def saampling_umda_worlds(literal, yes_prob_vector, no_prob_vector, quantity,
bn):
global uniquePrograms, uniquesWorlds
print("Generating words...")
generated_worlds = generate_population(yes_prob_vector, no_prob_vector,
quantity)
print("OK\n")
bar = IncrementalBar("Analyzing generated worlds...",
max=len(generated_worlds))
for world in generated_worlds:
worldAsTuple = tuple(world)
if (not worldAsTuple in uniquesWorlds):
uniquesWorlds.add(worldAsTuple)
evidence = {i: world[i] for i in range(0, len(world))} # Dict
prWorld = bn.get_sampling_prob(evidence)
# Build the PreDeLP Program for a world
delpProgram = map_world_to_program(af, em_atoms, world)
status = queryToProgram(delpProgram, literal, uniquePrograms)
if status[1] == 'yes':
results['yes']['total'] += 1
results['yes']['prob'] = results['yes']['prob'] + prWorld
elif status[1] == 'no':
results['no']['total'] += 1
results['no']['prob'] = results['no']['prob'] + prWorld
elif status[1] == 'undecided':
results['und']['total'] += 1
results['und']['prob'] = results['und']['prob'] + prWorld
elif status[1] == 'unknown':
results['unk']['total'] += 1
results['unk']['prob'] = results['unk']['prob'] + prWorld
bar.next()
bar.finish()
def get_interest_worlds(worlds, literal, bn):
global uniquesWorlds, uniquePrograms
yes_worlds = []
no_worlds = []
for world in worlds:
worldAsTuple = tuple(world)
if (not worldAsTuple in uniquesWorlds):
uniquesWorlds.add(worldAsTuple)
evidence = {i: world[i] for i in range(0, len(world))} # Dict
prWorld = bn.get_sampling_prob(evidence)
# Build the PreDeLP Program for a world
delpProgram = map_world_to_program(af, em_atoms, world)
status = queryToProgram(delpProgram, literal, uniquePrograms)
if status[1] == 'yes':
yes_worlds.append(world)
results['yes']['total'] += 1
results['yes']['prob'] = results['yes']['prob'] + prWorld
elif status[1] == 'no':
no_worlds.append(world)
results['no']['total'] += 1
results['no']['prob'] = results['no']['prob'] + prWorld
elif status[1] == 'undecided':
results['und']['total'] += 1
results['und']['prob'] = results['und']['prob'] + prWorld
elif status[1] == 'unknown':
results['unk']['total'] += 1
results['unk']['prob'] = results['unk']['prob'] + prWorld
return [yes_worlds, no_worlds]
def umda_brute_force_worlds(literal, bn):
global uniquePrograms
all_possible_worlds = pow(2, dimension)
bar = IncrementalBar('Analyzing worlds...', max=all_possible_worlds)
initial_time = time.time()
for int_value in range(all_possible_worlds):
worldData = int_to_bin_with_format(
int_value, dimension) # Return [program, evidence] REVISAR
world = worldData[0]
evidence = worldData[1]
prWorld = bn.get_sampling_prob(evidence)
# Build the delp program for a world
delpProgram = map_world_to_program(af, em_atoms, world)
# Compute the literal status
status = queryToProgram(delpProgram, literal, uniquePrograms)
if status[1] == 'yes':
results['yes']['total'] += 1
results['yes']['prob'] = results['yes']['prob'] + prWorld
elif status[1] == 'no':
results['no']['total'] += 1
results['no']['prob'] = results['no']['prob'] + prWorld
elif status[1] == 'undecided':
results['und']['total'] += 1
results['und']['prob'] = results['und']['prob'] + prWorld
elif status[1] == 'unknown':
results['unk']['total'] += 1
results['unk']['prob'] = results['unk']['prob'] + prWorld
bar.next()
bar.finish()
results['worldsAnalyzed'] = all_possible_worlds
time_execution = time.time() - initial_time
results['yes']['perc'] = "{:.2f}".format(
(results['yes']['total'] * 100) / all_possible_worlds)
results['no']['perc'] = "{:.2f}".format(
(results['no']['total'] * 100) / all_possible_worlds)
results['und']['perc'] = "{:.2f}".format(
(results['und']['total'] * 100) / all_possible_worlds)
results['unk']['perc'] = "{:.2f}".format(
(results['unk']['total'] * 100) / all_possible_worlds)
results['l'] = results['yes']['prob']
results['u'] = results['u'] - results['no']['prob']
results['time_execution'] = time_execution
# Save file with results
with open('/home/mario/results/umda/UMDAForceBruteWorlds.json',
'w') as outfile:
json.dump(results, outfile, indent=4)
def sampling_umda_programs(literal, yes_prob_vector, no_prob_vector, quantity,
bn):
global uniquePrograms, inconsistent_program, repeated_program, uniqueOnlyPrograms
print("Generating programs...")
