def test_theory(self):
'''
Test observer via grounding.
'''
ctl = Control()
obs = TestObserverTheory(self)
ctl.register_observer(obs)
ctl.add(
'base', [], '''\
#theory test {
t { };
&a/0 : t, head
}.
{a; b}.
#show t : a, b.
&a { (1,a): a,b }.
''')
ctl.ground([('base', [])])
self.assertIn('output_term', obs.called)
self.assertIn('theory_term_number', obs.called)
self.assertIn('theory_term_string', obs.called)
self.assertIn('theory_term_compound', obs.called)
self.assertIn('theory_element', obs.called)
self.assertIn('theory_atom', obs.called)
ctl.solve()
def test_control(self):
'''
Test observer together with a control object.
'''
ctl = Control()
ctl.register_observer(self.obs)
ctl.add('base', [], '''\
b.
{c}.
a :- b, not c.
#minimize{7@10,a:a; 5@10,c:c}.
#project a.
#project b.
#external a.
''')
ctl.ground([('base', [])])
a, b, c = (Function(s) for s in ('a', 'b', 'c'))
la, lb, lc = (ctl.symbolic_atoms[sym].literal for sym in (a, b, c))
self.prg.sort()
self.assertEqual(self.prg, Program(
output_atoms={la: a, lc: c},
shows=[],
facts=[Fact(symbol=b)],
rules=[Rule(choice=False, head=[lb], body=[]),
Rule(choice=False, head=[la], body=[-lc]),
Rule(choice=True, head=[lc], body=[])],
minimizes=[Minimize(priority=10, literals=[(lc, 5), (la, 7)])],
externals=[External(atom=la, value=TruthValue.False_)],
projects=[Project(atom=lb), Project(atom=la)]).sort())
def test_ground_error(self):
'''
Test grounding with context and parameters.
'''
ctx = Context()
ctl = Control()
ctl.add('part', ['c'], 'p(@cb_error()).')
self.assertRaisesRegex(TestError, 'test', ctl.ground, [('part', [Number(1)])], ctx)
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_simple_stats(self):
'''
Test simple statistics.
'''
ctl = Control(['-t', '2', '--stats=2'])
ctl.add('base', [], '1 { a; b }.')
ctl.ground([('base', [])])
ctl.solve()
stats = ctl.statistics
self.assertGreaterEqual(stats['problem']['lp']['atoms'], 2)
self.assertGreaterEqual(stats['solving']['solvers']['choices'], 1)
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(ctl.solve(on_unsat=lower.append).satisfiable)
self.assertEqual(lower, [[1], [2], [3]])
self.assertEqual(ctl.statistics['summary']['lower'], [3.0])
def test_solve(self):
cc = Control()
cc.add('base', [], '''
a(X) :- not b(X), d(X).
b(X) :- not a(X), d(X).''')
cc.add('base', [], "d(1;2;3).")
cc.ground([("base",[])])
out = {}
def onmodel(m):
out['out'] = str(m)
cc.solve(on_model = onmodel)
self.assertEqual('d(1) d(2) d(3) b(1) b(2) b(3)', out['out'])
def test_user_stats(self):
'''
Test user statistics.
'''
def on_statistics(step, accu):
step['test'] = {'a': 0, 'b': [1, 2], 'c': {'d': 3}}
accu['test'] = step['test']
step['test'] = {
'a': lambda a: a + 1,
'e': lambda a: 4 if a is None else 0,
'b': [-1, 2, 3]
}
self.assertEqual(len(step['test']), 4)
self.assertEqual(len(step['test']['b']), 3)
self.assertEqual(len(step['test']['c']), 1)
self.assertIn('a', step['test'])
self.assertEqual(sorted(step['test']), ['a', 'b', 'c', 'e'])
self.assertEqual(sorted(step['test'].keys()), ['a', 'b', 'c', 'e'])
self.assertEqual(sorted(step['test']['c'].items()), [('d', 3.0)])
self.assertEqual(sorted(step['test']['c'].values()), [3.0])
step['test']['b'][1] = 99
self.assertEqual(step['test']['b'][1], 99)
step['test']['b'].extend([3, 4])
step['test']['b'] += [3, 4]
ctl = Control(['-t', '2', '--stats=2'])
ctl.add('base', [], '1 { a; b }.')
ctl.ground([('base', [])])
ctl.solve(on_statistics=on_statistics)
stats = ctl.statistics
self.assertEqual(
stats['user_step']['test'], {
'a': 1.0,
'b': [-1.0, 99.0, 3.0, 3.0, 4.0, 3.0, 4.0],
'c': {
'd': 3.0
},
'e': 4.0
})
self.assertEqual(stats['user_accu']['test'], {
'a': 0,
'b': [1, 2],
'c': {
'd': 3
}
})
def test_error_handling(self):
'''
Test basic error handling during solving.
'''
ctl = Control()
ctl.add('base', [], '1 {a; b} 1.')
ctl.ground([('base', [])])
self.assertRaises(ZeroDivisionError,
lambda: ctl.solve(on_model=lambda m: 1 / 0))
with ctl.solve(on_model=lambda m: 1 / 0, yield_=True) as handle:
self.assertRaises(ZeroDivisionError, lambda: [_ for _ in handle])
# Note: currently clasp does not store and re-raise the exception in
# asynchronous mode, so we get a runtime error instead
with ctl.solve(on_model=lambda m: 1 / 0, async_=True) as handle:
self.assertRaises(RuntimeError, handle.get)
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']))
def test_theory_with_guard(self):
'''
Test observer via grounding.
'''
ctl = Control()
obs = TestObserverTheoryWithGuard(self)
ctl.register_observer(obs)
ctl.add(
'base', [], '''\
#theory test {
t { };
&a/0 : t, {=}, t, head
}.
&a { } = a.
''')
ctl.ground([('base', [])])
self.assertIn('theory_term_string: a', obs.called)
self.assertIn('theory_term_string: =', obs.called)
self.assertIn('theory_atom_with_guard', obs.called)
ctl.solve()
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 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(.*)')
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 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)
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)