def test_set_custom_gen_creator(self):
with saved(Config.get_instance()) as config:
Config.get_instance().set("POPULATION", "size", "5")
Config.get_instance().set("BEHAVIOUR", "save", "false")
from MLC.Population.Creation.BaseCreation import BaseCreation
from MLC.individual.Individual import Individual
class TestCreator(BaseCreation):
def __init__(self):
BaseCreation.__init__(self)
def create(self, gen_size):
MLCRepository.get_instance().add_individual(
Individual("(root 1)"))
MLCRepository.get_instance().add_individual(
Individual("(root 2)"))
MLCRepository.get_instance().add_individual(
Individual("(root 3)"))
MLCRepository.get_instance().add_individual(
Individual("(root 4)"))
MLCRepository.get_instance().add_individual(
Individual("(root 5)"))
def individuals(self):
return [(0, 1), (1, 2), (2, 3), (3, 4), (4, 5)]
simulation = Simulation("")
mlc = Application(simulation, gen_creator=TestCreator())
mlc.go(to_generation=1)
# Assert first Population was filled using the TestCreator
population = MLCRepository.get_instance().get_population(1)
self.assertEqual(population.get_individuals(), [1, 2, 3, 4, 5])
def test_remove_population_to_from_bad_values(self):
mlc_repo = self.__get_new_repo()
# add individuals
mlc_repo.add_individual(Individual("(root (+ 1 1))"))
mlc_repo.add_individual(Individual("(root (+ 2 2))"))
mlc_repo.add_individual(Individual("(root (+ 3 3))"))
mlc_repo.add_individual(Individual("(root (+ 4 4))"))
mlc_repo.add_individual(Individual("(root (+ 5 5))"))
# add population
p = Population(3, 0, Config.get_instance(), mlc_repo)
p._individuals = [1, 1, 1]
mlc_repo.add_population(p)
# add population
p = Population(3, 0, Config.get_instance(), mlc_repo)
p._individuals = [2, 2, 2]
mlc_repo.add_population(p)
# add population
p = Population(3, 0, Config.get_instance(), mlc_repo)
p._individuals = [3, 3, 3]
mlc_repo.add_population(p)
self.assertEqual(mlc_repo.count_population(), 3)
# Remove all generations (1 to 3)
mlc_repo.remove_population_to(10)
self.assertEqual(mlc_repo.count_population(), 0)
def test_update_individual_cost_in_generation(self):
mlc_repo = self.__get_new_repo()
# add individuals
mlc_repo.add_individual(Individual("(root (+ 1 1))"))
mlc_repo.add_individual(Individual("(root (+ 2 2))"))
# add first population
p = Population(3, 0, Config.get_instance(), mlc_repo)
p._individuals = [1, 2, 1]
p._costs = [4, 5, 6]
p._ev_time = [5, 6, 7]
mlc_repo.add_population(p)
# add second population
p = Population(3, 0, Config.get_instance(), mlc_repo)
p._individuals = [2, 1, 2]
p._costs = [8, 9, 10]
p._ev_time = [11, 12, 13]
mlc_repo.add_population(p)
# update cost for individual 1
mlc_repo.update_individual_cost(1, 45, 46, generation=1)
# check cost update in the first population
p = mlc_repo.get_population(1)
self.assertEqual(p._individuals, [1, 2, 1])
self.assertEqual(p._costs, [45, 5, 45])
self.assertEqual(p._ev_time, [46, 6, 46])
# check cost update in the second population
p = mlc_repo.get_population(2)
self.assertEqual(p._individuals, [2, 1, 2])
self.assertEqual(p._costs, [8, 9, 10])
self.assertEqual(p._ev_time, [11, 12, 13])
def test_reload_individuals_from_file(self):
with saved(Config.get_instance()) as config:
config.set("BEHAVIOUR", "save", "true")
config.set("BEHAVIOUR", "savedir", "test.db")
config.set("POPULATION", "sensor_spec", "false")
config.set("POPULATION", "sensors", "0")
config.set("OPTIMIZATION", "simplify", "false")
mlc_repo = self.__get_new_repo()
# add individuals
mlc_repo.add_individual(Individual("(root (+ 1 1))"))
mlc_repo.add_individual(Individual("(root (+ 2 2))"))
# add population
p = Population(3, 0, Config.get_instance(), mlc_repo)
p._individuals = [2, 1, 2]
mlc_repo.add_population(p)
