def fit(self, my_lambda, coverage=3, max_iters=50):
#while(True):
print('beta = ', self.my_beta)
print('lambda = ', my_lambda)
my_args = (self, my_lambda, coverage, False)
nfeatures = self.train_args['feature'].shape[1]
w0 = self.randomw0(nfeatures)
sigma_value = 0.02
print('sigma0 ', sigma_value)
es = CMAEvolutionStrategy(w0,
sigma0=sigma_value,
inopts={
'maxiter': max_iters,
'popsize': 40
})
while not es.stop():
solutions = es.ask()
fitnesses = [
InterestingnessLearner.cost(x, *my_args) for x in solutions
]
es.tell(solutions, fitnesses)
es.disp()
self.nmcost(es.result[0], my_lambda, coverage, is_debug=True)
final_model = self.createClassifier(es.result[0], coverage)
print(final_model.size(), final_model.meanSupport())
return final_model
def main(filename, directory):
initialFilename = os.path.join(directory, "initial.json")
genDir = os.path.join(directory, "gens")
finalFilename = os.path.join(directory, "final.json")
baseCondor = "base.condor"
with open(baseCondor, "r") as f:
condorConfig = f.read()
optMkdir(directory)
optMkdir(genDir)
shutil.copyfile(filename, initialFilename)
with open(filename, "r") as f:
conf = json.load(f)
paramNames = []
paramVals = []
flattenParams(conf, paramNames, paramVals, "")
sigma0 = SIGMA0
opts = Options()
opts["popsize"] = POP_SIZE
# opts.printme()
cma = CMAEvolutionStrategy(paramVals, sigma0, opts)
while (cma.countiter < NUM_GENS) and not (cma.stop()):
thisGenDir = os.path.join(genDir, str(cma.countiter))
optMkdir(thisGenDir)
xs = cma.ask()
xs, fits = runEvals(paramNames, xs, cma.countiter, thisGenDir, condorConfig)
cma.tell(xs, fits)
res = cma.result()
paramsToJsonFile(finalFilename, paramNames, res[0])
def main(filename, directory):
initialFilename = os.path.join(directory, 'initial.json')
genDir = os.path.join(directory, 'gens')
finalFilename = os.path.join(directory, 'final.json')
baseCondor = 'base.condor'
with open(baseCondor, 'r') as f:
condorConfig = f.read()
optMkdir(directory)
optMkdir(genDir)
shutil.copyfile(filename, initialFilename)
with open(filename, 'r') as f:
conf = json.load(f)
paramNames = []
paramVals = []
flattenParams(conf, paramNames, paramVals, '')
sigma0 = SIGMA0
opts = Options()
opts['popsize'] = POP_SIZE
#opts.printme()
cma = CMAEvolutionStrategy(paramVals, sigma0, opts)
while (cma.countiter < NUM_GENS) and not (cma.stop()):
thisGenDir = os.path.join(genDir, str(cma.countiter))
optMkdir(thisGenDir)
xs = cma.ask()
xs, fits = runEvals(paramNames, xs, cma.countiter, thisGenDir,
condorConfig)
cma.tell(xs, fits)
res = cma.result()
paramsToJsonFile(finalFilename, paramNames, res[0])
def main():
# param length
# 10 fights
# rating_mu, rating_sig, wins, losses, odds
input_params_len = 10 * 2 * 4
es = CMAEvolutionStrategy([0] * input_params_len, 0.5)
while not es.stop():
solutions = es.ask()
func_vals = [get_betting_result(x) for x in solutions]
es.tell(solutions, func_vals)
es.logger.add() # write data to disc to be plotted
es.disp()
es.result_pretty()
def update_borders(self):
for i in range(len(self.clusters)):
cluster = self.clusters[i]
