def run(self):
global DEBUG_FLAG
data = self.preprocess_metafunctions()
if DEBUG_FLAG:
self.display_preprocessed_code(data)
model = PrologString(data)
if DEBUG_FLAG:
print '=' * 80
print "BEFORE ESCAPING"
print '=' * 80
for elem in model: print clause2str(elem)
print '=' * 80
model = escape_metafunctions(model)
if DEBUG_FLAG:
print '=' * 80
print "AFTER ESCAPING"
print '=' * 80
for elem in model: print clause2str(elem)
print '=' * 80
engine = DefaultEngine(label_all=True)
engine.add_builtin(METAMETAFUNCTION_FUNCTOR, 2, BooleanBuiltIn(self.builtin_metafunction))
engine.add_builtin('declare', 2, BooleanBuiltIn(self.builtin_declare))
engine.add_builtin('declare', 3, BooleanBuiltIn(self.builtin_declare))
db = engine.prepare(model)
if DEBUG_FLAG:
print "=" * 80
print "DATABASE"
print "=" * 80
for elem in db: print elem
print "=" * 80
gp = LogicFormula(
keep_all=True,
keep_order=True,
keep_duplicates=True,
avoid_name_clash=True
)
gp = engine.ground_all(db, target=gp)
if DEBUG_FLAG:
print "=" * 80
print "GROUND PROGRAM (GROUNDER)"
print "=" * 80
for elem in gp.enum_clauses():
print elem
print "=" * 80
clauses = []
facts = []
for clause in unescape_metafunctions(gp.enum_clauses()):
if isinstance(clause, Clause):
clauses.append(clause)
else:
facts.append(clause)
query_atoms = gp._names['query']
return self.builtin_declare.declarations, clauses + facts, query_atoms
def sample( filename, N=1, with_facts=False, oneline=False ) :
pl = PrologFile(filename)
engine = DefaultEngine()
db = engine.prepare(pl)
for i in range(0, N) :
result = engine.ground_all(db, target=SampledFormula())
print ('====================')
print (result.toString(db, with_facts, oneline))
def estimate( filename, N=1 ) :
from collections import defaultdict
pl = PrologFile(filename)
engine = DefaultEngine()
db = engine.prepare(pl)
estimates = defaultdict(float)
counts = 0.0
for i in range(0, N) :
result = engine.ground_all(db, target=SampledFormula())
for k, v in result.queries() :
if v == 0 :
estimates[k] += 1.0
counts += 1.0
for k in estimates :
estimates[k] = estimates[k] / counts
return estimates
def main(filename, output):
model = PrologFile(filename)
engine = DefaultEngine(label_all=True)
with Timer("parsing"):
db = engine.prepare(model)
print("\n=== Database ===")
print(db)
print("\n=== Queries ===")
queries = engine.query(db, Term("query", None))
print("Queries:", ", ".join([str(q[0]) for q in queries]))
print("\n=== Evidence ===")
evidence = engine.query(db, Term("evidence", None, None))
print("Evidence:", ", ".join(["%s=%s" % ev for ev in evidence]))
print("\n=== Ground Program ===")
with Timer("ground"):
gp = engine.ground_all(db)
print(gp)
print("\n=== Acyclic Ground Program ===")
with Timer("acyclic"):
gp = LogicDAG.createFrom(gp)
print(gp)
print("\n=== Conversion to CNF ===")
with Timer("convert to CNF"):
cnf = CNF.createFrom(gp)
with open(output, "w") as f:
f.write(cnf.to_dimacs(weighted=False, names=True))
class AIController(PlayerController):
'''Intelligenza artificiale
Contiene i metodi per la gestione delle decisioni prese dal maziere
'''
''' conoscenza del dt_problog'''
conoscenza = []
''' conoscenza del problog (calcolo inferenza) '''
conoscenza_prob = []
vincita_attuale = 0
def __init__(self, nome, soldi, prolog):
'''COSTRUTTORE
chiama il costruttore della superclasse PlayerController ed istanzia gli oggetti per la gestione delle query problog
:type nome: string
:param nome: nome del giocatore
:type soldi: float
:param soldi: soldi del giocatore
:type prolog: PrologController
:param prolog: Oggetto PrologController che estende la classe Prolog del modulo pyswip