# check status
self.assertEqual(mlc_repo.count_individual(), 2)
self.assertEqual(mlc_repo.count_population(), 1)
# reload mlc_repository using another instance
mlc_repo = self.__get_new_repo()
self.assertEqual(mlc_repo.count_individual(), 2)
self.assertEqual(mlc_repo.count_population(), 1)
def test_parameter_controls_mutate_hoist(self):
# save and restore original configuration
with saved(Config.get_instance()):
Config.get_instance().set("POPULATION", "controls", "5")
individual = Individual(
'(root (/ (exp (/ 8.2118 S0)) (* (* S0 (* 1.6755 -0.0699)) (log (exp -3.2288)))) (* (+ (sin -9.8591) (exp S0)) -9.4159) 0.0290 (* (log (* (+ -5.0573 -6.2191) S0)) (/ (cos (log S0)) (cos (tanh 2.2886)))) (log -8.6795))'
)
new_ind = individual.mutate(Individual.MutationType.HOIST)
self.assertEqual(
new_ind.get_value(),
"(root (log (exp -3.2288)) (* (+ (sin -9.8591) (exp S0)) -9.4159) 0.0290 (* (log (* (+ -5.0573 -6.2191) S0)) (/ (cos (log S0)) (cos (tanh 2.2886)))) (log -8.6795))"
)
new_ind = individual.mutate(Individual.MutationType.HOIST)
self.assertEqual(
new_ind.get_value(),
"(root (/ (exp (/ 8.2118 S0)) (* (* S0 (* 1.6755 -0.0699)) (log (exp -3.2288)))) (exp S0) 0.0290 (* (log (* (+ -5.0573 -6.2191) S0)) (/ (cos (log S0)) (cos (tanh 2.2886)))) (log -8.6795))"
)
new_ind = individual.mutate(Individual.MutationType.HOIST)
self.assertEqual(
new_ind.get_value(),
"(root (* S0 (* 1.6755 -0.0699)) (* (+ (sin -9.8591) (exp S0)) -9.4159) 0.0290 (* (log (* (+ -5.0573 -6.2191) S0)) (/ (cos (log S0)) (cos (tanh 2.2886)))) (log -8.6795))"
)
def test_get_individual_data(self):
mlc_repo = self.__get_new_repo()
# add individuals
mlc_repo.add_individual(Individual("(root (+ 1 1))"))
mlc_repo.add_individual(Individual("(root (+ 2 2))"))
mlc_repo.add_individual(Individual("(root (+ 3 3))"))
mlc_repo.add_individual(Individual("(root (+ 4 4))"))
# first population
p = Population(3, 0, Config.get_instance(), mlc_repo)
p._individuals = [1, 2, 1]
p._costs = [4, 5, 6]
p._ev_time = [5, 6, 7]
mlc_repo.add_population(p)
# second population
p = Population(3, 0, Config.get_instance(), mlc_repo)
p._individuals = [3, 4, 2]
p._costs = [7, 4, 9]
p._ev_time = [8, 5, 10]
mlc_repo.add_population(p)
# check idividuals data loaded from the mlc_repo
self.assertEqual(mlc_repo.count_population(), 2)
# Individual 1 have two appearances in the first generation
data = mlc_repo.get_individual_data(1)
self.assertEqual(data.get_value(), "(root (+ 1 1))")
self.assertEqual(data.get_appearances(), 2)
self.assertEqual(data.get_cost_history(), {1: [(4.0, 5), (6.0, 7)]})
# Individual 2 have two appearances
data = mlc_repo.get_individual_data(2)
self.assertEqual(data.get_value(), "(root (+ 2 2))")
self.assertEqual(data.get_appearances(), 2)
self.assertEqual(data.get_cost_history(), {
1: [(5.0, 6)],
2: [(9.0, 10)]
})
# Individual 3 have one appearances
data = mlc_repo.get_individual_data(3)
self.assertEqual(data.get_value(), "(root (+ 3 3))")
self.assertEqual(data.get_appearances(), 1)
self.assertEqual(data.get_cost_history(), {2: [(7.0, 8)]})
# Individual 4 have one appearances
data = mlc_repo.get_individual_data(4)
self.assertEqual(data.get_value(), "(root (+ 4 4))")
self.assertEqual(data.get_appearances(), 1)
self.assertEqual(data.get_cost_history(), {2: [(4.0, 5)]})
# get individual data from invalid individual
try:
data = mlc_repo.get_individual_data(100)
self.assertTrue(False)
except KeyError, ex:
self.assertTrue(True)
def test_list_of_callbacks(self):
with saved(Config.get_instance()) as config:
Config.get_instance().set("POPULATION", "size", "10")
Config.get_instance().set("BEHAVIOUR", "save", "false")
ApplicationTest.on_start = 0
ApplicationTest.on_start_counter_2 = 0
