class TestSimulationExecutor(unittest.TestCase):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
logging.basicConfig(level=logging.INFO,
filename=r"C:\Users\Michał\PycharmProjects\mgr\sgcs\log.log",
format='%(asctime)s %(message)s')
self.sut = SimulationExecutor()
self.starting_symbol = Symbol(1)
def test_population_saving_and_loading(self):
# Given:
path = r"C:\Users\Michał\PycharmProjects\mgr\sgcs\sgcs\data\experimental"
name = 'tmp'
terminal_rule = Rule(Symbol(101), Symbol(-101))
non_terminal_rule = Rule(Symbol(101), Symbol(101), Symbol(101))
population = StochasticRulePopulation(self.starting_symbol)
population.add_rule(terminal_rule, self.sut.randomizer)
population.add_rule(non_terminal_rule, self.sut.randomizer)
# When:
self.sut.save_population(population, lambda _: '', path, name)
loaded_pop = self.sut.load_population(path, name, starting_symbol=self.starting_symbol)
# Then:
assert_that(loaded_pop.get_all_non_terminal_rules(), only_contains(non_terminal_rule))
assert_that(loaded_pop.get_terminal_rules(), only_contains(terminal_rule))
def __init__(self, runner):
super().__init__(runner.widget)
self.runner = runner
self.is_running = False
self.simulation_executor = SimulationExecutor()
self.current_data = RunnerGuiModel()
self.root_dir = self.runner.scheduler.ui.outputDirectorylineEdit.text()
def __init__(self, population_editor):
super().__init__(population_editor.widget)
self.population_editor = population_editor
self.translator = None
self.population_executor = PopulationExecutor()
self.simulation_executor = SimulationExecutor()
self.operation = None
self.randomizer = Randomizer(Random())
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
logging.basicConfig(level=logging.INFO,
filename=r"C:\Users\Michał\PycharmProjects\mgr\sgcs\log.log",
format='%(asctime)s %(message)s')
self.sut = SimulationExecutor()
self.starting_symbol = Symbol(1)
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
logging.basicConfig(level=logging.INFO,
filename=r"C:\Users\Michał\PycharmProjects\mgr\sgcs\log.log",
format='%(asctime)s %(message)s')
self.sut = SimulationExecutor()
self.runner_dummy = RunnerDummy()
self.task_no = 0
self.config_path = \
r"C:\Users\Michał\PycharmProjects\mgr\sgcs\sgcs\data\experimental\unold0.parconf"
class LongTestSimulationExecutor(unittest.TestCase):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
logging.basicConfig(level=logging.INFO,
filename=r"C:\Users\Michał\PycharmProjects\mgr\sgcs\log.log",
format='%(asctime)s %(message)s')
self.sut = SimulationExecutor()
self.runner_dummy = RunnerDummy()
self.task_no = 0
self.config_path = \
r"C:\Users\Michał\PycharmProjects\mgr\sgcs\sgcs\data\experimental\unold0.parconf"
def test_tomita_1(self):
input_data = r"C:\Users\Michał\PycharmProjects\mgr\sgcs\sgcs\data\experimental\t1.inconf"
run_func, conf = self.sut.prepare_simulation(
self.runner_dummy, self.task_no, input_data, self.config_path)
run_func(conf)
def load_config_action(self):
selected_filename = QtGui.QFileDialog.getOpenFileName(
self.widget, 'Load config...', self.last_directory, self.DATA_CONFIG_EXT)
if selected_filename:
self.selected_learning_filename, self.selected_testing_filename = \
SimulationExecutor.load_input_config(selected_filename)
class LoadPopulationWorker(QtCore.QThread):
TRANSLATOR_READY_SIGNAL = 'TRANSLATOR_READY_SIGNAL'
POPULATION_LOADED_SIGNAL = 'POPULATION_LOADED_SIGNAL'
STARTING_SYMBOL = Symbol(1)
def __init__(self, population_editor):
super().__init__(population_editor.widget)
self.population_editor = population_editor
self.translator = None
self.population_executor = PopulationExecutor()
self.simulation_executor = SimulationExecutor()
self.operation = None
self.randomizer = Randomizer(Random())
def run(self):
self.emit(QtCore.SIGNAL(AsyncProgressDialog.CHANGE_STEP_EVENT), 'Operation in progress...')
