def main_cross_validation(fname_examples: str, fname_settings: str, fname_background: str,
dir_fold_files: str, fname_prefix_fold: str, fold_start_index: int, nb_folds: int,
fold_suffix: str, dir_output_files: str,
filter_out_unlabeled_examples=False,
debug_printing_example_parsing=False,
debug_printing_tree_building=False,
debug_printing_tree_pruning=False,
debug_printing_program_conversion=False,
debug_printing_get_classifier=False,
debug_printing_classification=False):
engine = DefaultEngine()
engine.unknown = 1
fd = FoldData.build_fold_data(fname_examples, fname_settings, fname_background,
dir_fold_files, fname_prefix_fold, fold_start_index, nb_folds, fold_suffix,
dir_output_files,
filter_out_unlabeled_examples,
debug_printing_example_parsing,
debug_printing_tree_building,
debug_printing_tree_pruning,
debug_printing_program_conversion,
debug_printing_get_classifier,
debug_printing_classification,
engine=engine
)
# take one key set as test, the others as training
for fold_index, test_key_set in enumerate(fd.all_key_sets):
do_one_fold(fold_index, test_key_set, fd)
do_all_examples(fd)
def run_eval_neg(filename, **other):
from .data import read_data, concat, Interpretations, Instance
from problog.program import PrologString
# from problog.logic import AnnotatedDisjunction, Clause
print("starting eval")
data = read_data(filename)
rules = concat(data['RULES'])
engine = DefaultEngine()
engine.prepare(PrologString(':- unknown(fail).'))
background_pl = concat(data.get('BACKGROUND', []))
examples = data.get('!', [])
examples_db = [
engine.prepare(PrologString(background_pl + example_pl))
for example_pl in examples
]
instances = Interpretations(
[Instance(example_db) for example_db in examples_db],
PrologString(background_pl))
for rule in PrologString(rules):
clause = Clause.from_logic(rule)
print('Evaluation of rule:', clause)
if not clause.validate(instances, engine):
print('\tRule is invalid')
#for ex, success in enumerate(clause.successes):
# if not success:
# print('\t\tExample %s:' % (ex + 1), success)
else:
print('\tRule is valid.')
def __init__(self,
pos_examples,
neg_examples,
extra_terms=[],
target_name='target'):
# Define the language of terms
self.target = Term(target_name)
self.equal = Term('equal')
self.pos_examples = pos_examples
self.neg_examples = neg_examples
self.examples = pos_examples + neg_examples
self.extra_terms = extra_terms
#TODO: check extra terms arity, if greater than target arity, create more variables
n_target_variables = len(self.examples[0])
target_variables_names = [
'X' + str(i) for i in range(1, n_target_variables + 1)
]
self.X = list(map(Var, target_variables_names))
constants = set()
for example in self.examples:
constants.update(example)
self.c = list(map(Term, [str(constant) for constant in constants]))
# Initialize the logic program
self.pl = SimpleProgram()
self.pl += self.equal(self.X[0], self.X[0])
self.pl += self.target(*tuple(self.X))
for extra_term in self.extra_terms:
self.pl += PrologString(extra_term)
self.predicates = [self.equal] # + list(extra_terms.keys())
self.engine = DefaultEngine()
self.db = self.engine.prepare(self.pl)
self.original_rule = list(self.pl)[1]
self.new_body_literals = []
print(list(self.pl))
def main():
p = PrologString("""
mother_child(trude, sally).
father_child(tom, sally).
father_child(tom, erica).
father_child(mike, tom).
sibling(X, Y) :- parent_child(Z, X), parent_child(Z, Y).
parent_child(X, Y) :- father_child(X, Y).
parent_child(X, Y) :- mother_child(X, Y).
""")
sibling = Term('sibling')
query_term = sibling(None, None)
engine = DefaultEngine()
# prepare the model for querying
model_db = engine.prepare(p) # This compiles the Prolog model into an internal format.
# This step is optional, but it might be worthwhile if you
# want to query the same model multiple times.
times_query = test_query_method1(engine, model_db, query_term)
times_query_extended = test_query_method2(engine, model_db, query_term)
print("average duration query:", statistics.mean(times_query), "seconds")
print("average duration query:", statistics.mean(times_query_extended), "seconds")
def _run_tests():
from problog.program import PrologString
from problog.engine import DefaultEngine
p = PrologString("""
coin(c1). coin(c2).
0.4::heads(C); 0.6::tails(C) :- coin(C).
win :- heads(C).