def test_on_start_callback():
ApplicationTest.on_start += 1
def test_on_start_callback_increment_2():
ApplicationTest.on_start_counter_2 += 1
start_callbacks = [
test_on_start_callback, test_on_start_callback_increment_2
]
ApplicationTest.mlc_local.new_experiment(
ApplicationTest.experiment_name,
ApplicationTest.test_conf_path)
ApplicationTest.mlc_local.open_experiment(
ApplicationTest.experiment_name)
ApplicationTest.mlc_local.go(
experiment_name=ApplicationTest.experiment_name,
to_generation=2,
from_generation=0,
callbacks={MLC_CALLBACKS.ON_START: start_callbacks})
ApplicationTest.mlc_local.close_experiment(
ApplicationTest.experiment_name)
ApplicationTest.mlc_local.delete_experiment(
ApplicationTest.experiment_name)
self.assertEqual(ApplicationTest.on_start, 1)
self.assertEqual(ApplicationTest.on_start_counter_2, 1)
def reload_configuration(self):
"""
Load again the experiment configuration, reading the config file from disk
"""
self._configuration = ConfigParser.ConfigParser()
self._configuration.read(self._config_file)
Config.get_instance().read(self._config_file)
return Config.to_dictionary(self._configuration)
def test_remove_population_to(self):
mlc_repo = self.__get_new_repo()
# add individuals
mlc_repo.add_individual(Individual("(root (+ 1 1))"))
mlc_repo.add_individual(Individual("(root (+ 2 2))"))
mlc_repo.add_individual(Individual("(root (+ 3 3))"))
mlc_repo.add_individual(Individual("(root (+ 4 4))"))
mlc_repo.add_individual(Individual("(root (+ 5 5))"))
# add population
p = Population(3, 0, Config.get_instance(), mlc_repo)
p._individuals = [1, 1, 1]
mlc_repo.add_population(p)
# add population
p = Population(3, 0, Config.get_instance(), mlc_repo)
p._individuals = [2, 2, 2]
mlc_repo.add_population(p)
# add population
p = Population(3, 0, Config.get_instance(), mlc_repo)
p._individuals = [3, 3, 3]
mlc_repo.add_population(p)
# add population
p = Population(3, 0, Config.get_instance(), mlc_repo)
p._individuals = [4, 4, 4]
mlc_repo.add_population(p)
self.assertEqual(mlc_repo.count_population(), 4)
# remove generations 1 to 2
mlc_repo.remove_population_to(2)
self.assertEqual(mlc_repo.count_population(), 2)
p = mlc_repo.get_population(1)
self.assertEqual(p._individuals, [3, 3, 3])
p = mlc_repo.get_population(2)
self.assertEqual(p._individuals, [4, 4, 4])
# New generation must be number 3
p = Population(3, 0, Config.get_instance(), mlc_repo)
p._individuals = [5, 5, 5]
mlc_repo.add_population(p)
self.assertEqual(mlc_repo.count_population(), 3)
p = mlc_repo.get_population(1)
self.assertEqual(p._individuals, [3, 3, 3])
p = mlc_repo.get_population(2)
self.assertEqual(p._individuals, [4, 4, 4])
p = mlc_repo.get_population(3)
self.assertEqual(p._individuals, [5, 5, 5])
def test_cut_generation(self):
"""
Cut a generation using remove_population_from/remove_population_last
:return:
"""
mlc_repo = self.__get_new_repo()
# add individuals
mlc_repo.add_individual(Individual("(root (+ 1 1))"))
mlc_repo.add_individual(Individual("(root (+ 2 2))"))
mlc_repo.add_individual(Individual("(root (+ 3 3))"))
mlc_repo.add_individual(Individual("(root (+ 4 4))"))
mlc_repo.add_individual(Individual("(root (+ 5 5))"))
# add population
p = Population(3, 0, Config.get_instance(), mlc_repo)
p._individuals = [1, 1, 1]
mlc_repo.add_population(p)
# add population
p = Population(3, 0, Config.get_instance(), mlc_repo)
p._individuals = [2, 2, 2]
mlc_repo.add_population(p)
# add population
p = Population(3, 0, Config.get_instance(), mlc_repo)
p._individuals = [3, 3, 3]
mlc_repo.add_population(p)
# add population
p = Population(3, 0, Config.get_instance(), mlc_repo)
p._individuals = [4, 4, 4]
mlc_repo.add_population(p)
# add population
p = Population(3, 0, Config.get_instance(), mlc_repo)