'''
super(AIController, self).__init__(nome, soldi, prolog)
self.read_file(path='controller/dt_problog.pl',
path1='controller/knowledge_problog.pl')
self.learn()
def read_file(self, path, path1):
'''Legge i file .pl contenenti la conoscenza problog e crea una lista contenente tutti i predicati
'''
try:
with open(path) as kn_file:
self.conoscenza = kn_file.read()
with open(path1) as kn_file:
for row in kn_file:
self.conoscenza_prob.append(row.rstrip('\n'))
self.conoscenza_prob = list(
filter(('').__ne__, self.conoscenza_prob))
except Exception as e:
print(e)
def learn(self):
'''Construisce gli oggetti problog per la valutazione dell'inferenza'''
try:
knowledge_str = ''
for predicate in self.conoscenza_prob:
knowledge_str += predicate + '\n'
knowledge_str = PrologString(knowledge_str)
self.problog = DefaultEngine()
self.knowledge_database = self.problog.prepare(knowledge_str)
except Exception as e:
print(e)
def get_used_card_evidence(self, prolog):
'''construisce la lista contenente le stringhe delle evidenze per il DTProblog'''
ret_list = []
for card in prolog.uscite:
ret_list.append('evidence(not ' + str(card) + ').\n')
return ret_list
def get_utility(self, prolog):
'''constuisce la lista delle utility per il DTProblog'''
vs_score, utility = prolog.get_utility()
ret_list = []
for i in range(len(vs_score)):
ret_list.append(
f'utility(vinco({self.punteggio},{vs_score[i]}), {utility[i]}).\n'
)
ret_list.append(
f'utility(perdo({self.punteggio},{vs_score[i]}), {-1*utility[i]}).\n'
)
ret_list.append(
f'utility(sballo({self.punteggio}), {-1*prolog.get_winnable_bet()}).\n'
)
vincita, _ = prolog.get_gain()
prob_di_migliorare = self.query('prob_di_migliorare',
self.punteggio,
evidence=self.get_used_card(prolog))
util = 0
for i in range(len(vincita)):
util += prob_di_migliorare * vincita[i]
ret_list.append(f'utility(miglioro({self.punteggio}), {util}).\n')
'''
prob_di_migliorare = self.query('prob_di_migliorare',self.punteggio,evidence = self.get_used_card(prolog))
for i in range(len(vincita)):
ret_list.append(f'utility(miglioro({self.punteggio}), {prob_di_migliorare*vincita[i]}).\n')
'''
return ret_list
def decidi(self, prolog):
'''prende la decisione'''
temp = self.conoscenza
evidence = self.get_used_card_evidence(prolog)
for ev in evidence:
temp += ev
utilities = self.get_utility(prolog)
for ut in utilities:
temp += ut
program = PrologString(temp)
decisions, _, _ = dtproblog(program)
for _, value in decisions.items():
if value == 1:
return True
print('STO\n')
return False
def eval_query(self, term, *args, **kwargs):
try:
if args:
t = '\'' + term + '\''
termine = f'Term({t},'
for termine_arg in args:
if isinstance(termine_arg, str):
termine += 'Term(\'' + termine_arg + '\')' + ','
elif isinstance(termine_arg, int) or isinstance(
termine_arg, float):
termine += 'Constant(' + str(termine_arg) + ')' + ','
termine = termine[:-1] + ')'
query_term = eval(termine)
else:
query_term = Term(term)
evidenze = []
if kwargs:
for i in kwargs['evidence']:
# Term(Constant(1),Term('spade'))
i = i.replace('card(',
'').replace(' ', '').replace(')',
'').split(',')
term_str = f'Term(\'card\',Constant({i[0]}), Term(\'{i[1]}\'))'
tupla = (eval(term_str), False)
evidenze.append(tupla)
lf = self.problog.ground_all(self.knowledge_database,
queries=[query_term],
evidence=evidenze)
return get_evaluatable().create_from(lf).evaluate()
except Exception as e:
print(e)
print("QUESTA SOPRA è L'ECCEZIONE")
return None
def query(self, term, *args, **kwargs):
