def test_dump_and_load():
res = gp_minimize(bench3, [(-2.0, 2.0)],
x0=[0.],
acq_func="LCB",
n_calls=2,
n_random_starts=0,
random_state=1)
# Test normal dumping and loading
with tempfile.TemporaryFile() as f:
dump(res, f)
f.seek(0)
res_loaded = load(f)
check_optimization_results_equality(res, res_loaded)
assert_true("func" in res_loaded.specs["args"])
# Test dumping without objective function
with tempfile.TemporaryFile() as f:
dump(res, f, store_objective=False)
f.seek(0)
res_loaded = load(f)
check_optimization_results_equality(res, res_loaded)
assert_true(not ("func" in res_loaded.specs["args"]))
# Delete the objective function and dump the modified object
del res.specs["args"]["func"]
with tempfile.TemporaryFile() as f:
dump(res, f, store_objective=False)
f.seek(0)
res_loaded = load(f)
check_optimization_results_equality(res, res_loaded)
assert_true(not ("func" in res_loaded.specs["args"]))
def test_deadline_stopper():
deadline = DeadlineStopper(0.0001)
gp_minimize(bench3, [(-1.0, 1.0)],
callback=deadline,
n_calls=10,
random_state=1)
assert len(deadline.iter_time) == 1
assert np.sum(deadline.iter_time) > deadline.total_time
deadline = DeadlineStopper(60)
gp_minimize(bench3, [(-1.0, 1.0)],
callback=deadline,
n_calls=10,
random_state=1)
assert len(deadline.iter_time) == 10
assert np.sum(deadline.iter_time) < deadline.total_time
def test_n_jobs():
r_single = gp_minimize(bench3, [(-2.0, 2.0)],
acq_optimizer="lbfgs",
acq_func="EI",
n_calls=2,
n_random_starts=1,
random_state=1,
noise=1e-10)
r_double = gp_minimize(bench3, [(-2.0, 2.0)],
acq_optimizer="lbfgs",
acq_func="EI",
n_calls=2,
n_random_starts=1,
random_state=1,
noise=1e-10,
n_jobs=2)
assert_array_equal(r_single.x_iters, r_double.x_iters)
def test_expected_minimum():
res = gp_minimize(bench3, [(-2.0, 2.0)],
x0=[0.],
noise=1e-8,
n_calls=8,
n_random_starts=3,
random_state=1)
x_min, f_min = expected_minimum(res, random_state=1)
x_min2, f_min2 = expected_minimum(res, random_state=1)
assert f_min <= res.fun # true since noise ~= 0.0
assert x_min == x_min2
assert f_min == f_min2
def test_use_given_estimator():
""" Test that gp_minimize does not use default estimator if one is passed
in explicitly. """
domain = [(1.0, 2.0), (3.0, 4.0)]
noise_correct = 1e+5
noise_fake = 1e-10
estimator = cook_estimator("GP", domain, noise=noise_correct)
res = gp_minimize(branin,
domain,
n_calls=1,
n_random_starts=1,
base_estimator=estimator,
noise=noise_fake)
assert res['models'][-1].noise == noise_correct
def check_minimize(func,
y_opt,
bounds,
acq_optimizer,
acq_func,
margin,
n_calls,
n_random_starts=10):
r = gp_minimize(func,
bounds,
acq_optimizer=acq_optimizer,
acq_func=acq_func,
n_random_starts=n_random_starts,
n_calls=n_calls,
random_state=1,
noise=1e-10)
assert_less(r.fun, y_opt + margin)
def test_defaults_are_equivalent():
# check that the defaults of Optimizer reproduce the defaults of
# gp_minimize
space = [(-5., 10.), (0., 15.)]
#opt = Optimizer(space, 'ET', acq_func="EI", random_state=1)
opt = Optimizer(space, random_state=1)
for n in range(12):
x = opt.ask()
res_opt = opt.tell(x, branin(x))
#res_min = forest_minimize(branin, space, n_calls=12, random_state=1)
res_min = gp_minimize(branin, space, n_calls=12, random_state=1)
assert res_min.space == res_opt.space
# tolerate small differences in the points sampled
assert np.allclose(res_min.x_iters, res_opt.x_iters) # , atol=1e-5)
assert np.allclose(res_min.x, res_opt.x) # , atol=1e-5)
def test_gpr_default():
"""Smoke test that gp_minimize does not fail for default values."""
gp_minimize(branin, ((-5.0, 10.0), (0.0, 15.0)),
n_random_starts=1,
n_calls=2)
from ProcessOptimizer.plots import plot_objective
from ProcessOptimizer import bokeh_plot
# For reproducibility
import numpy as np
np.random.seed(123)
import matplotlib.pyplot as plt
plt.set_cmap("viridis")
SPACE = [
Integer(1, 20, name='max_depth'),
Integer(2, 100, name='min_samples_split'),
Integer(5, 30, name='min_samples_leaf'),
Integer(1, 30, name='max_features'),
Categorical(list('abc'), name='dummy'),
Categorical(['gini', 'entropy'], name='criterion'),
Categorical(list('def'), name='dummy'),
]
def objective(params):
clf = DecisionTreeClassifier(**{
dim.name: val
for dim, val in zip(SPACE, params) if dim.name != 'dummy'
})
return -np.mean(cross_val_score(clf, *load_breast_cancer(True)))
result = gp_minimize(objective, SPACE, n_calls=20)
bokeh_plot.start(result)
from ProcessOptimizer import bokeh_plot
# For reproducibility
import numpy as np
np.random.seed(123)
plt.set_cmap("viridis")
# Here we define a function that we evaluate.
def funny_func(x):
s = 0
for i in range(len(x)):
s += (x[i])**2
return s
# We run forest_minimize on the function
bounds = [
(-1, 1.),
] * 7
n_calls = 30
result = gp_minimize(funny_func,
bounds,
n_calls=n_calls,
n_random_starts=20,
acq_optimizer="auto",
acq_func="gp_hedge",
random_state=4)
bokeh_plot.start(result)