p._individuals = [5, 5, 5]
mlc_repo.add_population(p)
self.assertEqual(mlc_repo.count_population(), 5)
# Cut population 4
mlc_repo.remove_population_from(4 + 1)
mlc_repo.remove_population_to(4 - 1)
# remove unused individuals
mlc_repo.remove_unused_individuals()
self.assertEqual(mlc_repo.count_population(), 1)
self.assertEqual(mlc_repo.count_individual(), 1)
p = mlc_repo.get_population(1)
self.assertEqual(p._individuals, [4, 4, 4])
individual = mlc_repo.get_individual(4)
self.assertEqual(individual.get_value(), "(root (+ 4 4))")
def test_compare_random_individuals(self):
individual_1 = Individual.generate(individual_type=3,
config=Config.get_instance())
RandomManager.clear_random_values()
RandomManager.load_random_values(self._random_file)
individual_2 = Individual.generate(individual_type=3,
config=Config.get_instance())
self.assertTrue(individual_1.compare(individual_2))
def test_crossover_same_level_4(self):
individual_1 = Individual("(root S0)", Config.get_instance())
individual_2 = Individual("(root S0)", Config.get_instance())
try:
new_ind_1, new_ind_2, _ = individual_1.crossover(individual_2)
self.assertFalse(True,
"crossover with individual type 4 should fail")
except OperationOverIndividualFail, ex:
self.assertTrue(True)
def create_empty_population_for(generation):
from MLC.Population.Population import Population
cascade = Config.get_instance().get_list('OPTIMIZATION', 'cascade')
size = Config.get_instance().get_list('POPULATION', 'size')
population_size = cascade[1] if (generation > 1
and len(size) > 1) else size[0]
population_subgenerations = 1 if cascade[1] == 0 else cascade[1]
return Population(population_size, population_subgenerations,
Config.get_instance(), MLCRepository.get_instance())
def test_remove_from_population(self):
mlc_repo = self.__get_new_repo()
# add individuals
mlc_repo.add_individual(Individual("(root (+ 1 1))"))
mlc_repo.add_individual(Individual("(root (+ 2 2))"))
mlc_repo.add_individual(Individual("(root (+ 3 3))"))
mlc_repo.add_individual(Individual("(root (+ 4 4))"))
# add first population
p = Population(3, 0, Config.get_instance(), mlc_repo)
p._individuals = [1, 2, 1]
mlc_repo.add_population(p)
# add second population
p = Population(3, 0, Config.get_instance(), mlc_repo)
p._individuals = [1, 2, 3]
mlc_repo.add_population(p)
# add third population
p = Population(3, 0, Config.get_instance(), mlc_repo)
p._individuals = [1, 2, 4]
mlc_repo.add_population(p)
# remove last population
mlc_repo.remove_population_from(2)
# last generation must be removed
self.assertEqual(mlc_repo.count_population(), 1)
# first generation exists
p = mlc_repo.get_population(1)
self.assertEqual(p._individuals, [1, 2, 1])
# all individuals exists and the third individual do not appear in any generation
self.assertEqual(mlc_repo.count_individual(), 4)
# remove unused individuals
deleted = mlc_repo.remove_unused_individuals()
self.assertEqual(deleted, 2)
self.assertEqual(mlc_repo.count_individual(), 2)
individual = mlc_repo.get_individual(1)
self.assertEqual(individual.get_value(), "(root (+ 1 1))")
individual = mlc_repo.get_individual(2)
self.assertEqual(individual.get_value(), "(root (+ 2 2))")
try:
individual = mlc_repo.get_individual(3)
self.assertTrue(False)
except KeyError:
self.assertTrue(True)
def get_simulation(self):
if Experiment.__last_simulation is None or Experiment.__last_simulation != self._simulation:
MLCRepository._instance = None
Config._instance = None
Config.get_instance().read(self._config_file)
set_logger(Config.get_instance().get('LOGGING', 'logmode'))
if Experiment.__last_simulation:
Experiment.__last_simulation.close()
self._simulation = Simulation(self._name)