len_bounds = np.linalg.norm(cluster.border[1] - cluster.border[1])
es = CMAEvolutionStrategy(
cluster.border.ravel().tolist(), len_bounds * 0.1, {
'bounds':
[self.boundary.min_bounds[0], self.boundary.max_bounds[0]]
})
while not es.stop():
solutions = es.ask()
#TODO
es.tell(
solutions,
[self.evaluate_border(border, i) for border in solutions])
#es.tell( solutions, [cluster.evaluate_border(border) for border in solutions] )
x_best = es.result()[0]
#if x_best is not None and cluster.in_global_border( x_best ):
if x_best is not None:
cluster.border = x_best.reshape(cluster.border.shape)
'''
def run():
train = 0
names = [
# 'bet_pred_a', 'bet_pred_b', 'bet_odds_a', 'bet_odds_b', 'bet_wnl_a', 'bet_wnl_b',
'bet_ts_a', 'bet_ts_b', 'bet_tmi_a', 'bet_tmi_b', 'bet_tma_a', 'bet_tma_b',
]
params = [
0, 0, 0, 0, 0, 0
]
bounds = [[-np.inf],
[np.inf]]
assert len(params) == len(names)
# assert len(params) == len(bounds[0])
if train:
sigma = 1
opts = CMAOptions()
# opts['tolx'] = 1E-2
opts['bounds'] = bounds
es = CMAEvolutionStrategy(params, sigma, inopts=opts)
while not es.stop():
solutions = es.ask()
fitness = [main(x, train=1) for x in solutions]
es.tell(solutions, fitness)
es.disp()
print(list(es.result[0]))
print(list(es.result[5]))
es.result_pretty()
print('')
print('best')
print(list(es.result[0]))
print('')
print('xfavorite: distribution mean in "phenotype" space, to be considered as current best estimate of the optimum')
print(list(es.result[5]))
else:
main(params)
def cma_minimize(acq_function,
bounds,
return_best_only=True,
**kwargs) -> Tuple[torch.Tensor, ...]:
x0 = 0.5 * np.ones(bounds.shape[-1])
opts = {
'bounds': [0, 1],
"popsize": kwargs.get('popsize', 100),
"seed": 10,
"verbose": -1
}
if "maxiter" in kwargs:
opts.update(maxiter=kwargs["maxiter"])
es = CMAEvolutionStrategy(x0=x0,
sigma0=kwargs.get('sigma0', 0.5),
inopts=opts)
xs_list, y_list = [], []
with torch.no_grad():
while not es.stop():
xs = es.ask()
X = torch.tensor(xs, dtype=torch.float64)
Y = acq_function(X.unsqueeze(-2))
y = Y.view(-1).double().numpy()
es.tell(xs, y)
xs_list.append(xs)
y_list.append(y)
if return_best_only:
cand = torch.tensor([es.best.x])
cand_val = torch.tensor([es.best.f])
else:
cand = torch.tensor(np.concatenate(xs_list, axis=0))
cand_val = torch.tensor(np.concatenate(y_list, axis=0))
return cand, cand_val
def train(self, cost, model, x_data, y_data=None, tolfun=1e-11, popsize=None, maxiter=None, use_grad=False):
"""Trains the ``model`` using the custom `cost` function.
Args:
cost (theta.costfunctions): the cost function.
model (theta.model.Model or theta.rtbm.RTBM): the model to be trained.
x_data (numpy.array): the support data with shape (Nv, Ndata).
y_data (numpy.array): the target prediction.
tolfun (float): the maximum tolerance of the cost function fluctuation to stop the minimization.
popsize (int): the population size.
maxiter (int): the maximum number of iterations.
use_grad (bool): if True the gradients for the cost and model are used in the minimization.
Returns:
numpy.array: the optimal parameters
Note:
The parameters of the model are changed by this algorithm.