# print(ai.query('prob_di_sballare', 7, evidence=['card(2,bastoni)']))
query_result = self.eval_query(term, *args, **kwargs)
if len(query_result) > 1:
res = []
for prob_res in query_result.values():
res.append(prob_res)
else:
for prob_res in query_result.values():
res = prob_res
return res
def get_used_card(self, prolog):
'''ritorna una lista di stringhe contenenti le carte uscite'''
uscite = []
for card in prolog.uscite:
uscite.append(str(card))
return uscite
def main(filename, with_dot, knowledge):
dotprefix = None
if with_dot:
dotprefix = os.path.splitext(filename)[0] + "_"
model = PrologFile(filename)
engine = DefaultEngine(label_all=True)
with Timer("parsing"):
db = engine.prepare(model)
print("\n=== Database ===")
print(db)
print("\n=== Queries ===")
queries = engine.query(db, Term("query", None))
print("Queries:", ", ".join([str(q[0]) for q in queries]))
print("\n=== Evidence ===")
evidence = engine.query(db, Term("evidence", None, None))
print("Evidence:", ", ".join(["%s=%s" % ev for ev in evidence]))
print("\n=== Ground Program ===")
with Timer("ground"):
gp = engine.ground_all(db)
print(gp)
if dotprefix != None:
with open(dotprefix + "gp.dot", "w") as f:
print(gp.toDot(), file=f)
print("\n=== Acyclic Ground Program ===")
with Timer("acyclic"):
gp = gp.makeAcyclic()
print(gp)
if dotprefix != None:
with open(dotprefix + "agp.dot", "w") as f:
print(gp.toDot(), file=f)
if knowledge == "sdd":
print("\n=== SDD compilation ===")
with Timer("compile"):
nnf = SDD.createFrom(gp)
if dotprefix != None:
nnf.saveSDDToDot(dotprefix + "sdd.dot")
else:
print("\n=== Conversion to CNF ===")
with Timer("convert to CNF"):
cnf = CNF.createFrom(gp)
print("\n=== Compile to d-DNNF ===")
with Timer("compile"):
nnf = DDNNF.createFrom(cnf)
if dotprefix != None:
with open(dotprefix + "nnf.dot", "w") as f:
print(nnf.toDot(), file=f)
print("\n=== Evaluation result ===")
with Timer("evaluate"):
result = nnf.evaluate()
for it in result.items():
print("%s : %s" % (it))
class Engine(object):
"""
Adapter class to Problog grounding and query engine.
:param program: a valid MDP-ProbLog program
:type program: str
"""
def __init__(self, program):
self._engine = DefaultEngine()
self._db = self._engine.prepare(PrologString(program))
self._gp = None
self._knowledge = None
def declarations(self, declaration_type):
"""
Return a list of all terms of type `declaration_type`.
:param declaration_type: declaration type.
:type declaration_type: str
:rtype: list of problog.logic.Term
"""
return [
t[0]
for t in self._engine.query(self._db, Term(declaration_type, None))
]
def assignments(self, assignment_type):
"""
Return a dictionary of assignments of type `assignment_type`.
:param assignment_type: assignment type.
:type assignment_type: str
:rtype: dict of (problog.logic.Term, problog.logic.Constant) items.
"""
return dict(
self._engine.query(self._db, Term(assignment_type, None, None)))
def get_instructions_table(self):
"""
Return the table of instructions separated by instruction type
as described in problog.engine.ClauseDB.
:rtype: dict of (str, list of (node,namedtuple))
"""
instructions = {}
for node, instruction in enumerate(self._db._ClauseDB__nodes):
instruction_type = str(instruction)
instruction_type = instruction_type[:instruction_type.find('(')]
if instruction_type not in instructions:
instructions[instruction_type] = []
assert (self._db.get_node(node) == instruction) # sanity check
instructions[instruction_type].append((node, instruction))
return instructions
def add_fact(self, term, probability=None):
"""
Add a new `term` with a given `probability` to the program database.
