atom.elements, key=lambda elem: len(elem.terms))
self.assertEqual([str(elem) for elem in elements], ['1', '2,3: a,b'])
a, b = elements
self.assertEqual(len(a.condition), 0)
self.assertEqual(len(b.condition), 2)
self.assertTrue(all(lit >= 1 for lit in b.condition))
self.assertGreaterEqual(b.condition_id, 1)
self.assertEqual(a, a)
self.assertNotEqual(a, b)
self.assertNotEqual(hash(a), hash(b))
self.assertNotEqual(a < b, b < a)
self.assertRegex(repr(a), 'TheoryElement(.*)')
def test_theory_atom(self):
'''
Test theory atom.
'''
self.ctl.add('base', [], THEORY)
self.ctl.add('base', [], '&a {}.')
self.ctl.add('base', [], '&b {} = 1.')
self.ctl.ground([('base', [])])
atoms = sorted(list(self.ctl.theory_atoms),
key=lambda atom: atom.term.name)
self.assertEqual([str(atom) for atom in atoms], ['&a{}', '&b{}=1'])
a, b = atoms
self.assertTrue(a.literal >= 1)
self.assertIsNone(a.guard)
self.assertIsNotNone(b.guard)
self.assertEqual(b.guard[0], "=")
self.assertEqual(str(b.guard[1]), "1")
self.assertEqual(len(a.elements), 0)
self.assertEqual(a, a)
self.assertNotEqual(a, b)
self.assertNotEqual(hash(a), hash(b))
self.assertNotEqual(a < b, b < a)
self.assertRegex(repr(a), 'TheoryAtom(.*)')
class TestSolving(TestCase):
'''
Tests basic solving and related functions.
'''
def setUp(self):
self.mcb = _MCB()
self.mit = _MCB()
self.ctl = Control(['0'])
def tearDown(self):
self.mcb = None
self.mit = None
self.ctl = None
def test_solve_result_str(self):
'''
Test string representation of solve results.
'''
ret = self.ctl.solve()
self.assertEqual(str(ret), 'SAT')
self.assertRegex(repr(ret), 'SolveResult(.*)')
def test_model_str(self):
'''
Test string representation of models.
'''
self.ctl.add('base', [], 'a.')
self.ctl.ground([('base', [])])
with self.ctl.solve(yield_=True) as hnd:
for mdl in hnd:
self.assertEqual(str(mdl), "a")
self.assertRegex(repr(mdl), "Model(.*)")
def test_solve_cb(self):
'''
Test solving using callback.
'''
self.ctl.add("base", [], "1 {a; b} 1. c.")
self.ctl.ground([("base", [])])
_check_sat(self, cast(SolveResult, self.ctl.solve(on_model=self.mcb.on_model, yield_=False, async_=False)))
self.assertEqual(self.mcb.models, _p(['a', 'c'], ['b', 'c']))
self.assertEqual(self.mcb.last[0], ModelType.StableModel)
def test_solve_async(self):
'''
Test asynchonous solving.
'''
self.ctl.add("base", [], "1 {a; b} 1. c.")
self.ctl.ground([("base", [])])
with cast(SolveHandle, self.ctl.solve(on_model=self.mcb.on_model, yield_=False, async_=True)) as hnd:
_check_sat(self, hnd.get())
self.assertEqual(self.mcb.models, _p(['a', 'c'], ['b', 'c']))
def test_solve_yield(self):
'''
Test solving yielding models.
'''
self.ctl.add("base", [], "1 {a; b} 1. c.")
self.ctl.ground([("base", [])])
with cast(SolveHandle, self.ctl.solve(on_model=self.mcb.on_model, yield_=True, async_=False)) as hnd:
for m in hnd:
self.mit.on_model(m)
_check_sat(self, hnd.get())
self.assertEqual(self.mcb.models, _p(['a', 'c'], ['b', 'c']))
self.assertEqual(self.mit.models, _p(['a', 'c'], ['b', 'c']))
def test_solve_async_yield(self):
'''
Test solving yielding models asynchronously.