"""
initsol = np.real(model.get_parameters())
args = {'bounds': model.get_bounds(),
'tolfun': tolfun,
'verb_log': 0}
sigma = np.max(model.get_bounds()[1])*0.1
if popsize is not None:
args['popsize'] = popsize
if maxiter is not None:
args['maxiter'] = maxiter
grad = None
if use_grad:
grad = worker_gradient
es = CMAEvolutionStrategy(initsol, sigma, args)
if self.num_cores > 1:
with closing(mp.Pool(self.num_cores, initializer=worker_initialize,
initargs=(cost, model, x_data, y_data))) as pool:
while not es.stop():
f_values, solutions = [], []
while len(solutions) < es.popsize:
x = es.ask(es.popsize-len(solutions), gradf=grad)
curr_fit = pool.map_async(worker_compute, x).get()
for value, solution in zip(curr_fit,x):
if not np.isnan(value):
solutions.append(solution)
f_values.append(value)
es.tell(solutions, f_values)
es.disp()
pool.terminate()
else:
worker_initialize(cost, model, x_data, y_data)
while not es.stop():
f_values, solutions = [], []
while len(solutions) < es.popsize:
curr_fit = x = np.NaN
while np.isnan(curr_fit):
x = es.ask(1, gradf=grad)[0]
curr_fit = worker_compute(x)
solutions.append(x)
f_values.append(curr_fit)
es.tell(solutions, f_values)
es.disp()
print(es.result)
model.set_parameters(es.result[0])
return es.result[0]
def run():
train = 0
names = [
# 'pred_a', 'pred_b', # 0.0
# 'odds_a', 'odds_b', # 2.1
# 'bet_wnl_a', 'bet_wnl_b', # 2.1
# 'bet_ts_a', 'bet_ts_b', # 1.5
# 'bet_tmi_a', 'bet_tmi_b', # 1.1
# 'bet_tma_a', 'bet_tma_b', # 0.9
# 'bet_drs_a', 'bet_drs_b', # 0.5
'bet_sfc_a',
'bet_sfc_b', # -0.1
# 'bet_spd_a', 'bet_spd_b', # 0.8
# 'bet_set_a', 'bet_set_b', # 1.0
# 'bet_gms_a', 'bet_gms_b', # -0.2
# 'bet_tie_a', 'bet_tie_b', # 4.1
# 'bet_ups_a', 'bet_ups_b', # -0.2
# 'bet_age_a', 'bet_age_b', # -2.5
]
params = [0, 0]
bounds = [[-np.inf], [np.inf]]
assert len(params) == len(names)
assert len(params) == len(bounds)
if train:
sigma = 1
opts = CMAOptions()
# opts['tolx'] = 1E-2
opts['bounds'] = bounds
es = CMAEvolutionStrategy(params, sigma, inopts=opts)
while not es.stop():
solutions = es.ask()
try:
fitness = [main(x, train) for x in solutions]
except ValueError as exc:
print(str(exc))
continue
es.tell(solutions, fitness)
es.disp()
print(list(es.result[0]))
print(list(es.result[5]))
es.result_pretty()
print(
f'finished after {es.result[3]} evaluations and {es.result[4]} iterations'
)
print('')
print('best')
print(list(es.result[0]))
print('')
print(
'xfavorite: distribution mean in "phenotype" space, to be considered as current best estimate of the optimum'
)
print(list(es.result[5]))
# res = minimize(main, params, (train,), bounds=bounds)
# print('')
# print(f'{res.nit} iterations')
# print(f'Success: {res.success} {res.message}')
# print(f'Solution: {res.x}')
# return
else:
main(params)
def run_optimization(self):
"""
Runs optimization job
:return:
"""
simulation_name = self.parse_args.input
population_size = self.parse_args.population_size
self.optim_param_mgr = OptimizationParameterManager()
optim_param_mgr = self.optim_param_mgr
# optim_param_mgr.parse(args.params_file)
optim_param_mgr.parse(self.parse_args.params_file)
starting_params = optim_param_mgr.get_starting_points()
print 'starting_params (mapped to [0,1])=', starting_params
print 'remapped (true) starting params=', optim_param_mgr.params_from_0_1(
starting_params)
print 'dictionary of remapped parameters labeled by parameter name=', optim_param_mgr.param_from_0_1_dict(
starting_params)
print 'simulation_name=', simulation_name
self.workload_dict = self.prepare_optimization_run(
simulation_name=simulation_name)
workload_dict = self.workload_dict
print workload_dict
std_dev = optim_param_mgr.std_dev
default_bounds = optim_param_mgr.default_bounds
optim = CMAEvolutionStrategy(starting_params, std_dev,
{'bounds': list(default_bounds)})
while not optim.stop(): # iterate
# get candidate solutions
# param_set_list = optim.ask(number=self.num_workers)
# param_set_list = optim.ask(number=1)
param_set_list = optim.ask(number=population_size)
# set param_set_list for run_task to iterate over
self.set_param_set_list(param_set_list=param_set_list)
