def get_next_cost_dict(self,params_dict):
'''
Implementation of file read in and out. Put parameters into a file and wait for a cost file to be returned.
'''
self.out_file_count += 1
self.log.debug('Writing out_params to file. Count:' + repr(self.out_file_count))
self.last_params_dict = params_dict
mlu.save_dict_to_file(self.last_params_dict,self.total_out_filename,self.out_file_type)
while not self.end_event.is_set():
if os.path.isfile(self.total_in_filename):
time.sleep(mlu.filewrite_wait) #wait for file to be written to disk
try:
in_dict = mlu.get_dict_from_file(self.total_in_filename, self.in_file_type)
except IOError:
self.log.warning('Unable to open ' + self.total_in_filename + '. Trying again.')
continue
except (ValueError,SyntaxError):
self.log.error('There is something wrong with the syntax or type of your file:' + self.in_filename + '.' + self.in_file_type)
raise
os.remove(self.total_in_filename)
self.in_file_count += 1
self.log.debug('Putting dict from file onto in queue. Count:' + repr(self.in_file_count))
break
else:
time.sleep(self.interface_wait)
else:
raise InterfaceInterrupt
return in_dict
def run(self):
'''
Implementation of file read in and out. Put parameters into a file and wait for a cost file to be returned.
'''
self.log.debug('Entering FakeExperiment loop')
while not self.end_event.is_set():
if os.path.isfile(self.total_in_filename):
time.sleep(mlu.filewrite_wait) #wait for file to be written
try:
in_dict = mlu.get_dict_from_file(self.total_in_filename, self.in_file_type)
except IOError:
self.log.warning('Unable to open ' + self.total_in_filename + '. Trying again.')
continue
except (ValueError,SyntaxError):
self.log.error('There is something wrong with the syntax or type of your file:' + self.in_filename + '.' + self.in_file_type)
raise
os.remove(self.total_in_filename)
self.test_count +=1
self.log.debug('Test exp evaluating cost. Num:' + repr(self.test_count))
try:
params = in_dict['params']
except KeyError as e:
self.log.error('You are missing ' + repr(e.args[0]) + ' from the in params dict you provided through the queue.')
raise
cost_dict = self.test_landscape.get_cost_dict(params)
time.sleep(self.exp_wait)
mlu.save_dict_to_file(cost_dict, self.total_out_filename, self.out_file_type)
else:
time.sleep(self.poll_wait)
self.log.debug('Ended FakeExperiment')
def __init__(self, filename,
file_type ='pkl',
**kwargs):
self.log = logging.getLogger(__name__)
self.filename = str(filename)
self.file_type = str(file_type)
if not mlu.check_file_type_supported(self.file_type):
self.log.error('GP training file type not supported' + repr(self.file_type))
learner_dict = mlu.get_dict_from_file(self.filename, self.file_type)
if 'archive_type' in learner_dict and not (learner_dict['archive_type'] == 'differential_evolution'):
self.log.error('The archive appears to be the wrong type.' + repr(learner_dict['archive_type']))
raise ValueError
self.archive_type = learner_dict['archive_type']
self.num_generations = int(learner_dict['generation_count'])
self.num_population_members = int(learner_dict['num_population_members'])
self.num_params = int(learner_dict['num_params'])
self.min_boundary = np.squeeze(np.array(learner_dict['min_boundary']))
self.max_boundary = np.squeeze(np.array(learner_dict['max_boundary']))
self.params_generations = np.array(learner_dict['params_generations'])
self.costs_generations = np.array(learner_dict['costs_generations'])
self.finite_flag = True
self.param_scaler = lambda p: (p-self.min_boundary)/(self.max_boundary - self.min_boundary)
self.scaled_params_generations = np.array([[self.param_scaler(self.params_generations[inda,indb,:]) for indb in range(self.num_population_members)] for inda in range(self.num_generations)])
self.gen_numbers = np.arange(1,self.num_generations+1)
self.param_colors = _color_list_from_num_of_params(self.num_params)
self.gen_plot = np.array([np.full(self.num_population_members, ind, dtype=int) for ind in self.gen_numbers]).flatten()
def get_next_cost_dict(self,params_dict):
'''
Implementation of file read in and out. Put parameters into a file and wait for a cost file to be returned.