'''
self.ctl.add("base", [], "1 {a; b} 1. c.")
self.ctl.ground([("base", [])])
with self.ctl.solve(on_model=self.mcb.on_model, yield_=True, async_=True) as hnd:
while True:
hnd.resume()
_ = hnd.wait()
m = hnd.model()
if m is None:
break
self.mit.on_model(m)
_check_sat(self, hnd.get())
self.assertEqual(self.mcb.models, _p(['a', 'c'], ['b', 'c']))
self.assertEqual(self.mit.models, _p(['a', 'c'], ['b', 'c']))
def test_solve_interrupt(self):
'''
Test interrupting solving.
'''
self.ctl.add("base", [], "1 { p(P,H): H=1..99 } 1 :- P=1..100.\n1 { p(P,H): P=1..100 } 1 :- H=1..99.")
self.ctl.ground([("base", [])])
with self.ctl.solve(async_=True) as hnd:
hnd.resume()
hnd.cancel()
ret = hnd.get()
self.assertTrue(ret.interrupted)
with self.ctl.solve(async_=True) as hnd:
hnd.resume()
self.ctl.interrupt()
ret = hnd.get()
self.assertTrue(ret.interrupted)
def test_solve_core(self):
'''
Test core retrieval.
'''
self.ctl.add("base", [], "3 { p(1..10) } 3.")
self.ctl.ground([("base", [])])
ass = []
for atom in self.ctl.symbolic_atoms.by_signature("p", 1):
ass.append(-atom.literal)
ret = cast(SolveResult, self.ctl.solve(on_core=self.mcb.on_core, assumptions=ass))
self.assertTrue(ret.unsatisfiable)
self.assertTrue(len(self.mcb.core) > 7)
def test_enum(self):
'''
Test core retrieval.
'''
self.ctl = Control(['0', '-e', 'cautious'])
self.ctl.add("base", [], "1 {a; b} 1. c.")
self.ctl.ground([("base", [])])
self.ctl.solve(on_model=self.mcb.on_model)
self.assertEqual(self.mcb.last[0], ModelType.CautiousConsequences)
self.assertEqual([self.mcb.last[1]], _p(['c']))
self.ctl = Control(['0', '-e', 'brave'])
self.ctl.add("base", [], "1 {a; b} 1. c.")
self.ctl.ground([("base", [])])
self.ctl.solve(on_model=self.mcb.on_model)
self.assertEqual(self.mcb.last[0], ModelType.BraveConsequences)
self.assertEqual([self.mcb.last[1]], _p(['a', 'b', 'c']))
def test_model(self):
'''
Test functions of model.
'''
def on_model(m: Model):
self.assertTrue(m.contains(Function('a')))
self.assertTrue(m.is_true(cast(SymbolicAtom, m.context.symbolic_atoms[Function('a')]).literal))
self.assertFalse(m.is_true(1000))
self.assertEqual(m.thread_id, 0)
self.assertEqual(m.number, 1)
self.assertFalse(m.optimality_proven)
self.assertEqual(m.cost, [3])
m.extend([Function('e')])
self.assertSequenceEqual(m.symbols(theory=True), [Function('e')])
self.ctl.add("base", [], "a. b. c. #minimize { 1,a:a; 1,b:b; 1,c:c }.")
self.ctl.ground([("base", [])])
self.ctl.solve(on_model=on_model)
def test_control_clause(self):
'''
Test adding clauses while solving.
'''
self.ctl.add("base", [], "1 {a; b; c} 1.")
self.ctl.ground([("base", [])])
with cast(SolveHandle, self.ctl.solve(on_model=self.mcb.on_model, yield_=True, async_=False)) as hnd:
for m in hnd:
clause = []
if m.contains(Function('a')):
clause.append((Function('b'), False))
else:
clause.append((Function('a'), False))
m.context.add_clause(clause)
_check_sat(self, hnd.get())
self.assertEqual(len(self.mcb.models), 2)
def test_control_nogood(self):
'''
Test adding nogoods while solving.
'''
self.ctl.add("base", [], "1 {a; b; c} 1.")
self.ctl.ground([("base", [])])
with cast(SolveHandle, self.ctl.solve(on_model=self.mcb.on_model, yield_=True, async_=False)) as hnd:
for m in hnd:
clause = []
if m.contains(Function('a')):
clause.append((Function('b'), True))
else:
clause.append((Function('a'), True))
m.context.add_nogood(clause)
_check_sat(self, hnd.get())
self.assertEqual(len(self.mcb.models), 2)
def main(self, ctl: Control, files: Sequence[str]):
'''
Parse LP^MLN program and convert to ASP with weak constraints.