# #debug
# return_result_vec = [self.fcn(optim_param_mgr.params_from_0_1(X)) for X in param_set_list]
# evaluate targert function values at the candidate solutions
return_result_vec = np.array([], dtype=float)
for param_set in self.param_generator(self.num_workers):
print 'CURRENT PARAM SET=', param_set
# distribution param_set to workers - run tasks spawns appropriate number of workers
# given self.num_workers and the size of the param_set
partial_return_result_vec = self.run_task(
workload_dict, param_set)
return_result_vec = np.append(return_result_vec,
partial_return_result_vec)
print 'FINISHED PARAM_SET=', param_set
optim.tell(param_set_list,
return_result_vec) # do all the real "update" work
optim.disp(20) # display info every 20th iteration
optim.logger.add() # log another "data line"
optimal_parameters = optim.result()[0]
print('termination by', optim.stop())
print('best f-value =', optim.result()[1])
optimal_parameters_remapped = optim_param_mgr.params_from_0_1(
optim.result()[0])
print('best solution =', optimal_parameters_remapped)
# print('best solution =', optim_param_mgr.params_from_0_1(optim.result()[0]))
print optim_param_mgr.params_names
self.save_optimal_parameters(optimal_parameters)
self.save_optimal_simulation(optimal_parameters)
def run():
train = 0
names = [
'bet_multi_param',
'bet_tma_a',
'bet_tma_b',
'bet_lati_a',
'bet_lati_b',
'bet_tiew_a',
'bet_tiew_b',
# 'bet_upsr_a', 'bet_upsr_b',
# 'bet_sfcw_a', 'bet_sfcw_b',
# 'bet_wnll_a', 'bet_wnll_b',
# 'bet_tier_a', 'bet_tier_b',
# 'bet_upsl_a', 'bet_upsl_b',
# 'bet_ts_a', 'bet_ts_b',
# 'bet_wnlw_a', 'bet_wnlw_b',
# 'bet_setw_a', 'bet_setw_b',
# 'bet_setl_a', 'bet_setl_b',
# 'bet_gms_a', 'bet_gms_b',
# 'bet_drsl_a', 'bet_drsl_b',
# 'bet_tmi_a', 'bet_tmi_b',
# 'bet_wnlr_a', 'bet_wnlr_b',
# 'bet_upsw_a', 'bet_upsw_b',
# 'bet_drs_a', 'bet_drs_b',
# 'bet_setr_a', 'bet_setr_b',
# 'bet_drsw_a', 'bet_drsw_b',
# 'bet_tiel_a', 'bet_tiel_b',
# 'bet_age_a', 'bet_age_b',
# 'bet_spd_a', 'bet_spd_b',
# 'bet_sfcr_a', 'bet_sfcr_b',
]
tolx = 10000 # more higher then longer time
params = [-14, 0, 0, 0, 0, 0, 0]
bounds = [[-np.inf], [np.inf]]
assert len(params) == len(names)
# assert len(params) == len(bounds)
if train:
time_start = time()
mins = 60 * 4
sigma = 1
opts = CMAOptions()
opts['bounds'] = bounds
es = CMAEvolutionStrategy(params, sigma, inopts=opts)
while not es.stop():
solutions = es.ask()
try:
fitness = [main(x, train) for x in solutions]
except ValueError as exc:
print(str(exc))
continue
es.tell(solutions, fitness)
es.disp()
# print(list(es.result[0]))
print(f'tolx={es.opts["tolx"]:.3f} sol={list(es.result[5])}')
es.opts['tolx'] = es.result[3] / tolx
if time() - time_start > 60 * mins:
print(f'{mins}min limit reached')
break
es.result_pretty()
print(
f'finished after {es.result[3]} evaluations and {es.result[4]} iterations'
)
# print('')
# print('best')
# print(list(es.result[0]))
print('')
print(
'xfavorite: distribution mean in "phenotype" space, to be considered as current best estimate of the optimum'
)
print(list(es.result[5]))
# pmin = -20
# pmax = 20
# step = (pmax - pmin) / 10
# rranges = [
# slice(pmin, pmax, step),
# slice(pmin, pmax, step),
# ]
# res = optimize.brute(main, rranges, (train,), finish=None)
# print(res)
# return
# res = minimize(main, params, (train,), bounds=bounds)
# print('')
# print(f'{res.nit} iterations')
# print(f'Success: {res.success} {res.message}')
# print(f'Solution: {res.x}')
# return
else:
main(params)
class CMAOptimizationSteppable(SteppableBasePy):
def __init__(self, _simulator, _frequency=1):
SteppableBasePy.__init__(self, _simulator, _frequency)
self.optim = None
self.sim_length_mcs = self.simulator.getNumSteps() - 1
self.f_vec = []
self.X_vec = []
self.X_vec_check = []
self.num_fcn_evals = -1
def minimized_fcn(self, *args, **kwds):
"""
this function needs to be overloaded in the subclass - it implements simulation fitness metric
:return {float}: number describing the "fitness" of the simulation
"""
return 0.0
