def get_cs_bc():
cs_bc = ConfigurationSpace()
x0 = UniformFloatHyperparameter("x0", scale1[0], scale1[1])
# x0 = UniformIntegerHyperparameter("x0", scale1[0], scale1[1]) # test int
x1 = UniformFloatHyperparameter("x1", scale2[0], scale2[1])
cs_bc.add_hyperparameters([x0, x1])
return cs_bc
def __init__(self, dim=2, bounds=None,
noise_std=0, random_state=None):
self.dim = dim
params = {f'x{i}': (0, 10, 5) for i in range(1, 1+self.dim)}
config_space = ConfigurationSpace()
config_space.add_hyperparameters([UniformFloatHyperparameter(e, *params[e]) for e in params])
super().__init__(config_space, noise_std, optimal_value=0, random_state=random_state)
def __init__(self, noise_std=0, random_state=None):
lb, ub = -4.5, 4.5
dim = 2
params = {f'x{i}': (lb, ub, (lb + ub)/2)
for i in range(1, dim+1)}
config_space = ConfigurationSpace()
config_space.add_hyperparameters([UniformFloatHyperparameter(e, *params[e]) for e in params])
super().__init__(config_space, noise_std, optimal_value=0, random_state=random_state)
def __init__(self, noise_std=0, random_state=None):
params = {'x1': (-10, 0, -5), 'x2': (-6.5, 0, -3.25)}
config_space = ConfigurationSpace()
config_space.add_hyperparameters([UniformFloatHyperparameter(e, *params[e]) for e in params])
super().__init__(config_space, noise_std,
optimal_value=-106.7645367,
optimal_point=[(-3.1302468, -1.5821422)],
random_state=random_state)
def __init__(self, noise_std=0, random_state=None):
params = {f'x{i}': (-1.25, 1.25, 1) for i in [1, 2]}
config_space = ConfigurationSpace()
config_space.add_hyperparameters([UniformFloatHyperparameter(e, *params[e]) for e in params])
super().__init__(config_space, noise_std,
optimal_value=-0.072,
optimal_point=[(0.84852813, -0.84852813), (-0.84852813, 0.84852813)],
random_state=random_state)
def __init__(self, noise_std=0, random_state=None):
self.ref_point = [1864.72022, 11.81993945, 0.2903999384]
params = {f'x{i}': (1.0, 3.0) for i in range(1, 6)}
config_space = ConfigurationSpace()
config_space.add_hyperparameters([UniformFloatHyperparameter(e, *params[e]) for e in params])
super().__init__(config_space, noise_std,
num_objs=3,
random_state=random_state)
def __init__(self, dim: int, num_constraints=0, noise_std=0, random_state=None):
self.dim = dim
self.ref_point = [11.0, 11.0]
params = {f'x{i}': (0, 1) for i in range(1, dim+1)}
config_space = ConfigurationSpace()
config_space.add_hyperparameters([UniformFloatHyperparameter(e, *params[e]) for e in params])
super().__init__(config_space, noise_std,
num_objs=2, num_constraints=num_constraints,
random_state=random_state)
def __init__(self, noise_std=0, random_state=None):
params = {'x1': (-5, 10, 2.5),
'x2': (0, 15, 7.5)}
config_space = ConfigurationSpace()
config_space.add_hyperparameters([UniformFloatHyperparameter(e, *params[e]) for e in params])
super().__init__(config_space, noise_std,
optimal_value=0.397887,
optimal_point=[(-np.pi, 12.275), (np.pi, 2.275), (9.42478, 2.475)],
random_state=random_state)
def __init__(self, noise_std=0, random_state=None):
params = {'x1': (-15.0, -5.0, -10.0),
'x2': (-3.0, 3.0, 0)}
config_space = ConfigurationSpace()
config_space.add_hyperparameters([UniformFloatHyperparameter(e, *params[e]) for e in params])
super().__init__(config_space, noise_std,
optimal_value=0,
optimal_point=[(-10.0, 1.0)],
random_state=random_state)
def __init__(self, noise_std=0, random_state=None):
self.ref_point = [10.0, 10.0]
params = {'x1': (0.1, 10.0),
'x2': (0.0, 5.0)}
config_space = ConfigurationSpace()