Experiment.__last_simulation = self._simulation
return self._simulation
def test_crossover_same_level_0(self):
individual_1 = Individual("(root (cos 5.046))", Config.get_instance())
individual_2 = Individual("(root (cos 5.046))", Config.get_instance())
new_ind_1, new_ind_2, _ = individual_1.crossover(individual_2)
self._assert_individual(new_ind_1,
complexity=4,
value="(root (cos 5.046))",
formal="cos(5.046)")
self._assert_individual(new_ind_2,
complexity=4,
value="(root (cos 5.046))",
formal="cos(5.046)")
def _fill_creation(self, individuals, index, type):
while index < len(individuals):
indiv = Individual.generate(individual_type=type,
config=Config.get_instance())
response = MLCRepository.get_instance().add_individual(indiv)
if not response[1]:
# The individual didn't exist
indiv_number = individuals[index]
lg.logger_.info('[FILL_CREATION] Generating individual N#' +
str(indiv_number))
lg.logger_.debug('[FILL_CREATION] Individual N#' +
str(indiv_number) + ' - Value: ' +
indiv.get_value())
# Call the preevaluation function if it exists and if it is configured
if self._config.getboolean('EVALUATOR', 'preevaluation'):
callback = PreevaluationManager.get_callback().preev
if callback is not None:
if not callback(indiv):
lg.logger_.info(
'[FILL_CREATION] Preevaluation failed'
'. Individual value: ' + indiv.get_value())
continue
self._individuals.append((index, response[0]))
index += 1
else:
lg.logger_.debug('[FILL_CREATION] Replica created.')
return index
def individual_data(indiv):
SAMPLES = 201
x = np.linspace(-10.0, 10.0, num=SAMPLES)
y = np.tanh(x**3 - x**2 - 1)
config = Config.get_instance()
artificial_noise = config.getint('EVALUATOR', 'artificialnoise')
y_with_noise = y + [random.random() / 2 - 0.25 for _ in xrange(SAMPLES)] + artificial_noise * 500
if isinstance(indiv.get_formal(), str):
formal = indiv.get_formal().replace('S0', 'x')
else:
# toy problem support for multiple controls
formal = indiv.get_formal()[0].replace('S0', 'x')
# Calculate J like the sum of the square difference of the
# functions in every point
lg.logger_.debug('[POP][TOY_PROBLEM] Individual Formal: ' + formal)
b = indiv.get_tree().calculate_expression([x])
# If the expression doesn't have the term 'x',
# the eval returns a value (float) instead of an array.
# In that case transform it to an array
if type(b) == float:
b = np.repeat(b, SAMPLES)
return x, y, y_with_noise, b
def test_add_population(self):
mlc_repo = self.__get_new_repo()
mlc_repo.add_individual(Individual("(root (+ 1 1))"))
mlc_repo.add_individual(Individual("(root (+ 2 2))"))
mlc_repo.add_individual(Individual("(root (+ 3 3))"))
self.assertEqual(mlc_repo.count_individual(), 3)
p = Population(3, 0, Config.get_instance(), mlc_repo)
p._individuals = [1, 2, 3]
p._costs = [4, 5, 6]
p._ev_time = [7, 8, 9]
p._gen_method = [10, 11, 12]
# add population to the mlc_repository
self.assertEqual(mlc_repo.count_population(), 0)
mlc_repo.add_population(p)
self.assertEqual(mlc_repo.count_population(), 1)
# obtain population
p_from_repo = mlc_repo.get_population(1)
# check population content
self.assertEqual(p_from_repo._individuals, p._individuals)
self.assertEqual(p_from_repo._costs, p._costs)
self.assertEqual(p_from_repo._ev_time, p._ev_time)
self.assertEqual(p_from_repo._gen_method, p._gen_method)
def __init__(self, simulation, callbacks={}, gen_creator=None):
self._config = Config.get_instance()
# Reload the Operations supported
Operations.get_instance(reload_operations=True)
self._simulation = simulation
self._mlc_repository = MLCRepository.get_instance()
# Set logger mode of the App
set_logger(self._config.get('LOGGING', 'logmode'))
self._simulation = simulation
self._project_validations()