def initial_condition_fcn(self, *args, **kwds):
"""
This function prepares initial condition for the simulaiton. Typically it creates cell field and initializes
all cell and field properties
:param args: first argument is a vector of parameters that are being optimized. The rest are up to the user
:param kwds: key words arguments - those are are up to the user
:return: None
"""
pass
def init_optimization_strategy(self, *args, **kwds):
"""
init_optimization_strategy initializes optimizer object. Its argument depend on the specific initializer used
IN the case of the CMA optimizer the options are described here: https://pypi.python.org/pypi/cma
:param args: see https://pypi.python.org/pypi/cma
:param kwds: see https://pypi.python.org/pypi/cma
:return: None
"""
self.optim = CMAEvolutionStrategy(*args, **kwds)
def optimization_step(self, mcs):
"""
THis function implements houklsekeeping associated with running optimization algorithm in a steppable
:param mcs {int}: current mcs
:return: None
"""
if not mcs % self.sim_length_mcs:
if self.optim.stop():
self.stopSimulation()
print('termination by', self.optim.stop())
print('best f-value =', self.optim.result()[1])
print('best solution =', self.optim.result()[0])
if not len(self.X_vec):
self.X_vec = self.optim.ask()
if len(self.f_vec):
# print 'self.X_vec_check=', self.X_vec_check
# print 'self.f_vec=', self.f_vec
self.optim.tell(
self.X_vec_check,
self.f_vec) # do all the real "update" work
self.optim.disp(20) # display info every 20th iteration
self.optim.logger.add() # log another "data line"
self.f_vec = []
self.num_fcn_evals = len(self.X_vec)
self.X_vec_check = deepcopy(self.X_vec)
self.X_current = self.X_vec[0]
if len(self.X_vec_check) != self.num_fcn_evals:
fcn_target = self.minimized_fcn()
self.f_vec.append(fcn_target)
self.X_vec.pop(0)
self.num_fcn_evals -= 1
CompuCellSetup.reset_current_step(0)
self.simulator.setStep(0)
self.clean_cell_field(reset_inventory=True)
self.initial_condition_fcn(self.X_current)
#create virtual display
if opt.vdisplay:
from xvfbwrapper import Xvfb
xvfb = Xvfb()
xvfb.start()
if opt.multiproc:
ray.init()
#TODO:revisit sigma
es = CMAEvolutionStrategy(torch.zeros(n_features), sigma0=5.0)
i = 0
n_generation = 10000
while not es.stop() and i < n_generation:
start_time = time.time()
solutions = es.ask()
#calculate fitness
objectives = []
bcs = []
if opt.multiproc:
futures = [
multi_fit_func.remote(torch.from_numpy(solution).float(),
opt.device,
generators,
opt.num_layer,
rand_network,
reals,
def run_optimization(self):
"""
Runs optimization job
:return:
"""
simulation_name = self.parse_args.input
population_size = self.parse_args.population_size
self.optim_param_mgr = OptimizationParameterManager()
optim_param_mgr = self.optim_param_mgr
# optim_param_mgr.parse(args.params_file)
optim_param_mgr.parse(self.parse_args.params_file)
starting_params = optim_param_mgr.get_starting_points()
print 'starting_params (mapped to [0,1])=', starting_params
print 'remapped (true) starting params=', optim_param_mgr.params_from_0_1(starting_params)
print 'dictionary of remapped parameters labeled by parameter name=', optim_param_mgr.param_from_0_1_dict(
starting_params)
print 'simulation_name=', simulation_name
self.workload_dict = self.prepare_optimization_run(simulation_name=simulation_name)
workload_dict = self.workload_dict
print workload_dict
std_dev = optim_param_mgr.std_dev
default_bounds = optim_param_mgr.default_bounds
optim = CMAEvolutionStrategy(starting_params, std_dev, {'bounds': list(default_bounds)})
while not optim.stop(): # iterate
# get candidate solutions
# param_set_list = optim.ask(number=self.num_workers)
# param_set_list = optim.ask(number=1)
param_set_list = optim.ask(number=population_size)
# set param_set_list for run_task to iterate over
self.set_param_set_list(param_set_list=param_set_list)
# #debug
# return_result_vec = [self.fcn(optim_param_mgr.params_from_0_1(X)) for X in param_set_list]
# evaluate targert function values at the candidate solutions
return_result_vec = np.array([], dtype=float)