self.emit(QtCore.SIGNAL(AsyncProgressDialog.SET_PROGRESS_EVENT), -1)
if self.operation is not None:
# noinspection PyCallingNonCallable
self.operation()
self.emit(QtCore.SIGNAL(AsyncProgressDialog.RESET_EVENT))
def load_learning_set_operation(self):
try:
self.translator = self.population_executor.get_learned_translator(
self.population_editor.population_path)
except:
dialog = QtGui.QMessageBox()
dialog.critical(
self.population_editor.widget, 'Invalid file',
'File "{0}" has invalid format!'.format(self.population_editor.population_path))
finally:
self.emit(QtCore.SIGNAL(self.TRANSLATOR_READY_SIGNAL))
def load_population_operation(self):
name = os.path.basename(self.population_editor.population_path).split('.')[0]
dir_path = os.path.dirname(self.population_editor.population_path)
rule_population = self.simulation_executor.load_population(
dir_path, name, starting_symbol=self.STARTING_SYMBOL)
self.population_editor.rules.clear()
for rule in rule_population.get_terminal_rules():
translated = rule.human_friendly_representation(rule_population.symbol_shift(),
rule_population.starting_symbol,
rule_population.universal_symbol)
rule_model = TerminalRuleModel()
rule_model.parent = translated[0]
rule_model.left_child = self.translator.symbol_to_word(rule.left_child)
self.population_editor.rules.append(rule_model)
for rule in rule_population.get_all_non_terminal_rules():
translated = rule.human_friendly_representation(rule_population.symbol_shift(),
rule_population.starting_symbol,
rule_population.universal_symbol)
rule_model = NonTerminalRuleModel()
rule_model.parent, rule_model.left_child, rule_model.right_child = translated
self.population_editor.rules.append(rule_model)
self.emit(QtCore.SIGNAL(self.POPULATION_LOADED_SIGNAL))
def save_population_operation(self):
population = RulePopulation(self.STARTING_SYMBOL)
for rule_model in self.population_editor.rules:
if rule_model.is_terminal:
parent, terminal_word = Rule.from_human_friendly_representation(
population.symbol_shift(),
population.starting_symbol,
population.universal_symbol,
rule_model.parent,
rule_model.left_child,
rule_model.right_child
)
terminal_symbol = self.translator.word_to_symbol(terminal_word)
population.add_rule(Rule(parent, terminal_symbol), self.randomizer)
else:
parent, left, right = Rule.from_human_friendly_representation(
population.symbol_shift(),
population.starting_symbol,
population.universal_symbol,
rule_model.parent,
rule_model.left_child,
rule_model.right_child
)
population.add_rule(Rule(parent, left, right), self.randomizer)
name = os.path.basename(self.population_editor.population_path).split('.')[0]
path = os.path.dirname(self.population_editor.population_path)
self.simulation_executor.save_population_data(population, path, name)
def main():
logging.basicConfig(level=logging.INFO, format="%(asctime)s %(message)s")
usage = "usage: %prog -o -OUTPUT_DIR [options] [input, config [,starting_rules]]..."
parser = OptionParser(usage=usage)
parser.add_option(
"-p",
"--print_only",
action="store_false",
dest="run",
default=True,
help="Only shows configuration, does not run it",
)
parser.add_option("-o", "--output", dest="output", default=None, help="Destination")
parser.add_option(
"-s",
"--starting_rules",
action="store_true",
dest="starting_rules",
default=False,
help="Script expects triples of data(input, config, starting_rules) "
+ "instead of doubles (input, config), if used.",
)
options, args = parser.parse_args()
if options.output is None:
print("No output dir specified!")
return
print("artifact directory:", options.output)
chunk_size = 3 if options.starting_rules else 2
if not args or len(args) % chunk_size != 0:
print("Invalid number of arguments!")