Return the corresponding node number.
:param term: a predicate
:type term: problog.logic.Term
:param probability: a number in [0,1]
:type probability: float
:rtype: int
"""
return self._db.add_fact(term.with_probability(Constant(probability)))
def get_fact(self, node):
"""
Return the fact in the table of instructions corresponding to `node`.
:param node: identifier of fact in table of instructions
:type node: int
:rtype: problog.engine.fact
"""
fact = self._db.get_node(node)
if not str(fact).startswith('fact'):
raise IndexError('Node `%d` is not a fact.' % node)
return fact
def add_rule(self, head, body):
"""
Add a new rule defined by a `head` and `body` arguments
to the program database. Return the corresponding node number.
:param head: a predicate
:type head: problog.logic.Term
:param body: a list of literals
:type body: list of problog.logic.Term or problog.logic.Not
:rtype: int
"""
b = body[0]
for term in body[1:]:
b = b & term
rule = head << b
return self._db.add_clause(rule)
def get_rule(self, node):
"""
Return the rule in the table of instructions corresponding to `node`.
:param node: identifier of rule in table of instructions
:type node: int
:rtype: problog.engine.clause
"""
rule = self._db.get_node(node)
if not str(rule).startswith('clause'):
raise IndexError('Node `%d` is not a rule.' % node)
return rule
def add_assignment(self, term, value):
"""
Add a new utility assignment of `value` to `term` in the program database.
Return the corresponding node number.
:param term: a predicate
:type term: problog.logic.Term
:param value: a numeric value
:type value: float
:rtype: int
"""
args = (term.with_probability(None), Constant(1.0 * value))
utility = Term('utility', *args)
return self._db.add_fact(utility)
def get_assignment(self, node):
"""
Return the assignment in the table of instructions corresponding to `node`.
:param node: identifier of assignment in table of instructions
:type node: int
:rtype: pair of (problog.logic.Term, problog.logic.Constant)
"""
fact = self._db.get_node(node)
if not (str(fact).startswith('fact') and fact.functor == 'utility'):
raise IndexError('Node `%d` is not an assignment.' % node)
return (fact.args[0], fact.args[1])
def add_annotated_disjunction(self, facts, probabilities):
"""
Add a new annotated disjunction to the program database from
a list of `facts` and its `probabilities`.
Return a list of choice nodes.
:param facts: list of probabilistic facts
:type facts: list of problog.logic.Term
:param probabilities: list of valid individual probabilities
such that the total probability is less
than or equal to 1.0
:type probabilities: list of float in [0.0, 1.0]
:rtype: list of int
"""
disjunction = [
f.with_probability(Constant(p))
for f, p in zip(facts, probabilities)
]
self._db += AnnotatedDisjunction(heads=disjunction,
body=Constant('true'))
choices = []
for node, term in enumerate(self._db._ClauseDB__nodes):
if str(term).startswith('choice'):
choices.append((term, node))
nodes = []
for term in disjunction:
term = term.with_probability(None)
for choice, node in choices:
if term in choice.functor.args:
nodes.append(node)
return nodes
def get_annotated_disjunction(self, nodes):
"""
Return the list of choice nodes in the table of instructions
corresponding to `nodes`.
:param nodes: list of node identifiers
:type nodes: list of int
:rtype: list of problog.engine.choice
"""
choices = [self._db.get_node(node) for node in nodes]
for choice in choices:
if not str(choice).startswith('choice'):
raise IndexError('Node `%d` is not a choice node.' % choice)
return choices
def relevant_ground(self, queries):
"""
Create ground program with respect to `queries`.
:param queries: list of predicates
:type queries: list of problog.logic.Term
"""
self._gp = self._engine.ground_all(self._db, queries=queries)
def compile(self, terms=[]):
"""
Create compiled knowledge database from ground program.
Return mapping of `terms` to nodes in the compiled knowledge database.