for param_set in self.param_generator(self.num_workers):
print 'CURRENT PARAM SET=', param_set
# distribution param_set to workers - run tasks spawns appropriate number of workers
# given self.num_workers and the size of the param_set
partial_return_result_vec = self.run_task(workload_dict, param_set)
return_result_vec = np.append(return_result_vec, partial_return_result_vec)
print 'FINISHED PARAM_SET=', param_set
optim.tell(param_set_list, return_result_vec) # do all the real "update" work
optim.disp(20) # display info every 20th iteration
optim.logger.add() # log another "data line"
optimal_parameters = optim.result()[0]
print('termination by', optim.stop())
print('best f-value =', optim.result()[1])
optimal_parameters_remapped = optim_param_mgr.params_from_0_1(optim.result()[0])
print('best solution =', optimal_parameters_remapped)
# print('best solution =', optim_param_mgr.params_from_0_1(optim.result()[0]))
print optim_param_mgr.params_names
self.save_optimal_parameters(optimal_parameters)
self.save_optimal_simulation(optimal_parameters)
class CMAOptimizationSteppable(SteppableBasePy):
def __init__(self, _simulator, _frequency=1):
SteppableBasePy.__init__(self, _simulator, _frequency)
self.optim = None
self.sim_length_mcs = self.simulator.getNumSteps() - 1
self.f_vec = []
self.X_vec = []
self.X_vec_check = []
self.num_fcn_evals = -1
def minimized_fcn(self, *args, **kwds):
"""
this function needs to be overloaded in the subclass - it implements simulation fitness metric
:return {float}: number describing the "fitness" of the simulation
"""
return 0.0
def initial_condition_fcn(self, *args, **kwds):
"""
This function prepares initial condition for the simulaiton. Typically it creates cell field and initializes
all cell and field properties
:param args: first argument is a vector of parameters that are being optimized. The rest are up to the user
:param kwds: key words arguments - those are are up to the user
:return: None
"""
pass
def init_optimization_strategy(self, *args, **kwds):
"""
init_optimization_strategy initializes optimizer object. Its argument depend on the specific initializer used
IN the case of the CMA optimizer the options are described here: https://pypi.python.org/pypi/cma
:param args: see https://pypi.python.org/pypi/cma
:param kwds: see https://pypi.python.org/pypi/cma
:return: None
"""
self.optim = CMAEvolutionStrategy(*args, **kwds)
def optimization_step(self, mcs):
"""
THis function implements houklsekeeping associated with running optimization algorithm in a steppable
:param mcs {int}: current mcs
:return: None
"""
if not mcs % self.sim_length_mcs:
if self.optim.stop():
self.stopSimulation()
print('termination by', self.optim.stop())
print('best f-value =', self.optim.result()[1])
print('best solution =', self.optim.result()[0])
if not len(self.X_vec):
self.X_vec = self.optim.ask()
if len(self.f_vec):
# print 'self.X_vec_check=', self.X_vec_check
# print 'self.f_vec=', self.f_vec
self.optim.tell(self.X_vec_check, self.f_vec) # do all the real "update" work
self.optim.disp(20) # display info every 20th iteration
self.optim.logger.add() # log another "data line"
self.f_vec = []
self.num_fcn_evals = len(self.X_vec)
self.X_vec_check = deepcopy(self.X_vec)
self.X_current = self.X_vec[0]
if len(self.X_vec_check) != self.num_fcn_evals:
fcn_target = self.minimized_fcn()
self.f_vec.append(fcn_target)
self.X_vec.pop(0)
self.num_fcn_evals -= 1
CompuCellSetup.reset_current_step(0)
self.simulator.setStep(0)
self.clean_cell_field(reset_inventory=True)
self.initial_condition_fcn(self.X_current)
class ALGO:
def __init__(self, max_evaluations, n_points, dimension, function_id,
**kwargs):
self.function_id = function_id
self.n_points = n_points
self.dimension = dimension
self.function = CEC2005(dimension)[function_id].objective_function
self.max_evaluations = max_evaluations * dimension
self.max_bounds = Boundary(dimension, function_id).max_bounds
self.min_bounds = Boundary(dimension, function_id).min_bounds
self.optimal_position = CEC2005(
