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 __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 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 main():
# MATLAB random numbers, used in integration tests
RandomManager.load_random_values(
"./tests/integration_tests/matlab_randoms.txt")
# load configuration
config = initialize_config()
# set logger
log_mode = config.get('LOGGING', 'logmode')
set_logger(log_mode)
simulation = Simulation()
mlc = Application(simulation)
mlc.go(to_generation=3, display_best=False)
raw_input("Press enter to continue...")
def setUp(self):
set_logger("testing")
# Load randoms from file
random_file = './mlc/unit_matlab_randoms.txt'
RandomManager.clear_random_values()
RandomManager.load_random_values(random_file)
config = Config.get_instance()
config.read(
os.path.join(mlc_config_path.get_test_path(),
'mlc/individual/configuration.ini'))
self._individual_l0 = Individual("(root (cos 5.046))")
self._individual_l1 = Individual(
"(root (log (sin (exp (tanh 3.6284)))))")
self._individual_l2 = Individual(
"(root (cos (* (+ (* -1.912 -9.178) (cos S0)) 3.113)))")
self._individual_l3 = Individual(
"(root (log (/ (* (sin 4.37) (- -8.815 -3.902)) (log (+ 2.025 -8.685)))))"
)
self._individual_l4 = Individual("(root S0)")
def setUp(self):
try:
os.makedirs(MLCWorkspaceTest.WORKSPACE_DIR)
except OSError:
shutil.rmtree(MLCWorkspaceTest.WORKSPACE_DIR)
os.makedirs(MLCWorkspaceTest.WORKSPACE_DIR)
mlc = MLCLocal(working_dir=MLCWorkspaceTest.WORKSPACE_DIR)
# general settings for MLC
set_logger('console')
set_working_directory(MLCWorkspaceTest.WORKSPACE_DIR)
this_dir = os.path.dirname(os.path.abspath(__file__))
MLCWorkspaceTest.ORIGINAL_CONFIGURATION = os.path.join(this_dir,
MLCWorkspaceTest.ORIGINAL_EXPERIMENT + ".conf")
MLCWorkspaceTest.NEW_CONFIGURATION = os.path.join(this_dir,
MLCWorkspaceTest.NEW_EXPERIMENT + ".conf")
mlc.new_experiment(MLCWorkspaceTest.ORIGINAL_EXPERIMENT,
MLCWorkspaceTest.ORIGINAL_CONFIGURATION)
# random file for simulations
MLCWorkspaceTest.FILE_WITH_RANDOMS = os.path.join(this_dir, "matlab_randoms.txt")
import MLC.Log.log as lg
from MLC.Common.LispTreeExpr.LispTreeExpr import LispTreeExpr
from MLC.Log.log import set_logger
from MLC.mlc_parameters.mlc_parameters import Config
def initialize_config():
config = Config.get_instance()
config.read('/home/htomar/MLC-0.0.5/Clone_18/Clone_18.conf')
return config
# Set printable resolution (don't alter numpy interval resolution)
np.set_printoptions(precision=9)
# Show full arrays, no matter what size do they have
np.set_printoptions(threshold=np.inf)
# Don't show scientific notation
np.set_printoptions(suppress=True)
initialize_config()
set_logger('console')
# Full expression
expr6 = "(root (/ (+ 636.4469 (cos S6)) (log (* 1069.5890 (/ (/ (/ (/ (/ (sin (- -1491.0946 (- S7 1541.5198))) (- (exp (sin (- -701.6797 (- 394.8346 (- S5 0.5484))))) -0.1508)) (exp (- (sin (sin (/ S3 1053.8509))) -0.0004))) (tanh (exp (log S6)))) (exp (sin (sin (tanh (sin (sin (- -701.6797 (- 394.8346 (- S5 0.5484)))))))))) (tanh (exp (- (tanh (- (log S3) (exp (sin (- -701.6797 (- 394.8346 (- S5 0.5484))))))) -0.0004))))))))"
expr61 = "(root (exp (- -6.3726 (* -7.1746 S0))))"
expr612 = "(root (- -6.3726 (* -7.1746 S0)))"
tree = LispTreeExpr(expr6)
out = LispTreeExpr.formal(tree)
print out
def setUpClass(cls):
set_logger("file")
config = Config.get_instance()
config.read(
os.path.join(config_path.get_test_path(),
'mlc/individual/configuration.ini'))
S1 = np.array(g_data[1]) # Temperature
S2 = np.array(g_data[2]) # Wind
S3 = np.array(g_data[3]) # Solar
S4 = np.array(g_data[4]) # Humidity
S5 = np.array(g_data[5]) # IsHoliday
S6 = np.array(g_data[6]) # Day of the Week
S7 = np.array(g_data[7])
y = np.array(g_data[8]) # Whole Building Energy
# func = (((636.4469 + np.cos(S6)))/(np.log((1069.5890 * ((((((((((np.sin(((-1491.0946) - (S7 - 1541.5198))))/((np.exp(np.sin(((-701.6797) - (394.8346 - (S5 - 0.5484))))) - (-0.1508)))))/(np.exp((np.sin(np.sin(((S3)/(1053.8509)))) - (-0.0004))))))/(np.tanh(np.exp(np.log(S6))))))/(np.exp(np.sin(np.sin(np.tanh(np.sin(np.sin(((-701.6797) - (394.8346 - (S5 - 0.5484))))))))))))/(np.tanh(np.exp((np.tanh((np.log(S3) - np.exp(np.sin(((-701.6797) - (394.8346 - (S5 - 0.5484))))))) - (-0.0004))))))))))
func = "(root (/ (+ 636.4469 (cos S6)) (log (* 1069.5890 (/ (/ (/ (/ (/ (sin (- -1491.0946 (- S7 1541.5198))) (- (exp (sin (- -701.6797 (- 394.8346 (- S5 0.5484))))) -0.1508)) (exp (sin (sin (tanh (sin (tanh (/ S3 1053.8509)))))))) (tanh (exp (/ (tanh (/ (/ S3 1053.8509) 0.1508)) 0.1508)))) (exp (sin (sin (/ (sin (tanh (sin (tanh (/ S3 1053.8509))))) (- (/ S3 1053.8509) -0.1508)))))) (tanh (exp (/ (/ (/ (/ S3 1053.8509) (- (- (sin (tanh (/ S3 1053.8509))) -0.0004) -0.1508)) (- (cos (/ -1994.7186 S5)) -0.1508)) (tanh (exp (sin S6)))))))))))"
initialize_config()
# Don't log anything
set_logger('testing')
# Evaluate the expression
tree = LispTreeExpr(func)
b = tree.calculate_expression([S0, S1, S2, S3, S4, S5, S6, S7])
# Compare the expressions
#==============================================================================
# z = (np.vstack((b, y, S5, S6, S7))).T
# for index in xrange(len(z)):
# print ("Index: {}\t func: {}\t y: {}\t S3: {}\t S5: {}\t S6: {}\t S7: {}"
# .format(index, z[index][0], z[index][1], S3[index], S5[index], S6[index], S7[index]))
#==============================================================================
y_mean = np.mean(y)
sq_diff = np.dot((y-b), (y-b))