# callbacks configuration
self.__callbacks_manager = MLCCallbacksManager()
# bad values and duplicates
self.__badvalues_elim = self._config.get('EVALUATOR', 'badvalues_elim')
# Gen creator
if gen_creator is None:
gen_method = self._config.get('GP', 'generation_method')
self._gen_creator = CreationFactory.make(gen_method)
else:
self._gen_creator = gen_creator
# Gen evaluator
ev_method = self._config.get('EVALUATOR', 'evaluation_method')
self._evaluator = EvaluatorFactory.make(ev_method,
self.__callbacks_manager)
self._look_for_duplicates = self._config.getboolean(
'OPTIMIZATION', 'lookforduplicates')
# callbacks for the MLC application
if MLC_CALLBACKS.ON_START in callbacks:
self.__callbacks_manager.subscribe(
MLC_CALLBACKS.ON_START, callbacks[MLC_CALLBACKS.ON_START])
if MLC_CALLBACKS.ON_EVALUATE in callbacks:
self.__callbacks_manager.subscribe(
MLC_CALLBACKS.ON_EVALUATE,
callbacks[MLC_CALLBACKS.ON_EVALUATE])
if MLC_CALLBACKS.ON_NEW_GENERATION in callbacks:
self.__callbacks_manager.subscribe(
MLC_CALLBACKS.ON_NEW_GENERATION,
callbacks[MLC_CALLBACKS.ON_NEW_GENERATION])
if MLC_CALLBACKS.ON_FINISH in callbacks:
self.__callbacks_manager.subscribe(
MLC_CALLBACKS.ON_FINISH, callbacks[MLC_CALLBACKS.ON_FINISH])
# add callback to show best individual
self.__callbacks_manager.subscribe(MLC_CALLBACKS.ON_NEW_GENERATION,
self.show_best)
self.__display_best = True
def test_generate_individuals_types(self):
individual = Individual.generate(individual_type=0,
config=Config.get_instance())
self._assert_individual(
individual,
complexity=120,
value=
"(root (sin (+ (/ (cos -3.0973) (exp (log (* (* -1.3423 (tanh (log -3.5094))) (+ (/ (/ (* -9.1213 (cos (exp 3.6199))) (cos (* S0 (cos (- 5.0161 (sin 4.2656)))))) S0) (- (cos (* (+ (sin -9.8591) (exp S0)) -9.4159)) (log (* (- (tanh -8.5969) S0) (/ (exp (/ 8.2118 S0)) (* (* S0 (* 1.6755 -0.0699)) (log (exp -3.2288)))))))))))) S0)))",
formal=
"sin(((my_div(cos((-3.0973)),exp(my_log((((-1.3423) .* tanh(my_log((-3.5094)))) .* ((my_div((my_div(((-9.1213) .* cos(exp(3.6199))),cos((S0 .* cos((5.0161 - sin(4.2656))))))),S0)) + (cos(((sin((-9.8591)) + exp(S0)) .* (-9.4159))) - my_log(((tanh((-8.5969)) - S0) .* (my_div(exp((my_div(8.2118,S0))),((S0 .* (1.6755 .* (-0.0699))) .* my_log(exp((-3.2288))))))))))))))) + S0))"
)
individual = Individual.generate(individual_type=1,
config=Config.get_instance())
self._assert_individual(
individual,
complexity=24,
value=
"(root (- (sin (* (log -3.7260) (+ -5.0573 -6.2191))) (* 7.3027 (/ (cos S0) (* 4.7410 6.7097)))))",
formal=
"(sin((my_log((-3.7260)) .* ((-5.0573) + (-6.2191)))) - (7.3027 .* (my_div(cos(S0),(4.7410 .* 6.7097)))))"
)
individual = Individual.generate(individual_type=2,
config=Config.get_instance())
self._assert_individual(
individual,
complexity=15,
value="(root (tanh (cos (+ (+ 5.4434 -3.1258) (+ S0 5.1136)))))",
formal="tanh(cos(((5.4434 + (-3.1258)) + (S0 + 5.1136))))")
individual = Individual.generate(individual_type=3,
config=Config.get_instance())
self._assert_individual(
individual,
complexity=18,
value="(root (log (sin (+ (log -6.2620) (* 8.3709 -6.7676)))))",
formal="my_log(sin((my_log((-6.2620)) + (8.3709 .* (-6.7676)))))")
individual = Individual.generate(individual_type=4,
config=Config.get_instance())
self._assert_individual(individual,
complexity=1,
value="(root -0.6212)",
formal="(-0.6212)")
def test_callback_on_evaluate(self):
with saved(Config.get_instance()) as config:
Config.get_instance().set("POPULATION", "size", "10")
Config.get_instance().set("BEHAVIOUR", "save", "false")
def test_on_start_callback():
ApplicationTest.on_start += 1
def test_on_evaluate_callback(individual_id, cost):
ApplicationTest.on_evaluate += 1