dimension)[function_id].get_optimal_solutions()[0].phenome
self.optimal_fitness = self.function(self.optimal_position)
#self.problem = Problem(self.function.objective_function, max_evaluations=max_evaluations)
self.boundary = Boundary(dimension, function_id)
self.verbose = kwargs.get('verbose', False)
self.population = [
] #self.init_population( self.n_points, self.dimension )
self.algo_type = kwargs.get('algo_type', 'CMA')
self.init_algo()
self.iteration = 0
self.should_terminate = False
self.optimal_solution = self.find_optimal_solution()
self.stats = OrderedDict([
('iteration', []),
('FEs', []),
('error', []),
('best_value', []),
#('best_position',[])
])
self.run()
self.best_solution = min(self.population,
key=attrgetter('objective_values'))
def init_population(self, n_points, dim):
positions = np.zeros((n_points, dim))
for d in range(dim):
positions[:, d] = np.random.uniform(self.boundary.min_bounds[d],
self.boundary.max_bounds[d],
self.n_points)
population = [Individual(position) for position in positions]
self.problem.batch_evaluate(population)
population = sorted(population, key=attrgetter('objective_values'))
population = population[:len(population) / 2]
ranks = range(1, len(population) + 1)
return [Cluster(population, ranks)]
def init_algo(self):
init_min_bound = self.boundary.init_min_bounds[0]
init_max_bound = self.boundary.init_max_bounds[0]
min_bound = self.boundary.min_bounds[0]
max_bound = self.boundary.max_bounds[0]
if self.algo_type == 'CMA':
init_point = [(init_max_bound + init_min_bound) / 2] * dimension
sigma = (init_max_bound - init_min_bound) * 0.2
#print 'init_point:', init_point
#print 'sigma:', sigma
self.algo = CMAEvolutionStrategy(init_point, sigma, {
'popsize': self.n_points,
'bounds': [min_bound, max_bound]
})
#elif self.algo_type == 'PSO':
def find_optimal_solution(self):
dimension = self.dimension
function_id = self.function_id
optimal_solutions = CEC2005(
dimension)[function_id].get_optimal_solutions()
test_prob = Problem(CEC2005(dimension)[function_id].objective_function)
test_prob.batch_evaluate(optimal_solutions)
return min(optimal_solutions, key=attrgetter('objective_values'))
def run(self):
self.iteration = self.iteration + 1
if self.algo_type == 'CMA':
positions = self.algo.ask()
solutions = [Individual(position) for position in positions]
try:
self.problem.batch_evaluate(solutions)
except ResourcesExhausted:
self.should_terminate = True
return
self.algo.tell([p.phenome for p in solutions],
[p.objective_values for p in solutions])
self.population = sorted(solutions,
key=attrgetter('objective_values'))
self.best_solution = min(self.population,
key=attrgetter('objective_values'))
self.update_statistics()
def found_optimum(self, delta=1e-8):
if self.best_solution.objective_values - self.optimal_solution.objective_values < delta:
return True
return False
def stop(self):
if self.algo.stop():
if self.verbose: print('Algorithm stops!')
self.should_terminate = True
elif self.problem.remaining_evaluations < 1:
if self.verbose: print('Consumed all evaluations!')
self.should_terminate = True
elif self.found_optimum(delta=goal_error):
if self.verbose: print('Found Optimum!')
self.should_terminate = True
return self.should_terminate
def print_status(self):
error = self.best_solution.objective_values - self.optimal_solution.objective_values
print('')
print(' Iteration %d: error = %e' % (self.iteration, error))
print(' Evaluations: consumed %d, remain %d' %
(self.problem.consumed_evaluations,
self.problem.remaining_evaluations))
print(' best fitness: %f at %r' %
(self.best_solution.objective_values,
self.best_solution.phenome.tolist()))
print('optimal solution: %f at %r' %
(self.optimal_solution.objective_values,
self.optimal_solution.phenome))
print('')
def update_statistics(self):
self.stats['iteration'].append(self.iteration)
self.stats['FEs'].append(self.problem.consumed_evaluations)
self.stats['error'].append(self.best_solution.objective_values -
self.optimal_solution.objective_values)
self.stats['best_value'].append(self.best_solution.objective_values)