""")
engine = DefaultEngine()
db = engine.prepare(p)
# tasks = [
# ([Term("win")], []),
# ([Term("win")], [(Term("heads", Term("c1")), True)]),
# ([Term("win")], [(Term("heads", Term("c1")), False)]),
# ]
# for q,e in tasks:
# qs.prepare_query(q, e)
# print(qs.evaluate_queries())
qs = QuerySession(engine, db)
inline_queries = [" win | heads(c1).", "win | \+heads(c1).", "win."]
for iq in inline_queries:
q, e = qs.transform_inline_query(PrologString(iq)[0])
qs.prepare_query(q, e)
result = qs.evaluate_queries()
print(result)
def __init__(self, *sources):
self._database = DefaultEngine().prepare(sources[0])
# pdb.set_trace()
for source in sources[1:]:
for clause in source:
# pdb.set_trace()
self._database += clause
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 init_engine(**kwdargs):
engine = DefaultEngine(**kwdargs)
engine.add_builtin("sample", 2, builtin_sample)
engine.add_builtin("value", 2, builtin_sample)
engine.add_builtin("previous", 2, builtin_previous)
engine.previous_result = None
return engine
def init_engine(**kwdargs):
engine = DefaultEngine(**kwdargs)
engine.add_builtin('sample', 2, builtin_sample)
engine.add_builtin('value', 2, builtin_sample)
engine.add_builtin('previous', 2, builtin_previous)
engine.previous_result = None
return engine
def do_inference(a, b, terms, program):
engine = DefaultEngine()
xs = engine.prepare(program)
print("[%s]是[%s]的 --> " % (b, a), end="")
for key in terms:
query_term = terms[key](a, b)
res = engine.query(xs, query_term)
if bool(res):
print(terms[key], end=" ")
print("")
def test_compare(self) :
"""Comparison operator"""
program = """
morning(Hour) :- Hour >= 6, Hour =< 10.
"""
engine = DefaultEngine()
db = engine.prepare( PrologString(program) )
self.assertEqual( list(map(list,engine.query(db, Term('morning', Constant(8)) ))), [[8]])
def extract_evidence(pl):
engine = DefaultEngine()
atoms = engine.query(pl, Term('evidence', None, None))
atoms1 = engine.query(pl, Term('evidence', None))
atoms2 = engine.query(pl, Term('observe', None))
for atom in atoms1 + atoms2:
atom = atom[0]
if atom.is_negated():
atoms.append((-atom, Term('false')))
else:
atoms.append((atom, Term('true')))
return [(at, str2bool(vl)) for at, vl in atoms]
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 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_labels_single_example_models(example: SimpleProgram,
rules: SimpleProgram,
possible_labels: Iterable[str],
background_knowledge=None,
debug_printing=False) -> List[str]:
"""
Classifies a single example and returns a list of its labels
:param example:
:param rules:
:param possible_labels:
:return:
"""
eng = DefaultEngine()
eng.unknown = 1
if background_knowledge is not None:
db = eng.prepare(background_knowledge)
for statement in example:
db += statement
for rule in rules:
db += rule
else:
db = eng.prepare(rules)
for statement in example:
db += statement
if debug_printing:
print('\nQueried database:')
for statement in db:
print('\t' + str(statement))
# print('\n')
result_list = []
for label in possible_labels:
db_to_query = db.extend()
db_to_query += Term('query')(label)
start_time = time.time()
result = problog.get_evaluatable().create_from(db_to_query,
engine=eng).evaluate()
end_time = time.time()
print("call time:", end_time - start_time)
if result[label] > 0.5:
result_list.append(label)
return result_list
class LabelCollector:
""""
IMPORTANT:
* ONLY for KEYS format
* has a lot of overlap with Classifier
"""
def __init__(self,
predicate_to_query,
index_of_label_var,
background_knowledge=None,
debug_printing=False,
engine: GenericEngine = None):
if engine is None:
self.engine = DefaultEngine()
self.engine.unknown = 1
else:
self.engine = engine
if background_knowledge is not None:
self.db = self.engine.prepare(background_knowledge)
else:
self.db = None
self.predicate_to_query = predicate_to_query