return
i = 0
tasks = []
for input_file, config_file, *rest in chunk(args, chunk_size):
print("Task", i)
task = TaskModel()
print("\tinput:", input_file)
task.data_configuration = input_file
print("\tconfig:", config_file)
task.params_configuration = config_file
starting_population = None
if rest:
starting_population = rest[0]
print("\tstarting population:", starting_population)
task.population_configuration = starting_population
tasks.append(task)
if options.run:
print("Starting run")
executor = SimulationExecutor()
runner = Runner()
for i, x in enumerate(tasks):
run_func, configuration, population_printer = executor.prepare_simulation(
runner, i, x.data_configuration, x.params_configuration, x.population_configuration
)
result = run_func(configuration)
collected = False
while not collected:
try:
_collect_task(x, result, i, configuration, population_printer, executor, options.output)
except PermissionError:
collected = False
print("not collected!")
else:
collected = True
class SimulationWorker(QtCore.QThread):
TASK_CONFIRMED_FINISHED_SIGNAL = 'TASK_CONFIRMED_FINISHED_SIGNAL'
ALL_TASKS_FINISHED_SIGNAL = 'ALL_TASKS_FINISHED_SIGNAL'
def __init__(self, runner):
super().__init__(runner.widget)
self.runner = runner
self.is_running = False
self.simulation_executor = SimulationExecutor()
self.current_data = RunnerGuiModel()
self.root_dir = self.runner.scheduler.ui.outputDirectorylineEdit.text()
def run(self):
for i, task in enumerate(self.runner.scheduler.tasks):
run_func, configuration, population_printer = self._setup_task(i, task)
result = self._run_task(run_func, configuration)
collected = False
while not collected:
try:
self._collect_task(result, i, configuration, population_printer)
except PermissionError:
collected = False
self.current_data.current_phase = SimulationPhases.PERMISSION_ERROR
else:
collected = True
self.current_data.current_phase = SimulationPhases.COLLECTING
self.current_data.current_phase = SimulationPhases.DONE
self.emit(QtCore.SIGNAL(self.ALL_TASKS_FINISHED_SIGNAL))
def _setup_task(self, task_no, task):
new_data = RunnerGuiModel()
new_data.tasks_progress = task_no
new_data.current_input = task.data_configuration
new_data.current_config = task.params_configuration
new_data.current_phase = SimulationPhases.SETUP
self.current_data = new_data
run_func, configuration, pop_printer = self.simulation_executor.prepare_simulation(
self.runner, task_no, new_data.current_input, new_data.current_config,
population_path=task.population_configuration)
run_post_mortem_data = []
for _ in range(configuration.max_algorithm_runs):
task_progress_model = RunPostMortemModel()
task_progress_model.max_steps = configuration.max_algorithm_steps
task_progress_model.task_no = task_no
run_post_mortem_data.append(task_progress_model)
self.runner.run_progress_data = run_post_mortem_data
return run_func, configuration, pop_printer
def _run_task(self, run_func, configuration):
self.current_data.current_phase = SimulationPhases.LEARNING
return run_func(configuration)
def _collect_task(self, result, task_id, configuration, population_printer):
self.current_data.current_phase = SimulationPhases.COLLECTING
run_estimator, ngen, grammar_estimator, population, generalisation_data = result
path = self._prepare_artifact_dir(task_id)
input_data_name = os.path.basename(self.current_data.current_input)
config_data_name = os.path.basename(self.current_data.current_config)
shutil.copy(self.current_data.current_input, os.path.join(path, input_data_name))
shutil.copy(self.current_data.current_config, os.path.join(path, config_data_name))
self.simulation_executor.save_population(
population, population_printer, path, 'final_population'
)
self.simulation_executor.save_grammar_estimator(
grammar_estimator, path, 'grammar_estimator'
)
self.simulation_executor.save_execution_summary(
run_estimator, ngen, generalisation_data, path, 'run_summary'
)
self.simulation_executor.generate_grammar_estimation_diagrams(
grammar_estimator, path, configuration
)
def _prepare_artifact_dir(self, task_id):
path = os.path.join(self.root_dir, 'task_{0}'.format(task_id))
if os.path.exists(path):
rmdir_forced(path)
os.mkdir(path)
return path