generated_programs = generate_population(yes_prob_vector, no_prob_vector,
quantity)
print("OK\n")
bar = IncrementalBar('Analyzing generate programs...', max=quantity * 2)
for program in generated_programs:
if not tuple(program) in uniqueOnlyPrograms:
uniqueOnlyPrograms.add(tuple(program))
delp = build_delp_from_binaries(program)
status = queryToProgram([delp, program], literal, uniquePrograms)
evidence = get_evidence(program)
if evidence != 'incorrect_program':
tuple_evidence = tuple(evidence.items())
if tuple_evidence not in uniqueEvidences:
uniqueEvidences.add(tuple_evidence)
prWorld = bn.get_sampling_prob(evidence)
if status[1] == 'yes':
results['yes']['total'] += 1
results['yes'][
'prob'] = results['yes']['prob'] + prWorld
elif status[1] == 'no':
results['no']['total'] += 1
results['no']['prob'] = results['no']['prob'] + prWorld
elif status[1] == 'undecided':
results['und']['total'] += 1
results['und'][
'prob'] = results['und']['prob'] + prWorld
elif status[1] == 'unknown':
results['unk']['total'] += 1
results['unk'][
'prob'] = results['unk']['prob'] + prWorld
else:
repeated_evidence += 1
else:
inconsistent_program += 1
else:
repeated_program += 1
bar.next()
bar.finish()
def get_interest_programs(programs, literal, bn):
global inconsistent_program, repeated_program, uniquePrograms, uniqueOnlyPrograms, uniqueEvidences, repeated_evidence
yes_programs = []
no_programs = []
for program in programs:
tuple_program = tuple(program)
if tuple_program not in uniqueOnlyPrograms:
uniqueOnlyPrograms.add(tuple_program)
delp = build_delp_from_binaries(program)
evidence = get_evidence(program)
if evidence != 'incorrect_program':
tuple_evidence = tuple(evidence.items())
if tuple_evidence not in uniqueEvidences:
uniqueEvidences.add(tuple_evidence)
status = queryToProgram([delp, program], literal,
uniquePrograms)
prWorld = bn.get_sampling_prob(evidence)
if status[1] == 'yes':
yes_programs.append(program)
results['yes']['total'] += 1
results['yes'][
'prob'] = results['yes']['prob'] + prWorld
elif status[1] == 'no':
no_programs.append(program)
results['no']['total'] += 1
results['no']['prob'] = results['no']['prob'] + prWorld
elif status[1] == 'undecided':
results['und']['total'] += 1
results['und'][
'prob'] = results['und']['prob'] + prWorld
elif status[1] == 'unknown':
results['unk']['total'] += 1
results['unk'][
'prob'] = results['unk']['prob'] + prWorld
else:
repeated_evidence += 1
else:
inconsistent_program += 1
else:
repeated_program += 1
return [yes_programs, no_programs]
def random_sampling_program(literal, bn, samples):
global inconsistent_program, repeated_program, uniquePrograms, uniqueOnlyPrograms, uniqueEvidences, repeated_evidence
all_possible_programs = pow(2, dimension)
bar = IncrementalBar("Random sampling programs...", max=samples)
initial_time = time.time()
for i in range(samples):
int_program = np.random.choice(all_possible_programs + 1, 1)
program = int_to_bin_with_format(int_program, dimension)[0]
tuple_program = tuple(program)
if tuple_program not in uniqueOnlyPrograms:
uniqueOnlyPrograms.add(tuple_program)
delp = build_delp_from_binaries(program)
evidence = get_evidence(program)
if evidence != 'incorrect_program':
tuple_evidence = tuple(evidence.items())
if tuple_evidence not in uniqueEvidences:
uniqueEvidences.add(tuple_evidence)
status = queryToProgram([delp, program], literal,
uniquePrograms)
prWorld = bn.get_sampling_prob(evidence)
if status[1] == 'yes':
results['yes']['total'] += 1
results['yes'][
'prob'] = results['yes']['prob'] + prWorld
elif status[1] == 'no':
results['no']['total'] += 1
results['no']['prob'] = results['no']['prob'] + prWorld
elif status[1] == 'undecided':
results['und']['total'] += 1
results['und'][
'prob'] = results['und']['prob'] + prWorld
elif status[1] == 'unknown':
results['unk']['total'] += 1
results['unk'][
'prob'] = results['unk']['prob'] + prWorld
else:
repeated_evidence += 1
else:
inconsistent_program += 1
else:
repeated_program += 1
bar.next()
bar.finish()
time_execution = time.time() - initial_time
results['execution_time'] = time_execution
results['unique_programs'] = len(uniqueOnlyPrograms)
results['inconsistent']['total'] = inconsistent_program
results['inconsistent']['perc'] = "{:.2f}".format(
(results['inconsistent']['total'] * 100) / samples)
results['repeated']['total'] = repeated_program
results['repeated']['perc'] = "{:.2f}".format(
(results['repeated']['total'] * 100) / samples)
results['domain'] = 'programs'
results['yes']['perc'] = "{:.2f}".format(
(results['yes']['total'] * 100) / samples)
results['no']['perc'] = "{:.2f}".format(
(results['no']['total'] * 100) / samples)
results['und']['perc'] = "{:.2f}".format(
(results['und']['total'] * 100) / samples)
results['unk']['perc'] = "{:.2f}".format(
(results['unk']['total'] * 100) / samples)
results['l'] = results['yes']['prob']
results['u'] = results['u'] - results['no']['prob']
results['total_sampling'] = samples
print("Unique programs: ", end='')
print_ok_ops("%s" % len(uniqueOnlyPrograms))
print("Unique evidence: ", end='')
print_ok_ops("%s" % len(uniqueEvidences))
print("Inconsistent programs: ", end='')
print_ok_ops("%s" % inconsistent_program)
print("repeated evidence: ", repeated_evidence)
with open('/home/mario/results/umda/UMDARandomPrograms.json',
'w') as outfile:
json.dump(results, outfile, indent=4)
def umda_brute_force_programs(literal, bn):
global uniqueEvidence, uniquePrograms, repeated_evidence, inconsistent_program
all_possible_programs = pow(2, dimension)
bar = IncrementalBar("Analyzing programs...", max=all_possible_programs)
initial_time = time.time()
for int_value in range(all_possible_programs):
program = int_to_bin_with_format(
int_value, dimension)[0] # Return [program, evidence] REVISAR
evidence = get_evidence(program)
if evidence != 'incorrect_program':
tuple_evidence = tuple(evidence.items())
if tuple_evidence not in uniqueEvidences:
uniqueEvidences.add(tuple_evidence)
delp = build_delp_from_binaries(program)
status = queryToProgram([delp, program], literal,
uniquePrograms)
prWorld = bn.get_sampling_prob(evidence)
if status[1] == 'yes':
results['yes']['total'] += 1
results['yes']['prob'] = results['yes']['prob'] + prWorld
elif status[1] == 'no':
results['no']['total'] += 1
results['no']['prob'] = results['no']['prob'] + prWorld
elif status[1] == 'undecided':
results['und']['total'] += 1
results['und']['prob'] = results['und']['prob'] + prWorld
elif status[1] == 'unknown':
results['unk']['total'] += 1
results['unk']['prob'] = results['unk']['prob'] + prWorld
else:
repeated_evidence += 1
else:
inconsistent_program += 1
bar.next()
bar.finish()
time_execution = time.time() - initial_time
results['execution_time'] = time_execution
results['yes']['perc'] = "{:.2f}".format(
(results['yes']['total'] * 100) / all_possible_programs)
results['no']['perc'] = "{:.2f}".format(
(results['no']['total'] * 100) / all_possible_programs)
results['und']['perc'] = "{:.2f}".format(
(results['und']['total'] * 100) / all_possible_programs)
results['unk']['perc'] = "{:.2f}".format(
(results['unk']['total'] * 100) / all_possible_programs)
results['inconsistent']['total'] = inconsistent_program
results['inconsistent']['perc'] = "{:.2f}".format(
(results['inconsistent']['total'] * 100) / all_possible_programs)
results['programsAnalyzed'] = all_possible_programs
results['l'] = results['yes']['prob']
results['u'] = results['u'] - results['no']['prob']
print("Unique programs: ", end='')
print_ok_ops("%s" % (int_value + 1))
print("Unique evidence: ", end='')
print_ok_ops("%s" % len(uniqueEvidences))
print("Inconsistent programs: ", end='')
print_ok_ops("%s" % inconsistent_program)
with open('/home/mario/results/umda/UMDAForceBrutePrograms.json',
'w') as outfile:
json.dump(results, outfile, indent=4)