'''
self.out_file_count += 1
self.log.debug('Writing out_params to file. Count:' + repr(self.out_file_count))
self.last_params_dict = params_dict
mlu.save_dict_to_file(self.last_params_dict,self.total_out_filename,self.out_file_type)
while not self.end_event.is_set():
if os.path.isfile(self.total_in_filename):
time.sleep(mlu.filewrite_wait) #wait for file to be written to disk
try:
in_dict = mlu.get_dict_from_file(self.total_in_filename, self.in_file_type)
except IOError:
self.log.warning('Unable to open ' + self.total_in_filename + '. Trying again.')
continue
except (ValueError,SyntaxError):
self.log.error('There is something wrong with the syntax or type of your file:' + self.in_filename + '.' + self.in_file_type)
raise
os.remove(self.total_in_filename)
self.in_file_count += 1
self.log.debug('Putting dict from file onto in queue. Count:' + repr(self.in_file_count))
break
else:
time.sleep(self.interface_wait)
else:
raise InterfaceInterrupt
return in_dict
def __init__(self, filename, file_type='pkl', **kwargs):
self.log = logging.getLogger(__name__)
self.filename = str(filename)
self.file_type = str(file_type)
if not mlu.check_file_type_supported(self.file_type):
self.log.error('GP training file type not supported' +
repr(self.file_type))
controller_dict = mlu.get_dict_from_file(self.filename, self.file_type)
self.archive_type = controller_dict['archive_type']
if 'archive_type' in controller_dict and not (
controller_dict['archive_type'] == 'controller'):
self.log.error('The archive appears to be the wrong type.')
raise ValueError
self.num_in_costs = int(controller_dict['num_in_costs'])
self.num_out_params = int(controller_dict['num_out_params'])
self.out_params = np.array(controller_dict['out_params'])
self.out_type = [x.strip() for x in list(controller_dict['out_type'])]
self.in_costs = np.squeeze(np.array(controller_dict['in_costs']))
self.in_uncers = np.squeeze(np.array(controller_dict['in_uncers']))
self.in_bads = np.squeeze(list(controller_dict['in_bads']))
self.best_index = int(controller_dict['best_index'])
self.num_params = int(controller_dict['num_params'])
self.min_boundary = np.squeeze(
np.array(controller_dict['min_boundary']))
self.max_boundary = np.squeeze(
np.array(controller_dict['max_boundary']))
if np.all(np.isfinite(self.min_boundary)) and np.all(
np.isfinite(self.min_boundary)):
self.finite_flag = True
self.param_scaler = lambda p: (p - self.min_boundary) / (
self.max_boundary - self.min_boundary)
self.scaled_params = np.array([
self.param_scaler(self.out_params[ind, :])
for ind in range(self.num_out_params)
])
else:
self.finite_flag = False
self.unique_types = set(self.out_type)
self.cost_colors = [
_color_from_controller_name(x) for x in self.out_type
]
self.in_numbers = np.arange(1, self.num_in_costs + 1)
self.out_numbers = np.arange(1, self.num_out_params + 1)
self.param_colors = _color_list_from_num_of_params(self.num_params)
def __init__(self, filename,
file_type ='pkl',
**kwargs):
self.log = logging.getLogger(__name__)
self.filename = str(filename)
self.file_type = str(file_type)
if not mlu.check_file_type_supported(self.file_type):
self.log.error('GP training file type not supported' + repr(self.file_type))
controller_dict = mlu.get_dict_from_file(self.filename, self.file_type)
self.archive_type = controller_dict['archive_type']
if 'archive_type' in controller_dict and not (controller_dict['archive_type'] == 'controller'):
self.log.error('The archive appears to be the wrong type.')