'''
observer = Observer()
ctl.register_observer(observer)
ctl.add("base", [], THEORY)
ctl.add("base", [], self.evidence_file)
if self.two_solve_calls:
ctl.add("base", [], '#external ext_helper.')
# TODO: Make sure the ext_helper atom is not contained in the program.
if not files:
files = ["-"]
self._convert(ctl, files)
ctl.ground([("base", [])])
if self.query != []:
self._ground_queries(ctl.symbolic_atoms)
bound_hr = 2**63 - 1
if self.two_solve_calls:
# First solve call
# Soft rules are deactivated
# TODO: Suppress output of first solve call, add flag
# TODO: Activate this per flag
ctl.assign_external(Function("ext_helper"), False)
with ctl.solve(yield_=True) as h:
for m in h:
bound_hr = m.cost[0]
# TODO: Don't show ext_helper
# ctl.release_external(Function("ext_helper"))
ctl.assign_external(Function("ext_helper"), True)
if self.display_all_probs:
ctl.configuration.solve.opt_mode = f'enum, {bound_hr}, {(2**63)-1}'
ctl.configuration.solve.models = 0
model_costs = []
with ctl.solve(yield_=True) as handle:
for model in handle:
if self.display_all_probs or self.query != []:
model_costs.append(model.cost)
if self.query != []:
self._check_model_for_query(model)
if model_costs != [] and (self.display_all_probs or self.query != []):
if 0 not in observer.priorities:
# TODO: Should this be error or warning?
print(
'No soft weights in program. Cannot calculate probabilites'
)
# TODO: What about case where there are other priorities than 0/1?
# elif not self.two_solve_calls and any(
# x > 1 for x in observer.priorities):
# print(observer.priorities)
# print('testasd')
else:
probs = ProbabilityModule(
model_costs, observer.priorities,
[self.translate_hard_rules, self.two_solve_calls])
if self.display_all_probs:
probs.print_probs()
if self.query != []:
probs.get_query_probability(self.query)
class Solver:
def __init__(self, horizon=0):
self.__horizon = horizon
self.__prg = Control(['-t4'])
self.__future = None
self.__solution = None
self.__assign = []
self.__prg.load("board.lp")
self.__prg.load("robots.lp")
parts = [ ("base", [])
, ("check", [Number(0)])
, ("state", [Number(0)])
]
for t in range(1, self.__horizon+1):
parts.extend([ ("trans", [Number(t)])
, ("check", [Number(t)])
, ("state", [Number(t)])
])
self.__prg.ground(parts)
self.__prg.assign_external(Function("horizon", [Number(self.__horizon)]), True)
def __next(self):
assert(self.__horizon < 30)
self.__prg.assign_external(Function("horizon", [Number(self.__horizon)]), False)
self.__horizon += 1
self.__prg.ground([ ("trans", [Number(self.__horizon)])
, ("check", [Number(self.__horizon)])
, ("state", [Number(self.__horizon)])
])
self.__prg.assign_external(Function("horizon", [Number(self.__horizon)]), True)
def start(self, board):
self.__assign = []
for robot, (x, y) in board.pos.items():
self.__assign.append(Function("pos", [Function(robot), Number(x+1), Number(y+1), Number(0)]))
self.__assign.append(Function("target",
[ Function(board.current_target[0])
, Number(board.current_target[2] + 1)
, Number(board.current_target[3] + 1)
]))
for x in self.__assign:
self.__prg.assign_external(x, True)
self.__solution = None
self.__future = self.__prg.solve(on_model=self.__on_model, async_=True)
def busy(self):
if self.__future is None:
return False
if self.__future.wait(0):
if self.__solution is None:
self.__next()
self.__future = self.__prg.solve(on_model=self.__on_model, async_=True)
return True
else:
self.__future = None
return False
return True
def stop(self):
if self.__future is not None:
self.__future.cancel()
self.__future.wait()
self.__future = None
self.get()
def get(self):
solution = self.__solution
self.__solution = None
for x in self.__assign:
self.__prg.assign_external(x, False)
self.__assign = []
return solution
def __on_model(self, m):
self.__solution = []
for atom in m.symbols(atoms=True):
if atom.name == "move" and len(atom.arguments) == 4:
c, x, y, t = [(n.number if n.type == SymbolType.Number else str(n)) for n in atom.arguments]
self.__solution.append((c, x, y, t))
self.__solution.sort(key=lambda x: x[3])
p = None
i = 0
for x in self.__solution:
if p is not None and \
p[0] == x[0] and \
p[1] == x[1] and \
p[2] == x[2]:
break
p = x
i += 1
del self.__solution[i:]
def setUp(self):
self.mcb = _MCB()
self.mit = _MCB()
self.ctl = Control(['0'])
class VizloControl(Control):
def add_to_painter(self, model: Union[Model, PythonModel,
Collection[clingo.Symbol]]):
"""
will register model with the internal painter. On all consecutive calls to paint(), this model will be painted.