self.labels = set() # type: Set[Label]
self.index_of_label_var = index_of_label_var
self.debug_printing = debug_printing
def get_labels(self) -> Set[Label]:
return self.labels
def extract_labels(self, example_collection: ExampleCollection):
raise NotImplementedError('Abstract method')
def __init__(self, engine: GenericEngine = None):
if engine is None:
self.engine = DefaultEngine()
self.engine.unknown = 1
else:
self.engine = engine
self.to_query = Term('to_query')
def __init__(self,
fname_prefix_fold,
nb_folds,
dir_output_files,
debug_printing_example_parsing=False,
debug_printing_tree_building=False,
debug_printing_tree_pruning=False,
debug_printing_program_conversion=False,
debug_printing_get_classifier=False,
debug_printing_classification=False,
engine: GenericEngine = None):
self.fname_prefix_fold = fname_prefix_fold
self.nb_folds = nb_folds
self.dir_output_files = dir_output_files
self.dir_output_files = dir_output_files
self.debug_printing_example_parsing = debug_printing_example_parsing
self.debug_printing_tree_building = debug_printing_tree_building
self.debug_printing_tree_pruning = debug_printing_tree_pruning
self.debug_printing_program_conversion = debug_printing_program_conversion
self.debug_printing_get_classifier = debug_printing_get_classifier
self.debug_printing_classification = debug_printing_classification
if engine is None:
self.engine = DefaultEngine()
self.engine.unknown = 1
else:
self.engine = engine
def test_cycle_goodcode(self):
N = 20
program = self.program_v1[:]
for i in range(0, N):
seed = str(random.random())[2:]
random.seed(seed)
random.shuffle(program)
txt = "\n".join(program)
f = DefaultEngine(label_all=True).ground_all(PrologString(txt))
paths = list(list_paths(f))
edges = set()
for p in paths:
for i in range(0, len(p) - 1):
edges.add((int(p[i]), int(p[i + 1])))
edges = list(sorted(edges))
# if (edges != self.edges) :
# with open('cycle_error.pl', 'w') as f :
# print(txt, file=f)
# with open('cycle_error.dot', 'w') as f :
# print('digraph CycleError {', file=f)
# for edge in edges :
# print('%s -> %s;' % edge, file=f)
# print('}', file=f)
self.assertCollectionEqual(self.edges,
edges,
msg="Test failed for random seed %s" %
seed)
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 get_full_background_knowledge_clausedb(self, engine=None) -> ClauseDB:
if self.full_background_knowledge_clausedb is not None:
return self.full_background_knowledge_clausedb
else:
if engine is None:
engine = DefaultEngine()
engine.unknown = 1
full_bg_kw = self.get_full_background_knowledge_simple_program()
if full_bg_kw is not None:
self.full_background_knowledge_clausedb = engine.prepare(
full_bg_kw) # ClauseDB
return self.full_background_knowledge_clausedb
else:
raise Exception(
"No sense in making an empty ClauseDB for an empty background knowledge"
)
def test_nonground_query_ad(self):
"""Non-ground call to annotated disjunction"""
program = """
0.1::p(a); 0.2::p(b).
query(p(_)).
"""
engine = DefaultEngine()
db = engine.prepare(PrologString(program))
result = None
for query in engine.query(db, Term("query", None)):
result = engine.ground(db, query[0], result, label="query")
found = [str(x) for x, y in result.queries()]
self.assertCollectionEqual(found, ["p(a)", "p(b)"])
def test_anonymous_variable(self) :
"""Anonymous variables are distinct"""
program = """
p(_,X,_) :- X = 3.
q(1,2,3).
q(1,2,4).
q(2,3,5).
r(Y) :- q(_,Y,_).
"""
engine = DefaultEngine()
db = engine.prepare( PrologString(program) )
self.assertEqual( list(map(list,engine.query(db, Term('p', Constant(1), Constant(3), Constant(2) )))), [[Constant(1),Constant(3),Constant(2)]])
self.assertEqual(list(map(list,engine.query(db, Term('r', None )))), [[2], [3]])
def test_functors(self) :
"""Calls with functors"""
program = """
p(_,f(A,B),C) :- A=y, B=g(C).
a(X,Y,Z) :- p(X,f(Y,Z),c).