config_space.add_hyperparameters([UniformFloatHyperparameter(e, *params[e]) for e in params])
super().__init__(config_space, noise_std,
num_objs=2, num_constraints=2,
random_state=random_state)
def __init__(self, dim=2, constrained=False, noise_std=0, random_state=None):
self.dim = dim
self.constrained = constrained
params = {f'x{i}': (-5.0, 10.0, 2.5) for i in range(1, 1+self.dim)}
config_space = ConfigurationSpace()
config_space.add_hyperparameters([UniformFloatHyperparameter(e, *params[e]) for e in params])
super().__init__(config_space, noise_std,
optimal_value=0,
optimal_point=[tuple(1.0 for _ in range(self.dim))],
random_state=random_state)
def __init__(self, constrained=False, noise_std=0, random_state=None):
self.ref_point = [18.0, 6.0]
self.constrained = constrained
num_constraints = 1 if self.constrained else 0
params = {'x1': (0, 1, 0.5),
'x2': (0, 1, 0.5)}
config_space = ConfigurationSpace()
config_space.add_hyperparameters([UniformFloatHyperparameter(e, *params[e]) for e in params])
super().__init__(config_space, noise_std,
num_objs=2,
num_constraints=num_constraints,
random_state=random_state)
def __init__(self, dim, num_objs=2, num_constraints=0, noise_std=0, random_state=None):
if dim <= num_objs:
raise ValueError(
"dim must be > num_objs, but got %s and %s" % (dim, num_objs)
)
self.dim = dim
self.k = self.dim - num_objs + 1
self.bounds = [(0.0, 1.0) for _ in range(self.dim)]
self.ref_point = [self._ref_val for _ in range(num_objs)]
params = {f'x{i}': (0, 1, i/dim) for i in range(1, dim+1)}
config_space = ConfigurationSpace()
config_space.add_hyperparameters([UniformFloatHyperparameter(e, *params[e]) for e in params])
super().__init__(config_space, noise_std, num_objs, num_constraints, random_state=random_state)
def __init__(self, dim=2, bounds=None, constrained=False,
noise_std=0, random_state=None):
self.constrained = constrained
if bounds is None:
if constrained:
lb, ub = -5, 10
else:
lb, ub = -10, 15
else:
lb, ub = bounds
params = {f'x{i}': (lb, ub, (lb + ub)/2)
for i in range(1, dim+1)}
config_space = ConfigurationSpace()
config_space.add_hyperparameters([UniformFloatHyperparameter(e, *params[e]) for e in params])
super().__init__(config_space, noise_std, optimal_value=0, random_state=random_state)
def get_cs_lightgbm(): # todo q and int for compare?
cs = ConfigurationSpace()
n_estimators = UniformFloatHyperparameter("n_estimators",
100,
1000,
default_value=500,
q=50)
num_leaves = UniformIntegerHyperparameter("num_leaves",
31,
2047,
default_value=128)
# max_depth = Constant('max_depth', 15)
learning_rate = UniformFloatHyperparameter("learning_rate",
1e-3,
0.3,
default_value=0.1,
log=True)
min_child_samples = UniformIntegerHyperparameter("min_child_samples",
5,
30,
default_value=20)
subsample = UniformFloatHyperparameter("subsample",
0.7,
1,
default_value=1,
q=0.1)
colsample_bytree = UniformFloatHyperparameter("colsample_bytree",
0.7,
1,
default_value=1,
q=0.1)
# cs.add_hyperparameters([n_estimators, num_leaves, max_depth, learning_rate, min_child_samples, subsample,
# colsample_bytree])
cs.add_hyperparameters([
n_estimators, num_leaves, learning_rate, min_child_samples,
subsample, colsample_bytree
])
return cs
import numpy as np
from litebo.optimizer.generic_smbo import SMBO
from litebo.utils.config_space import ConfigurationSpace, UniformFloatHyperparameter
def townsend(config):
X = np.array(list(config.get_dictionary().values()))
res = dict()
res['objs'] = (-(np.cos(
(X[0] - 0.1) * X[1])**2 + X[0] * np.sin(3 * X[0] + X[1])), )