:param model: the model to add to the painter.
:return:
"""
self.painter.append(PythonModel(model))
def __init__(self,
arguments: List[str] = [],
logger=None,
message_limit: int = 20,
print_entire_models=False,
atom_draw_maximum=15):
self.control = Control(arguments, logger, message_limit)
self.painter: List[PythonModel] = list()
self.program: ASTProgram = list()
self.raw_program: str = ""
self.transformer = JustTheRulesTransformer()
self._print_changes_only = not print_entire_models
self._atom_draw_maximum = atom_draw_maximum
def _set_print_only_changes(self, value: bool) -> None:
self._print_changes_only = value
def ground(self,
parts: List[Tuple[str, List[Symbol]]],
context: Any = None) -> None:
self.control.ground(parts, context)
def solve(self,
assumptions: List[Union[Tuple[Symbol, bool], int]] = [],
on_model=None,
on_statistics=None,
on_finish=None,
yield_: bool = False,
async_: bool = False) -> Union[SolveHandle, SolveResult]:
return self.control.solve(assumptions, on_model, on_statistics,
on_finish, yield_, async_)
def load(self, path):
prg = ""
with open(path) as f:
for line in f:
prg += line
self.program += prg
self.control.load(path)
def add(self, name: str, parameters: List[str], program: str) -> None:
self.raw_program += program
self.control.add(name, parameters, program)
def find_nodes_corresponding_to_stable_models(self, g, stable_models):
correspoding_nodes = set()
for model in stable_models:
for node in g.nodes():
log(f"{node} {type(node.model)} == {model} {type(model)} -> {set(node.model) == model}"
)
if set(node.model) == model and len(
g.edges(node)) == 0: # This is a leaf
log(f"{node} <-> {model}")
correspoding_nodes.add(node)
break
return correspoding_nodes
def prune_graph_leading_to_models(self, graph: nx.DiGraph,
models_as_nodes):
before = len(graph)
relevant_nodes = set()
for model in models_as_nodes:
for relevant_node in nx.all_simple_paths(graph, INITIAL_EMPTY_SET,
model):
relevant_nodes.update(relevant_node)
all_nodes = set(graph.nodes())
irrelevant_nodes = all_nodes - relevant_nodes
graph.remove_nodes_from(irrelevant_nodes)
after = len(graph)
log(f"Removed {before - after} of {before} nodes ({(before - after) / before})"
)
def _make_graph(self, _sort=True):
"""
Ties together transformation and solving. Transforms the already added program parts and creates a solving tree.
:param _sort: Whether the program should be sorted automatically. Setting this to false will likely result into
wrong results!
:return:
:raises ValueError:
"""
if not len(self.raw_program):
raise ValueError("Can't paint an empty program.")
else:
t = JustTheRulesTransformer()
program = t.transform(self.raw_program, _sort)
if len(self.painter):
universe = get_ground_universe(program)
global_assumptions = make_global_assumptions(
universe, self.painter)
solve_runner = SolveRunner(program, t.rule2signatures)
g = solve_runner.make_graph(global_assumptions)
else:
solve_runner = SolveRunner(program,
symbols_in_heads_map=t.rule2signatures)
g = solve_runner.make_graph()
return g
def paint(self,
atom_draw_maximum: int = 20,
show_entire_model: bool = False,
sort_program: bool = True,
**kwargs):
"""
Will create a graph visualization of the solving process. If models have been added using add_to_painter,
only the solving paths that lead to these models will be drawn.