"""
pl = PrologString(program)
r1 = DefaultEngine().query(pl, Term('a',Term('x'),None,Term('g',Term('c'))))
r1 = [ list(map(str,sol)) for sol in r1 ]
self.assertCollectionEqual( r1, [['x', 'y', 'g(c)']])
r2 = DefaultEngine().query(pl, Term('a',Term('x'),None,Term('h',Term('c'))))
self.assertCollectionEqual( r2, [])
r3 = DefaultEngine().query(pl, Term('a',Term('x'),None,Term('g',Term('z'))))
self.assertCollectionEqual( r3, [])
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 get_labels_single_example_probabilistic_models(
example: SimpleProgram,
rules: SimpleProgram,
possible_labels: Iterable[str],
background_knowledge=None,
debug_printing=False) -> List[str]:
"""
Classifies a single example and returns a list of its labels
:param example:
:param rules:
:param possible_labels:
:return:
"""
eng = DefaultEngine()
eng.unknown = 1
if background_knowledge is not None:
db = eng.prepare(background_knowledge)
for statement in example:
db += statement
for rule in rules:
db += rule
else:
db = eng.prepare(rules)
for statement in example:
db += statement
if debug_printing:
print('\nQueried database:')
for statement in db:
print('\t' + str(statement))
# print('\n')
query_terms = [Term('query')(label) for label in possible_labels]
db_to_query = db.extend()
for query_term in query_terms:
db_to_query += query_term
query_results = problog.get_evaluatable().create_from(
db_to_query, engine=eng).evaluate()
return query_results
def __init__(self,
predicate_to_query,
index_of_label_var,
background_knowledge=None,
debug_printing=False,
engine: GenericEngine = None):
if engine is None:
self.engine = DefaultEngine()
self.engine.unknown = 1
else:
self.engine = engine
if background_knowledge is not None:
self.db = self.engine.prepare(background_knowledge)
else:
self.db = None
self.predicate_to_query = predicate_to_query
self.labels = set() # type: Set[Label]
self.index_of_label_var = index_of_label_var
self.debug_printing = debug_printing
def _run_sl_operators_on_semiring(self, givensemiring, program=None):
engine = DefaultEngine()
if program == None:
program = self._slproblog_program
db = engine.prepare(PrologString(program))
semiring = givensemiring
knowledge = get_evaluatable(None, semiring=semiring)
formula = knowledge.create_from(db, engine=engine, database=db)
res = formula.evaluate(semiring=semiring)
ret = {}
for k, v in res.items():
if isinstance(semiring, BetaSemiring):
ret[k] = moment_matching(semiring.parse(v))
else:
ret[k] = semiring.parse(v)
return self._order_dicts(ret)
def get_example_databases(
simple_program_examples: Iterable[SimpleProgramExampleWrapper],
background_knowledge: Optional[LogicProgram] = None,
models=False,
engine=None) -> List[ClauseDBExampleWrapper]:
if engine is None:
engine = DefaultEngine()
engine.unknown = 1
clausedb_examples = [] # type: List[ClauseDBExampleWrapper]
if background_knowledge is not None:
db = engine.prepare(background_knowledge) # type: ClauseDB
for example in simple_program_examples:
db_example = db.extend() # type: ClauseDB
for statement in example:
db_example += statement
example_wrapper = ClauseDBExampleWrapper(logic_program=db_example)
clausedb_examples.append(example_wrapper)
if example.classification_term is not None:
example_wrapper.classification_term = example.classification_term
if example.key is not None:
example_wrapper.key = example.key
if models:
example_wrapper.label = example.label
else:
for example in simple_program_examples:
db_example = engine.prepare(
example.logic_program) # type: ClauseDB
example_wrapper = ClauseDBExampleWrapper(logic_program=db_example)
clausedb_examples.append(example_wrapper)
if example.classification_term is not None:
example_wrapper.classification_term = example.classification_term
if example.key is not None:
example_wrapper.key = example.key
if models:
example_wrapper.label = example.label
return clausedb_examples
def __init__(self, *sources):
self._database = DefaultEngine().prepare(sources[0])
self._databaseEn = EnStructure()
for source in sources[1:]:
for clause in source:
self._database += clause
for source in sources:
for clause in source:
predicate = str(clause.functor)
args = [str(x) for x in clause.args]
if predicate == 'base':
base = str(clause.args[0].functor)
types = [str(x) for x in clause.args[0].args]
self._databaseEn.add_base(base, types)
elif predicate not in [
'option', 'target', 'mode', 'learn', 'example_mode'
]:
if clause.probability == None or float(
clause.probability) != 0.0:
self._databaseEn.add_tuple(predicate, args)
def get_default_decision_tree_builder(language,
prediction_goal) -> TreeBuilder:
engine = DefaultEngine()
engine.unknown = 1
test_evaluator = SimpleProgramQueryEvaluator(engine=engine)
test_generator_builder = ProbLogTestGeneratorBuilder(
language=language, query_head_if_keys_format=prediction_goal)
splitter = Splitter(split_criterion_str=split_criterion(),
test_evaluator=test_evaluator,
test_generator_builder=test_generator_builder)
# splitter = ProblogSplitter(language=language,split_criterion_str='entropy', test_evaluator=test_evaluator,
# query_head_if_keys_format=prediction_goal)
leaf_builder = LeafBuilder()
stop_criterion = StopCriterion()
tree_builder = TreeBuilder(splitter=splitter,
leaf_builder=leaf_builder,
stop_criterion=stop_criterion)
return tree_builder
def __init__(self, program):
self._engine = DefaultEngine()
self._db = self._engine.prepare(PrologString(program))
self._gp = None
self._knowledge = None
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) ]