def test_on_new_generation_callback(generation_number):
ApplicationTest.on_new_generation.append(generation_number)
def test_on_finish_callback():
ApplicationTest.on_finish += 1
callbacks_dict = {
MLC_CALLBACKS.ON_START: test_on_start_callback,
MLC_CALLBACKS.ON_EVALUATE: test_on_evaluate_callback,
MLC_CALLBACKS.ON_NEW_GENERATION:
test_on_new_generation_callback,
MLC_CALLBACKS.ON_FINISH: test_on_finish_callback
}
ApplicationTest.mlc_local.new_experiment(
ApplicationTest.experiment_name,
ApplicationTest.test_conf_path)
ApplicationTest.mlc_local.open_experiment(
ApplicationTest.experiment_name)
ApplicationTest.mlc_local.go(
experiment_name=ApplicationTest.experiment_name,
to_generation=2,
from_generation=0,
callbacks=callbacks_dict)
ApplicationTest.mlc_local.close_experiment(
ApplicationTest.experiment_name)
ApplicationTest.mlc_local.delete_experiment(
ApplicationTest.experiment_name)
self.assertEqual(ApplicationTest.on_start, 1)
self.assertEqual(ApplicationTest.on_evaluate, 2 * 10)
self.assertEqual(ApplicationTest.on_new_generation,
range(1, 2 + 1))
self.assertEqual(ApplicationTest.on_finish, 1)
def _update_scatter_chart(self):
indivs_per_gen = Config.get_instance().getint("POPULATION", "size")
costs = None
if self._current_gen != 0:
generation = self._mlc_local.get_generation(self._experiment_name,
self._current_gen)
costs = generation.get_costs()
self._chart_conf.update_chart(indivs_per_gen, costs)
def _draw_individual_tree(self, pop_index, indiv_index, simplify, evaluate,
value):
fig = plt.figure()
# Put figure window on top of all other windows
fig.canvas.manager.window.setWindowModality(Qt.ApplicationModal)
fig.canvas.manager.window.setWindowTitle(
"Individual Tree Representation")
# Simplify the tree if neccesary
tree = LispTreeExpr(value)
if simplify == True:
tree.simplify_tree()
graph = tree.construct_graph()
# Evaluate the Individual if necessary
cost = None
if evaluate == True:
cost = test_individual_value(parent=self,
experiment_name=self._experiment_name,
log_prefix=self._log_prefix,
indiv_value=value,
config=Config.get_instance())
# Remove image axes
ax = fig.add_axes([0, 0, 1, 1])
ax.axis('off')
# Networkx use the node id as label. We have to relabel every node
labels = {}
for node_id in graph:
labels[node_id] = graph.node[node_id]['value']
# Use Pygraph to visualize the graph as a hierarchical tree
pos = nx.nx_pydot.graphviz_layout(graph, prog='dot')
# Draw nodes and edges in separated nodes to be able to change
# the perimter color of the nodes
nodes = nx.draw_networkx_nodes(graph,
pos,
node_size=1000,
node_color='#D3D3D3')
nodes.set_edgecolor('k')
nx.draw_networkx_edges(graph, pos, arrows=False)
nx.draw_networkx_labels(graph, pos, labels, font_size=12)
# Set the figure Title
plt.rc('font', family='serif')
title = (
u"Generation N°{0} - Individual N°{1} - Simplified: {2}".format(
self._current_gen, indiv_index, simplify))
if evaluate == True:
title += " - Cost: {0}".format(cost)
plt.suptitle(title, fontsize=12)
plt.show()
self.close()
def __init__(self):
self._config = Config.get_instance()
operations_config_file = os.path.join(mlc_paths.get_config_path(),
'operations.yaml')
available_operations = yaml.load(open(operations_config_file))
opsetrange = self._config.get_list('POPULATION', 'opsetrange')
self._ops = {}
for operation_id in opsetrange:
self._ops[operation_id] = available_operations[operation_id]
def setUpClass(cls):
config_file = os.path.join(MLCIntegrationTest.TEST_DIRECTORY,
'./configuration.ini')
ev_script = os.path.join(MLCIntegrationTest.TEST_DIRECTORY,
'./default_evaluation_script.py')
preev_script = os.path.join(MLCIntegrationTest.TEST_DIRECTORY,
'./default_preevaluation_script.py')
# Load the config
config = Config.get_instance()