:param terms: list of predicates
:type terms: list of problog.logic.Term
:rtype: dict of (problog.logic.Term, int)
"""
self._knowledge = get_evaluatable(None).create_from(self._gp)
term2node = {}
for term in terms:
term2node[term] = self._knowledge.get_node_by_name(term)
return term2node
def evaluate(self, queries, evidence):
"""
Compute probabilities of `queries` given `evidence`.
:param queries: mapping of predicates to nodes
:type queries: dict of (problog.logic.Term, int)
:param evidence: mapping of predicate and evidence weight
:type evidence: dictionary of (problog.logic.Term, {0, 1})
:rtype: list of (problog.logic.Term, [0.0, 1.0])
"""
evaluator = self._knowledge.get_evaluator(semiring=None,
evidence=None,
weights=evidence)
return [(query, evaluator.evaluate(queries[query]))
for query in sorted(queries, key=str)]
def sample_object(pl, N=1):
engine = DefaultEngine()
db = engine.prepare(pl)
result = [engine.ground_all(db, target=SampledFormula()) for i in range(N)]
return result, db
class GDLIIIProblogRep(object):
def __init__(self, program, fformat):
#GlobalEngine
self._engine = DefaultEngine()
gdl_parser = GDLIIIParser()
self._model = gdl_parser.output_model(program, fformat)
self._baseModelFile = self._model.as_problog()
self._playerList = []
self._randomIdentifier = Constant(0) #Hardcoded to give the random player a specific constant id as we apply some special rules to the random player
worlds = self._initialiseKB()
self._cur_node = GDLNode(worlds, GameData(self._playerList, self._randomIdentifier))
self._moveList = dict([(i,None) for i in self._playerList])
self.terminal = False
def getMoveList(self):
return self._moveList
def undo(self, increment=1):
for _ in range(increment):
self._cur_node = self._cur_node.parent
self._moveList = dict([(i,None) for i in self._playerList])
if self.terminal:
self.terminal = False
def getLegalMovesForPlayer(self, player):
return self._cur_node.legal_moves[player]
def _resetKnowledgeBase(self):
self._kb = self._engine.prepare(self._baseModelFile)
def getPlayersPossibleWorlds(self, player):
return self._cur_node.worlds[player]
#Assumption, term contains a single argument
def extractSingleArg(self,nArg, term):
return term.args[nArg]
#Recommended to never call this function with step > 1 as it will likely take a long time, exponential time complexity for values of step > 0.
def query(self, player, query, step=0):
if step == 0:
return self._cur_node.raw_query(player, Term('thinks', player, query))
else:
world_set = set([self._cur_node])
for _ in range(step):
#Create set of all possible move sequences from perspective of player
action_set = set()
for world in world_set:
action_set = action_set.union(set([i for i in world.get_legal_moves()[player].keys()]))
legal_moves_seqs = [{k:(None if k != player else a) for k in self._playerList} for a in action_set]
#Generate possible (but not always valid) successor worlds
new_set = set()
for world in world_set:
for actions in legal_moves_seqs:
new_set.add(world.generate_speculative_worlds(player, actions))
world_set = new_set
query_dict = {}
size = len(world_set)
for w in world_set:
for (item,val) in w.raw_query(player, Term('thinks', player, query)).items():
if item in query_dict.keys():
query_dict[item] += val/size
else:
query_dict[item] = val/size
return query_dict
#Private
def _initialiseKB(self):
self._kb = self._engine.prepare(self._baseModelFile)
initialState = \
set(map(lambda a: Term('ptrue', a[0].args[0]), self._engine.ground_all(self._kb, queries=[Term('init',Var('_'))]).get_names()))
players = \
set(map(lambda a: a[0], self._engine.ground_all(self._kb, queries=[Term('role',Var('_'))]).get_names()))
#Not needed, but dont care to remove right now
self._step = 0
playerWorldState = {}
for playerNum in map(lambda a: a.args[0], players):
knowledge = map(lambda a: Term('thinks', playerNum, a.args[0]), initialState)
playerPreds = initialState.union(set(knowledge))
self._playerList.append(playerNum)
#Each player starts with a single initial world
if playerNum == self._randomIdentifier:
#Random Specific world has no thinks predicates