:param atom_draw_maximum: int
The maximum amount of atoms that will be printed for each partial model. (default=20)
:param show_entire_model: bool
If false, only the atoms that have been added at a solving step will be printed (up to atom_draw_maximum).
If true, all atoms will always be printed (up to atom_draw_maximum). (default=False)
:param sort_program:
If true, the rules of a program will be sorted and grouped by their dependencies.
Each set of rules will contain all rules in which each atom in its heads is contained in a head.
:param kwargs:
kwargs will be forwarded to the visualisation module. See graph.draw()
:return:
"""
if type(atom_draw_maximum) != int:
raise ValueError(
f"Argument atom_draw_maximum should be an integer (received {atom_draw_maximum})."
)
g = self._make_graph(sort_program)
display = NetworkxDisplay(g, atom_draw_maximum, not show_entire_model)
img = display.draw(**kwargs)
return img
def _add_and_ground(self, prg):
"""Short cut for complex add and ground calls, should only be used for debugging purposes."""
self.add("base", [], prg)
self.ground([("base", [])])
##################
# Just pass-through stuff
##################
@property
def configuration(self) -> Configuration:
return self.control.configuration
@property
def is_conflicting(self) -> bool:
return self.control.is_conflicting
@property
def statistics(self) -> dict:
return self.control.statistics
@property
def symbolic_atoms(self) -> SymbolicAtoms:
return self.control.symbolic_atoms
@property
def theory_atoms(self) -> TheoryAtomIter:
return self.control.theory_atoms
@property
def use_enumeration_assumption(self) -> bool:
return self.control.use_enumeration_assumption
def assign_external(self, external: Union[Symbol, int],
truth: Optional[bool], **kwargs) -> None:
self.control.assign_external(external, truth, **kwargs)
def backend(self) -> Backend:
return self.control.backend()
def builder(self) -> ProgramBuilder:
return self.control.builder()
def cleanup(self) -> None:
self.control.cleanup()
def get_const(self, name: str) -> Optional[Symbol]:
return self.control.get_const(name)
def interrupt(self):
self.control.interrupt()
def register_observer(self, observer, replace=False):
self.register_observer(observer, replace)
def release_external(self, symbol: Union[Symbol, int]) -> None:
self.control.release_external(symbol)
f.cancel()
ret = f.get()
else:
ret = None
if msg == "interrupt":
state = States.IDLE
elif msg == "exit":
return
elif msg == "less_pigeon_please":
prg.assign_external(Function("p"), False)
state = States.IDLE
elif msg == "more_pigeon_please":
prg.assign_external(Function("p"), True)
state = States.IDLE
elif msg == "solve":
state = States.SOLVE
else:
raise (RuntimeError("unexpected message: " + msg))
if ret is not None and not ret.unknown:
k = k + 1
prg.ground([("sleep", [Number(k)])])
prg.release_external(Function("sleep", [Number(k - 1)]))
prg.assign_external(Function("sleep", [Number(k)]), True)
finally:
conn.close()
prg = Control()
prg.load("client.lp")
main(prg)
class TestSymbolicBackend(TestCase):
'''
Tests for the ymbolic symbolic_backend.
'''
def setUp(self):
self.prg = Program()
self.obs = ProgramObserver(self.prg)
self.ctl = Control(message_limit=0)
self.ctl.register_observer(self.obs)
def test_add_acyc_edge(self):
'''
Test edge statement.
'''
a = Function("a", [Function("c1")])
b = Function("b", [Function("c2")])
c = Function("c", [Function("c3")])
with SymbolicBackend(self.ctl.backend()) as symbolic_backend:
symbolic_backend.add_acyc_edge(1, 3, [a], [b, c])
self.assertEqual(str(self.prg),
"#edge (1,3): a(c1), not b(c2), not c(c3).")
def test_add_assume(self):
'''
Test assumptions.
'''
a = Function("a", [Function("c1")])
b = Function("b", [Function("c2")])
c = Function("c", [Function("c3")])
with SymbolicBackend(self.ctl.backend()) as symbolic_backend:
symbolic_backend.add_assume([a, b, c])
self.assertEqual(str(self.prg), "% assumptions: a(c1), b(c2), c(c3)")
def test_add_external(self):
'''
Test external statement.