config.read(config_file)
# Load randoms from file
random_file = 'matlab_randoms.txt'
RandomManager.clear_random_values()
RandomManager.load_random_values(random_file)
# Create the workspace directory
try:
os.makedirs(MLCIntegrationTest.WORKSPACE_DIR)
except OSError:
shutil.rmtree(MLCIntegrationTest.WORKSPACE_DIR)
os.makedirs(MLCIntegrationTest.WORKSPACE_DIR)
# Create a new experiment, which will be used to
cls._mlc = MLCLocal(working_dir=MLCIntegrationTest.WORKSPACE_DIR)
cls._mlc.new_experiment(
experiment_name=MLCIntegrationTest.EXPERIMENT_NAME,
experiment_configuration=config_file,
evaluation_script=ev_script,
preevaluation_script=preev_script)
cls._mlc.open_experiment(
experiment_name=MLCIntegrationTest.EXPERIMENT_NAME)
for generation_params in MLCIntegrationTest.GENERATIONS:
if isinstance(generation_params, int):
cls._mlc.go(experiment_name=MLCIntegrationTest.EXPERIMENT_NAME,
to_generation=generation_params)
elif isinstance(generation_params, list):
cls._mlc.go(experiment_name=MLCIntegrationTest.EXPERIMENT_NAME,
from_generation=generation_params[0],
to_generation=generation_params[1])
else:
raise Exception(
"Integration test, bad value for generations param")
# List with individuals data
cls._indivs = []
cls._populate_indiv_dict()
cls._pops = []
cls._populate_pop_dict()
def test_random_generate(self):
individual = Individual.generate(individual_type=3,
config=Config.get_instance())
self.assertEquals(
individual.get_value(),
"(root (sin (/ (+ (exp -2.6118) (cos S0)) (/ (log 5.9383) (log -4.5037)))))"
)
self.assertEquals(
individual.get_formal(),
"sin((my_div((exp((-2.6118)) + cos(S0)),(my_div(my_log(5.9383),my_log((-4.5037)))))))"
)
self.assertEquals(individual.get_complexity(), 28)
def test_add_population_with_invalid_individual(self):
mlc_repo = self.__get_new_repo()
# add population with invalid individual
p = Population(1, 0, Config.get_instance(), mlc_repo)
p._individuals = [100]
try:
mlc_repo.add_population(p)
self.assertTrue(False)
except KeyError:
self.assertTrue(True)
def __fill_and_assert(self, fill_creator, expected_pop_indexes,
expected_individuals):
with saved(Config.get_instance()) as config:
Config.get_instance().set("POPULATION", "size", "5")
Config.get_instance().set("BEHAVIOUR", "save", "false")
from MLC.Log.log import set_logger
set_logger('testing')
population = Population(5, 0, Config.get_instance(),
MLCRepository.make(""))
population.fill(fill_creator)
MLCRepository.get_instance().add_population(population)
# Assert that one Population was added
self.assertEqual(MLCRepository.get_instance().count_population(),
1)
# Assert that the individuals are in the expected position inside the Population
for position, i in enumerate(expected_pop_indexes):
expected_individual = expected_individuals[i]
inserted_individual_id = population.get_individuals()[position]
inserted_individual = MLCRepository.get_instance(
).get_individual(inserted_individual_id)
self.assertEqual(expected_individual.get_value(),
inserted_individual.get_value())
def test_crossover_same_level_2(self):
individual_1 = Individual(
"(root (cos (* (+ (* -1.912 -9.178) (cos S0)) 3.113)))",
Config.get_instance())
individual_2 = Individual(
"(root (cos (* (+ (* -1.912 -9.178) (cos S0)) 3.113)))",
Config.get_instance())
new_ind_1, new_ind_2, _ = individual_1.crossover(individual_2)
self._assert_individual(
new_ind_1,
complexity=8,
value="(root (cos (* (* -1.912 -9.178) 3.113)))",
formal="cos((((-1.912) .* (-9.178)) .* 3.113))")
self._assert_individual(
new_ind_2,
complexity=18,
value=
"(root (cos (* (+ (+ (* -1.912 -9.178) (cos S0)) (cos S0)) 3.113)))",
formal=
"cos((((((-1.912) .* (-9.178)) + cos(S0)) + cos(S0)) .* 3.113))")