playerWorldState[playerNum] = [RandomWorld(self._engine, self._baseModelFile, self._step, 1, initialState, playerNum)]
else:
playerWorldState[playerNum] = [World(self._engine, self._baseModelFile, self._step, 1, playerPreds, playerNum)]
return playerWorldState
def applyActionsToModelAndUpdate(self):
if (None in self._moveList.values()):
raise Exception("Error: Must have submitted moves for all players before proceeding")
self._cur_node = self._cur_node.get_next_node(self._moveList)
#Assume, there is at least one player
if len([(k,v) for (k,v) in self._cur_node.raw_query(\
self._playerList[0], Term('terminal')).items() if v > 0]) > 0:
self.terminal = True
self._step += 1
self._moveList = dict([(i,None) for i in self._playerList])
def submitAction(self, action, player):
if ( action not in self.getLegalMovesForPlayer(player)):
raise Exception("{} is not a legal action".format(action))
self._moveList[player] = action
db = engine.prepare(pl)
print(db)
query_term = sibling(tom, sally)
res = engine.query(db, query_term)
print ('%s? %s' % (query_term, bool(res)))
query_term = sibling(sally, erica)
res = engine.query(db, query_term)
print(res)
print ('%s? %s' % (query_term, bool(res)))
# NOTE: variables can be replaced by None of a negative number
# the difference is that each None is a different variable,
# while each variable with the same negative number is the same variable
query_term = sibling(None, None)
res = engine.query(db, query_term)
for args in res:
print(query_term(*args))
print('siblings of sally:')
query_term = Term('sibling', Term('sally'), None)
res = engine.query(db, query_term)
for args in res:
print(query_term(*args))
print(engine.ground_all(db, queries=[query_term]))
class Engine(object):
"""
Adapter class to Problog grounding and query engine.
:param program: a valid MDP-ProbLog program
:type program: str
"""
def __init__(self, program):
self._engine = DefaultEngine()
self._db = self._engine.prepare(PrologString(program))
self._gp = None
self._knowledge = None
def declarations(self, declaration_type):
"""
Return a list of all terms of type `declaration_type`.
:param declaration_type: declaration type.
:type declaration_type: str
:rtype: list of problog.logic.Term
"""
return [t[0] for t in self._engine.query(self._db, Term(declaration_type, None))]
def assignments(self, assignment_type):
"""
Return a dictionary of assignments of type `assignment_type`.
:param assignment_type: assignment type.
:type assignment_type: str
:rtype: dict of (problog.logic.Term, problog.logic.Constant) items.
"""
return dict(self._engine.query(self._db, Term(assignment_type, None, None)))
def get_instructions_table(self):
"""
Return the table of instructions separated by instruction type
as described in problog.engine.ClauseDB.
:rtype: dict of (str, list of (node,namedtuple))
"""
instructions = {}
for node, instruction in enumerate(self._db._ClauseDB__nodes):
instruction_type = str(instruction)
instruction_type = instruction_type[:instruction_type.find('(')]
if instruction_type not in instructions:
instructions[instruction_type] = []
assert(self._db.get_node(node) == instruction) # sanity check
instructions[instruction_type].append((node, instruction))
return instructions
def add_fact(self, term, probability=None):
"""
Add a new `term` with a given `probability` to the program database.
Return the corresponding node number.
:param term: a predicate
:type term: problog.logic.Term
:param probability: a number in [0,1]
:type probability: float
:rtype: int
"""
return self._db.add_fact(term.with_probability(Constant(probability)))
def get_fact(self, node):
"""
Return the fact in the table of instructions corresponding to `node`.
:param node: identifier of fact in table of instructions
:type node: int
:rtype: problog.engine.fact
"""
fact = self._db.get_node(node)
if not str(fact).startswith('fact'):
raise IndexError('Node `%d` is not a fact.' % node)
return fact
def add_rule(self, head, body):
"""
Add a new rule defined by a `head` and `body` arguments
to the program database. Return the corresponding node number.