'''
a = Function("a", [Function("c1")])
with SymbolicBackend(self.ctl.backend()) as symbolic_backend:
symbolic_backend.add_external(a, TruthValue.True_)
self.assertEqual(str(self.prg), "#external a(c1). [True]")
def test_add_heuristic(self):
'''
Test heuristic statement.
'''
a = Function("a", [Function("c1")])
b = Function("b", [Function("c2")])
c = Function("c", [Function("c3")])
with SymbolicBackend(self.ctl.backend()) as symbolic_backend:
symbolic_backend.add_heuristic(a, HeuristicType.Level, 2, 3, [b],
[c])
self.assertEqual(str(self.prg),
"#heuristic a(c1): b(c2), not c(c3). [2@3, Level]")
def test_add_minimize(self):
'''
Test minimize statement.
'''
a = Function("a", [Function("c1")])
b = Function("b", [Function("c2")])
c = Function("c", [Function("c3")])
with SymbolicBackend(self.ctl.backend()) as symbolic_backend:
symbolic_backend.add_minimize(1, [(a, 3), (b, 5)], [(c, 7)])
self.assertEqual(
str(self.prg),
"#minimize{3@1,0: a(c1); 5@1,1: b(c2); 7@1,2: not c(c3)}.")
def test_add_project(self):
'''
Test project statements.
'''
a = Function("a", [Function("c1")])
b = Function("b", [Function("c2")])
c = Function("c", [Function("c3")])
with SymbolicBackend(self.ctl.backend()) as symbolic_backend:
symbolic_backend.add_project([a, b, c])
self.assertEqual(str(self.prg),
"#project a(c1).\n#project b(c2).\n#project c(c3).")
def test_add_empty_project(self):
'''
Test project statements.
'''
with SymbolicBackend(self.ctl.backend()) as symbolic_backend:
symbolic_backend.add_project([])
self.assertEqual(str(self.prg), "#project x: #false.")
def test_add_rule(self):
'''
Test simple rules.
'''
a = Function("a", [Function("c1")])
b = Function("b", [Function("c2")])
c = Function("c", [Function("c3")])
with SymbolicBackend(self.ctl.backend()) as symbolic_backend:
symbolic_backend.add_rule([a], [b], [c])
self.assertEqual(str(self.prg), "a(c1) :- b(c2), not c(c3).")
def test_add_choice_rule(self):
'''
Test choice rules.
'''
a = Function("a", [Function("c1")])
b = Function("b", [Function("c2")])
c = Function("c", [Function("c3")])
with SymbolicBackend(self.ctl.backend()) as symbolic_backend:
symbolic_backend.add_rule([a], [b], [c], choice=True)
self.assertEqual(str(self.prg), "{a(c1)} :- b(c2), not c(c3).")
def test_add_weight_rule(self):
'''
Test weight rules.
'''
a = Function("a", [Function("c1")])
b = Function("b", [Function("c2")])
c = Function("c", [Function("c3")])
with SymbolicBackend(self.ctl.backend()) as symbolic_backend:
symbolic_backend.add_weight_rule([a], 3, [(b, 5)], [(c, 7)])
self.assertEqual(str(self.prg),
"a(c1) :- 3{5,0: b(c2), 7,1: not c(c3)}.")
def test_add_weight_choice_rule(self):
'''
Test weight rules that are also choice rules.
'''
a = Function("a", [Function("c1")])
b = Function("b", [Function("c2")])
c = Function("c", [Function("c3")])
with SymbolicBackend(self.ctl.backend()) as symbolic_backend:
symbolic_backend.add_weight_rule([a],
3, [(b, 5)], [(c, 7)],
choice=True)
self.assertEqual(str(self.prg),
"{a(c1)} :- 3{5,0: b(c2), 7,1: not c(c3)}.")
