def test_generate():
sobol = Sobol(min_skip=1, max_skip=1)
x = sobol.generate([
(0., 1.),
] * 3, 3)
x = np.array(x)
assert_array_equal(x[0, :], [0.5, 0.5, 0.5])
assert_array_equal(x[1, :], [0.75, 0.25, 0.75])
assert_array_equal(x[2, :], [0.25, 0.75, 0.25])
sobol.set_params(max_skip=2)
assert sobol.max_skip == 2
assert isinstance(sobol, InitialPointGenerator)
def test_generate():
sobol = Sobol(randomize=False)
x = sobol.generate([
(0., 1.),
] * 3, 4)
x = np.array(x)
assert_array_equal(x[0, :], [0., 0., 0.])
assert_array_equal(x[1, :], [0.5, 0.5, 0.5])
assert_array_equal(x[2, :], [0.75, 0.25, 0.75])
assert_array_equal(x[3, :], [0.25, 0.75, 0.25])
sobol.set_params(skip=2)
assert sobol.skip == 2
assert isinstance(sobol, InitialPointGenerator)
def cook_initial_point_generator(generator, **kwargs):
"""Cook a default initial point generator.
For the special generator called "random" the return value is None.
Parameters
----------
generator : "lhs", "sobol", "halton", "hammersly", "grid", "random" \
or InitialPointGenerator instance"
Should inherit from `skopt.sampler.InitialPointGenerator`.
kwargs : dict
Extra parameters provided to the generator at init time.
"""
if generator is None:
generator = "random"
elif isinstance(generator, str):
generator = generator.lower()
if generator not in [
"sobol", "halton", "hammersly", "lhs", "random", "grid",
"maxpro", "maxpro-gd"
]:
raise ValueError("Valid strings for the generator parameter "
" are: 'sobol', 'lhs', 'halton', 'hammersly',"
"'random', 'maxpro','maxpro-gd', or 'grid' not "
"%s." % generator)
elif not isinstance(generator, InitialPointGenerator):
raise ValueError("generator has to be an InitialPointGenerator."
"Got %s" % (str(type(generator))))
if isinstance(generator, str):
if generator == "sobol":
generator = Sobol()
elif generator == "halton":
generator = Halton()
elif generator == "hammersly":
generator = Hammersly()
elif generator == "lhs":
generator = Lhs()
elif generator == "grid":
generator = Grid()
elif generator == "random":
return None
elif generator == "maxpro":
generator = MaxPro(use_gradient=False)
elif generator == "maxpro-gd":
generator = MaxPro(use_gradient=True)
generator.set_params(**kwargs)
return generator
def test_sobol():
sobol = Sobol()
x, seed = sobol._sobol(3, 0)
assert_array_equal(x, [0., 0., 0.])
x, seed = sobol._sobol(3, 1)
assert_array_equal(x, [0.5, 0.5, 0.5])
x, seed = sobol._sobol(3, 2)
assert_array_equal(x, [0.75, 0.25, 0.75])
x, seed = sobol._sobol(3, 3)
assert_array_equal(x, [0.25, 0.75, 0.25])
x, seed = sobol._sobol(3, 4)
assert_array_equal(x, [0.375, 0.375, 0.625])
x, seed = sobol._sobol(3, 5)
assert_array_equal(x, [0.875, 0.875, 0.125])
x, seed = sobol._sobol(3, 6)
assert_array_equal(x, [0.625, 0.125, 0.375])
def _generate(self):
skip = self.rng.randint(int(1e6))
try:
from torch.quasirandom import SobolEngine
sobol = SobolEngine(dimension=len(self.search_dims),
scramble=True,
seed=skip)
X = sobol.draw(n=self.size).numpy()
except ImportError:
sobol = Sobol(min_skip=skip, max_skip=skip)
X = sobol.generate(self.search_dims, self.size)
return X
#############################################################################
# Random sampling
# ---------------
x = space.rvs(n_samples)
plot_searchspace(x, "Random samples")
pdist_data = []
x_label = []
pdist_data.append(pdist(x).flatten())
x_label.append("random")
#############################################################################
# Sobol'
# ------
sobol = Sobol()
x = sobol.generate(space.dimensions, n_samples)
plot_searchspace(x, "Sobol'")
pdist_data.append(pdist(x).flatten())
x_label.append("sobol'")
#############################################################################
# Classic Latin hypercube sampling
# --------------------------------
lhs = Lhs(lhs_type="classic", criterion=None)
x = lhs.generate(space.dimensions, n_samples)
plot_searchspace(x, 'classic LHS')
pdist_data.append(pdist(x).flatten())
x_label.append("lhs")