res['constraints'] = (-(-np.cos(1.5 * X[0] + np.pi) * np.cos(1.5 * X[1]) +
np.sin(1.5 * X[0] + np.pi) * np.sin(1.5 * X[1])), )
return res
townsend_params = {'float': {'x1': (-2.25, 2.5, 0), 'x2': (-2.5, 1.75, 0)}}
townsend_cs = ConfigurationSpace()
townsend_cs.add_hyperparameters([
UniformFloatHyperparameter(e, *townsend_params['float'][e])
for e in townsend_params['float']
])
bo = SMBO(townsend,
townsend_cs,
num_constraints=1,
acq_optimizer_type='random_scipy',
max_runs=60,
task_id='smbo_eic')
bo.run()
res = dict()
f1 = (px2 - 5.1/(4*np.pi**2) * px1**2 + 5/np.pi * px1 - 6)**2 + 10 * (1 - 1/(8*np.pi)) * np.cos(px1) + 10
f2 = (1 - np.exp(-1/(2*x2))) * (2300 * x1**3 + 1900 * x1**2 + 2092 * x1 + 60) / (100 * x1**3 + 500 * x1**2 + 4*x1 + 20)
res['objs'] = [f1, f2]
res['constraints'] = []
return res
bc_params = {
'float': {
'x1': (0, 1, 0.5),
'x2': (0, 1, 0.5)
}
}
bc_cs = ConfigurationSpace()
bc_cs.add_hyperparameters([UniformFloatHyperparameter(e, *bc_params['float'][e]) for e in bc_params['float']])
bc_max_hv = 59.36011874867746
bc_ref_point = [18., 6.]
bo = SMBO(branin_currin, bc_cs,
task_id='parego',
num_objs=2,
acq_type='parego',
ref_point=bc_ref_point,
max_runs=100)
bo.run()
hvs = bo.get_history().hv_data
log_hv_diff = np.log10(bc_max_hv - np.asarray(hvs))
x1 = xs['x1']
x2 = xs['x2']
a = 1.
b = 5.1 / (4. * np.pi**2)
c = 5. / np.pi
r = 6.
s = 10.
t = 1. / (8. * np.pi)
ret = a * (x2 - b * x1**2 + c * x1 - r)**2 + s * (1 - t) * np.cos(x1) + s
return {'objs': (ret, )}
cs = ConfigurationSpace()
x1 = UniformFloatHyperparameter("x1", -5, 10, default_value=0)
x2 = UniformFloatHyperparameter("x2", 0, 15, default_value=0)
cs.add_hyperparameters([x1, x2])
seed = np.random.randint(100)
# random search
# bo = SMBO(branin, cs, sample_strategy='random', max_runs=100, task_id='mcei', random_state=seed)
bo = SMBO(branin,
cs,
advisor_type='mcadvisor',
acq_type='mcei',
mc_times=10,
max_runs=50,
task_id='mcei',
random_state=seed)
bo.run()
def get_configspace():
cs = ConfigurationSpace()
x1 = UniformFloatHyperparameter("x1", -5, 10, default_value=0)
x2 = UniformFloatHyperparameter("x2", 0, 15, default_value=0)
cs.add_hyperparameters([x1, x2])
return cs
logging.basicConfig(level=logging.INFO)
# Build Configuration Space which defines all parameters and their ranges
cs = ConfigurationSpace()
# We define a few possible types of SVM-kernels and add them as "kernel" to our cs
kernel = CategoricalHyperparameter("kernel",
["linear", "rbf", "poly", "sigmoid"],
default_value="poly")
cs.add_hyperparameter(kernel)
# There are some hyperparameters shared by all kernels
C = UniformFloatHyperparameter("C", 0.001, 1000.0, default_value=1.0)
shrinking = CategoricalHyperparameter("shrinking", ["true", "false"],
default_value="true")
cs.add_hyperparameters([C, shrinking])
# Others are kernel-specific, so we can add conditions to limit the searchspace
degree = UniformIntegerHyperparameter(
"degree", 1, 5, default_value=3) # Only used by kernel poly
coef0 = UniformFloatHyperparameter("coef0", 0.0, 10.0,
default_value=0.0) # poly, sigmoid
cs.add_hyperparameters([degree, coef0])
use_degree = InCondition(child=degree, parent=kernel, values=["poly"])
use_coef0 = InCondition(child=coef0, parent=kernel, values=["poly", "sigmoid"])
cs.add_conditions([use_degree, use_coef0])
# This also works for parameters that are a mix of categorical and values from a range of numbers
# For example, gamma can be either "auto" or a fixed float
gamma = CategoricalHyperparameter(
"gamma", ["auto", "value"],