:param head: a predicate
:type head: problog.logic.Term
:param body: a list of literals
:type body: list of problog.logic.Term or problog.logic.Not
:rtype: int
"""
b = body[0]
for term in body[1:]:
b = b & term
rule = head << b
return self._db.add_clause(rule)
def get_rule(self, node):
"""
Return the rule in the table of instructions corresponding to `node`.
:param node: identifier of rule in table of instructions
:type node: int
:rtype: problog.engine.clause
"""
rule = self._db.get_node(node)
if not str(rule).startswith('clause'):
raise IndexError('Node `%d` is not a rule.' % node)
return rule
def add_assignment(self, term, value):
"""
Add a new utility assignment of `value` to `term` in the program database.
Return the corresponding node number.
:param term: a predicate
:type term: problog.logic.Term
:param value: a numeric value
:type value: float
:rtype: int
"""
args = (term.with_probability(None), Constant(1.0 * value))
utility = Term('utility', *args)
return self._db.add_fact(utility)
def get_assignment(self, node):
"""
Return the assignment in the table of instructions corresponding to `node`.
:param node: identifier of assignment in table of instructions
:type node: int
:rtype: pair of (problog.logic.Term, problog.logic.Constant)
"""
fact = self._db.get_node(node)
if not (str(fact).startswith('fact') and fact.functor == 'utility'):
raise IndexError('Node `%d` is not an assignment.' % node)
return (fact.args[0], fact.args[1])
def add_annotated_disjunction(self, facts, probabilities):
"""
Add a new annotated disjunction to the program database from
a list of `facts` and its `probabilities`.
Return a list of choice nodes.
:param facts: list of probabilistic facts
:type facts: list of problog.logic.Term
:param probabilities: list of valid individual probabilities
such that the total probability is less
than or equal to 1.0
:type probabilities: list of float in [0.0, 1.0]
:rtype: list of int
"""
disjunction = [ f.with_probability(Constant(p)) for f, p in zip(facts, probabilities) ]
self._db += AnnotatedDisjunction(heads=disjunction, body=Constant('true'))
choices = []
for node, term in enumerate(self._db._ClauseDB__nodes):
if str(term).startswith('choice'):
choices.append((term, node))
nodes = []
for term in disjunction:
term = term.with_probability(None)
for choice, node in choices:
if term in choice.functor.args:
nodes.append(node)
return nodes
def get_annotated_disjunction(self, nodes):
"""
Return the list of choice nodes in the table of instructions
corresponding to `nodes`.
:param nodes: list of node identifiers
:type nodes: list of int
:rtype: list of problog.engine.choice
"""
choices = [ self._db.get_node(node) for node in nodes ]
for choice in choices:
if not str(choice).startswith('choice'):
raise IndexError('Node `%d` is not a choice node.' % choice)
return choices
def relevant_ground(self, queries):
"""
Create ground program with respect to `queries`.
:param queries: list of predicates
:type queries: list of problog.logic.Term
"""
self._gp = self._engine.ground_all(self._db, queries=queries)
def compile(self, terms=[]):
"""
Create compiled knowledge database from ground program.
Return mapping of `terms` to nodes in the compiled knowledge database.
:param terms: list of predicates
:type terms: list of problog.logic.Term
:rtype: dict of (problog.logic.Term, int)
"""
self._knowledge = get_evaluatable(None).create_from(self._gp)
term2node = {}
for term in terms:
term2node[term] = self._knowledge.get_node_by_name(term)
return term2node
def evaluate(self, queries, evidence):
"""
Compute probabilities of `queries` given `evidence`.
:param queries: mapping of predicates to nodes
:type queries: dict of (problog.logic.Term, int)
:param evidence: mapping of predicate and evidence weight
:type evidence: dictionary of (problog.logic.Term, {0, 1})
:rtype: list of (problog.logic.Term, [0.0, 1.0])
"""
evaluator = self._knowledge.get_evaluator(semiring=None, evidence=None, weights=evidence)
return [ (query, evaluator.evaluate(queries[query])) for query in sorted(queries, key=str) ]