class SolveThread(Thread):
STATE_SOLVE = 1
STATE_IDLE = 2
STATE_EXIT = 3
def __init__(self, connection):
Thread.__init__(self)
self.k = 0
self.prg = Control()
self.prg.load("client.lp")
self.prg.ground([("pigeon", []), ("sleep", [Number(self.k)])])
self.prg.assign_external(Function("sleep", [Number(self.k)]), True)
self.state = SolveThread.STATE_IDLE
self.input = Connection()
self.output = connection
def on_model(self, model):
self.output.send("answer: " + str(model)),
def on_finish(self, ret):
self.output.send("finish: " + str(ret) +
(" (INTERRUPTED)" if ret.interrupted else ""))
def handle_message(self, msg):
if msg == "interrupt":
self.state = SolveThread.STATE_IDLE
elif msg == "exit":
self.state = SolveThread.STATE_EXIT
elif msg == "less_pigeon_please":
self.prg.assign_external(Function("p"), False)
self.state = SolveThread.STATE_IDLE
elif msg == "more_pigeon_please":
self.prg.assign_external(Function("p"), True)
self.state = SolveThread.STATE_IDLE
elif msg == "solve":
self.state = SolveThread.STATE_SOLVE
else:
raise (RuntimeError("unexpected message: " + msg))
def run(self):
while True:
if self.state == SolveThread.STATE_SOLVE:
f = self.prg.solve(on_model=self.on_model,
on_finish=self.on_finish,
async_=True)
msg = self.input.receive()
if self.state == SolveThread.STATE_SOLVE:
f.cancel()
ret = f.get()
else:
ret = None
self.handle_message(msg)
if self.state == SolveThread.STATE_EXIT:
return
elif ret is not None and not ret.unknown:
self.k = self.k + 1
self.prg.ground([("sleep", [Number(self.k)])])
self.prg.release_external(
Function("sleep", [Number(self.k - 1)]))
self.prg.assign_external(Function("sleep", [Number(self.k)]),
True)
def setUp(self):
self.ctl = Control()
class TestSymbol(TestCase):
'''
Tests basic solving and related functions.
'''
def setUp(self):
self.mcb = _MCB()
self.ctl = Control(['0'])
def tearDown(self):
self.mcb = None
self.ctl = None
def test_propagator_control(self):
'''
Test PropagateControl.
'''
self.ctl.add("base", [], "{a}.")
self.ctl.ground([("base", [])])
self.ctl.register_propagator(TestPropagatorControl(self))
_check_sat(self, cast(SolveResult, self.ctl.solve(on_model=self.mcb.on_model, yield_=False, async_=False)))
self.assertEqual(self.mcb.models, _p(['a']))
def test_propagator_init(self):
'''
Test PropagateInit and Assignment.
'''
self.ctl.add("base", [], "{a; b; c}.")
self.ctl.ground([("base", [])])
self.ctl.register_propagator(TestPropagatorInit(self))
_check_sat(self, cast(SolveResult, self.ctl.solve(on_model=self.mcb.on_model, yield_=False, async_=False)))
self.assertEqual(self.mcb.models[-1:], _p(['a', 'b', 'c']))
def test_propagator(self):
'''
Test adding literals while solving.
'''
self.ctl.add("base", [], "")
self.ctl.ground([("base", [])])
self.ctl.register_propagator(TestPropagator(self))
_check_sat(self, cast(SolveResult, self.ctl.solve(on_model=self.mcb.on_model, yield_=False, async_=False)))
self.assertEqual(self.mcb.models, _p([], []))
def test_heurisitc(self):
'''
Test decide.
'''
self.ctl = Control(['1'])
self.ctl.add("base", [], "{a;b}.")
self.ctl.ground([("base", [])])
self.ctl.register_propagator(TestHeuristic(self))
self.ctl.solve(on_model=self.mcb.on_model)
self.assertEqual(self.mcb.models, _p(['a']))
class Solver:
def __init__(self):
self.k = 0
self.prg = Control()
self.prg.load("client.lp")
self.prg.ground([("pigeon", []), ("sleep", [Number(self.k)])])
self.prg.assign_external(Function("sleep", [Number(self.k)]), True)
self.ret = None
self.models = []
def on_model(self, model):
self.models.append(str(model))
def start(self, on_finish):
if self.ret is not None and not self.ret.unknown():
self.k = self.k + 1
self.prg.ground([("sleep", [self.k])])
self.prg.release_external(Function("sleep", [Number(self.k - 1)]))
self.prg.assign_external(Function("sleep", [Number(self.k)]), True)
self.future = self.prg.solve(on_model=self.on_model,
on_finish=on_finish,
async_=True)
def stop(self):
self.future.cancel()
def finish(self):
ret = self.future.get()
return ret
def set_more_pigeon(self, more):
self.prg.assign_external(Function("p"), more)
from clingo import Control
from clingo.ast import parse_string, ProgramBuilder
from clingcon import ClingconTheory
prg = '&sum { x } >= 1. &sum { x } <= 3.'