raise ValueError
self.num_in_costs = int(controller_dict['num_in_costs'])
self.num_out_params = int(controller_dict['num_out_params'])
self.out_params = np.array(controller_dict['out_params'])
self.out_type = [x.strip() for x in list(controller_dict['out_type'])]
self.in_costs = np.squeeze(np.array(controller_dict['in_costs']))
self.in_uncers = np.squeeze(np.array(controller_dict['in_uncers']))
self.in_bads = np.squeeze(list(controller_dict['in_bads']))
self.best_index = int(controller_dict['best_index'])
self.num_params = int(controller_dict['num_params'])
self.min_boundary = np.squeeze(np.array(controller_dict['min_boundary']))
self.max_boundary = np.squeeze(np.array(controller_dict['max_boundary']))
if np.all(np.isfinite(self.min_boundary)) and np.all(np.isfinite(self.min_boundary)):
self.finite_flag = True
self.param_scaler = lambda p: (p-self.min_boundary)/(self.max_boundary - self.min_boundary)
self.scaled_params = np.array([self.param_scaler(self.out_params[ind,:]) for ind in range(self.num_out_params)])
else:
self.finite_flag = False
self.unique_types = set(self.out_type)
self.cost_colors = [_color_from_controller_name(x) for x in self.out_type]
self.in_numbers = np.arange(1,self.num_in_costs+1)
self.out_numbers = np.arange(1,self.num_out_params+1)
self.param_colors = _color_list_from_num_of_params(self.num_params)
def __init__(self, filename, file_type='pkl', **kwargs):
self.log = logging.getLogger(__name__)
self.filename = str(filename)
self.file_type = str(file_type)
if not mlu.check_file_type_supported(self.file_type):
self.log.error('GP training file type not supported' +
repr(self.file_type))
learner_dict = mlu.get_dict_from_file(self.filename, self.file_type)
if 'archive_type' in learner_dict and not (
learner_dict['archive_type'] == 'differential_evolution'):
self.log.error('The archive appears to be the wrong type.' +
repr(learner_dict['archive_type']))
raise ValueError
self.archive_type = learner_dict['archive_type']
self.num_generations = int(learner_dict['generation_count'])
self.num_population_members = int(
learner_dict['num_population_members'])
self.num_params = int(learner_dict['num_params'])
self.min_boundary = np.squeeze(np.array(learner_dict['min_boundary']))
self.max_boundary = np.squeeze(np.array(learner_dict['max_boundary']))
self.params_generations = np.array(learner_dict['params_generations'])
self.costs_generations = np.array(learner_dict['costs_generations'])
self.finite_flag = True
self.param_scaler = lambda p: (p - self.min_boundary) / (
self.max_boundary - self.min_boundary)
self.scaled_params_generations = np.array([[
self.param_scaler(self.params_generations[inda, indb, :])
for indb in range(self.num_population_members)
] for inda in range(self.num_generations)])
self.gen_numbers = np.arange(1, self.num_generations + 1)
self.param_colors = _color_list_from_num_of_params(self.num_params)
self.gen_plot = np.array([
np.full(self.num_population_members, ind, dtype=int)
for ind in self.gen_numbers
]).flatten()
def launch_from_file(config_filename, **kwargs):
'''
Launch M-LOOP using a configuration file. See configuration file documentation.
Args:
config_filename (str): Filename of configuration file
**kwargs : keywords that override the keywords in the file.
Returns:
controller (Controller): Controller for optimization.
'''
try:
file_kwargs = mlu.get_dict_from_file(config_filename, 'txt')
except (IOError, OSError):
print('Unable to open M-LOOP configuration file:' +
repr(config_filename))
raise
file_kwargs.update(kwargs)
#Main run sequence
#Create interface and extract unused keywords
interface = mli.create_interface(**file_kwargs)
file_kwargs = interface.remaining_kwargs
#Create controller and extract unused keywords
controller = mlc.create_controller(interface, **file_kwargs)
file_kwargs = controller.remaining_kwargs
#Extract keywords for post processing extras, and raise an error if any keywords were unused.
extras_kwargs = _pop_extras_kwargs(file_kwargs)
if file_kwargs:
logging.getLogger(__name__).error('Unused extra options provided:' +
repr(file_kwargs))
raise ValueError
#Run the actual optimization
controller.optimize()
#Launch post processing extras
launch_extras(controller, **extras_kwargs)
return controller
def launch_from_file(config_filename,
**kwargs):
'''
Launch M-LOOP using a configuration file. See configuration file documentation.
Args:
config_filename (str): Filename of configuration file
**kwargs : keywords that override the keywords in the file.
Returns:
controller (Controller): Controller for optimization.
'''
try:
file_kwargs = mlu.get_dict_from_file(config_filename,'txt')
except (IOError, OSError):
print('Unable to open M-LOOP configuration file:' + repr(config_filename))
raise
file_kwargs.update(kwargs)
#Main run sequence
#Create interface and extract unused keywords
interface = mli.create_interface(**file_kwargs)
file_kwargs = interface.remaining_kwargs
#Create controller and extract unused keywords
controller = mlc.create_controller(interface, **file_kwargs)
file_kwargs = controller.remaining_kwargs
#Extract keywords for post processing extras, and raise an error if any keywords were unused.
extras_kwargs = _pop_extras_kwargs(file_kwargs)
if file_kwargs:
logging.getLogger(__name__).error('Unused extra options provided:' + repr(file_kwargs))
raise ValueError
#Run the actual optimization
controller.optimize()
#Launch post processing extras
launch_extras(controller, **extras_kwargs)
return controller