thy = ClingconTheory()
ctl = Control(['0'])
thy.register(ctl)
with ProgramBuilder(ctl) as bld:
parse_string(prg, lambda ast: thy.rewrite_ast(ast, bld.add))
ctl.ground([('base', [])])
thy.prepare(ctl)
with ctl.solve(yield_=True) as hnd:
for mdl in hnd:
print([f'{key}={val}' for key, val in thy.assignment(mdl.thread_id)])
def solve_instance(self):
prg = Control()
prg.load("puzzle15.lp")
prg.load(self.new_filename)
ret, parts, step = SolveResult.unsatisfiable, [], 1
parts.append(("base", []))
while ret == SolveResult.unsatisfiable:
parts.append(("step", [step]))
parts.append(("check", [step]))
prg.ground(parts)
prg.release_external(Function("query", [step - 1]))
prg.assign_external(Function("query", [step]), True)
#f = lambda m: stdout.write(str(m)+'\n')
print("step:" + str(step) + " Solving...")
ret, parts, step = prg.solve(on_model=self.on_model), [], step + 1
if ret.__repr__() == 'UNSAT':
ret = SolveResult.unsatisfiable
else:
ret = SolveResult.satisfiable
from clingo import Control
from clingo.ast import parse_string, ProgramBuilder
from clingodl import ClingoDLTheory
prg = '&diff { x } >= 1. &diff { y } >= 3.'
thy = ClingoDLTheory()
ctl = Control(['0'])
thy.register(ctl)
with ProgramBuilder(ctl) as bld:
parse_string(prg, lambda ast: thy.rewrite_ast(ast, bld.add))
ctl.ground([('base', [])])
thy.prepare(ctl)
with ctl.solve(yield_=True, on_model=thy.on_model) as hnd:
for mdl in hnd:
print([f'{key}={val}' for key, val in thy.assignment(mdl.thread_id)])
def __init__(self, args):
self.control = Control()
self.args = args
self.horizon = 0
self.objects = 0
self.end = None
def setUp(self):
self.prg = Program()
self.obs = ProgramObserver(self.prg)
self.ctl = Control(message_limit=0)
self.ctl.register_observer(self.obs)
class App:
def __init__(self, args):
self.control = Control()
self.args = args
self.horizon = 0
self.objects = 0
self.end = None
def show(self, model):
if not self.args.quiet:
print("Model: {}".format(model))
def ground(self, kind):
count = self.objects + self.horizon + 1
parts = [("expand", [Number(count)])]
if self.args.scratch and count > 1:
self.control = Control()
for source in self.args.file:
self.control.load(source)
for i in range(0, self.objects):
parts.append(("object", [Number(i + 1, count)]))
for i in range(0, self.horizon):
parts.append(("horizon", [Number(i + 1, count)]))
if self.args.scratch or count == 1:
for option in self.args.option:
setattr(self.control.configuration, option[0], option[1])
parts.append(("base", []))
if kind:
self.objects += 1
parts.append(("object", [Number(self.objects), Number(count)]))
else:
self.horizon += 1
parts.append(("horizon", [Number(self.horizon), Number(count)]))
if self.args.verbose:
print("")
print("Objects: {}".format(Number(self.objects)))
print("Horizon: {}".format(Number(self.horizon)))
self.control.ground(parts)
if self.args.verbose:
print("Solving: {}".format(count))
def run(self):
for source in self.args.file:
self.control.load(source)
if self.args.maxobj is None:
self.end = self.control.get_const("n").number
else:
self.end = self.args.maxobj
while self.objects < self.end:
self.ground(True)
while True:
ret = self.control.solve(on_model=self.show)
if self.args.stats:
args = {
"sort_keys": True,
"indent": 0,
"separators": (',', ': ')
}
stats = {}
for x in [
"step", "enumerated", "time_cpu", "time_solve",
"time_sat", "time_unsat", "time_total"
]:
stats[x] = self.control.statistics[x]
for x in ["lp", "ctx", "solvers"]:
for y in self.control.statistics[x]:
stats[y] = self.control.statistics[x][y]
print(json.dumps(stats, *args))
if ret.satisfiable:
break
self.ground(False)