def test_optimizer_copy(acq_func):
# Checks that the base estimator, the objective and target values
# are copied correctly.
base_estimator = ExtraTreesRegressor(random_state=2)
opt = Optimizer([(-2.0, 2.0)], base_estimator, acq_func=acq_func,
n_initial_points=1, acq_optimizer="sampling")
# run three iterations so that we have some points and objective values
if "ps" in acq_func:
opt.run(bench1_with_time, n_iter=3)
else:
opt.run(bench1, n_iter=3)
opt_copy = opt.copy()
copied_estimator = opt_copy.base_estimator_
if "ps" in acq_func:
assert_true(isinstance(copied_estimator, MultiOutputRegressor))
# check that the base_estimator is not wrapped multiple times
is_multi = isinstance(copied_estimator.estimator,
MultiOutputRegressor)
assert_false(is_multi)
else:
assert_false(isinstance(copied_estimator, MultiOutputRegressor))
assert_array_equal(opt_copy.Xi, opt.Xi)
assert_array_equal(opt_copy.yi, opt.yi)
def test_optimizer_base_estimator_string_smoke_njobs():
opt = Optimizer([(-2.0, 2.0)],
base_estimator="GBRT",
n_initial_points=1,
acq_func="EI",
n_jobs=-1)
opt.run(func=lambda x: x[0]**2, n_iter=3)
def test_optimizer_copy(acq_func):
# Checks that the base estimator, the objective and target values
# are copied correctly.
base_estimator = ExtraTreesRegressor(random_state=2)
opt = Optimizer([(-2.0, 2.0)],
base_estimator,
acq_func=acq_func,
n_initial_points=1,
acq_optimizer="sampling")
# run three iterations so that we have some points and objective values
if "ps" in acq_func:
opt.run(bench1_with_time, n_iter=3)
else:
opt.run(bench1, n_iter=3)
opt_copy = opt.copy()
copied_estimator = opt_copy.base_estimator_
if "ps" in acq_func:
assert isinstance(copied_estimator, MultiOutputRegressor)
# check that the base_estimator is not wrapped multiple times
is_multi = isinstance(copied_estimator.estimator, MultiOutputRegressor)
assert not is_multi
else:
assert not isinstance(copied_estimator, MultiOutputRegressor)
assert_array_equal(opt_copy.Xi, opt.Xi)
assert_array_equal(opt_copy.yi, opt.yi)
def test_dump_and_load_optimizer():
base_estimator = ExtraTreesRegressor(random_state=2)
opt = Optimizer([(-2.0, 2.0)], base_estimator, n_random_starts=1,
acq_optimizer="sampling")
opt.run(bench1, n_iter=3)
with tempfile.TemporaryFile() as f:
dump(opt, f)
load(f)
def test_model_queue_size():
# Check if model_queue_size limits the model queue size
base_estimator = ExtraTreesRegressor(random_state=2)
opt = Optimizer([(-2.0, 2.0)], base_estimator, n_initial_points=1,
acq_optimizer="sampling", model_queue_size=2)
opt.run(bench1, n_iter=3)
# tell() computes the next point ready for the next call to ask()
# hence there are three after three iterations
assert_equal(len(opt.models), 2)
assert_equal(len(opt.Xi), 3)
opt.ask()
assert_equal(len(opt.models), 2)
assert_equal(len(opt.Xi), 3)
assert_equal(opt.ask(), opt.ask())
def test_multiple_asks():
# calling ask() multiple times without a tell() inbetween should
# be a "no op"
base_estimator = ExtraTreesRegressor(random_state=2)
opt = Optimizer([(-2.0, 2.0)], base_estimator, n_random_starts=1,
acq_optimizer="sampling")
opt.run(bench1, n_iter=3)
# tell() computes the next point ready for the next call to ask()
# hence there are three after three iterations
assert_equal(len(opt.models), 3)
assert_equal(len(opt.Xi), 3)
opt.ask()
assert_equal(len(opt.models), 3)
assert_equal(len(opt.Xi), 3)
assert_equal(opt.ask(), opt.ask())
def test_multiple_asks():
# calling ask() multiple times without a tell() inbetween should
# be a "no op"
base_estimator = ExtraTreesRegressor(random_state=2)
opt = Optimizer([(-2.0, 2.0)], base_estimator, n_initial_points=1,
acq_optimizer="sampling")
opt.run(bench1, n_iter=3)
# tell() computes the next point ready for the next call to ask()
# hence there are three after three iterations
assert_equal(len(opt.models), 3)
assert_equal(len(opt.Xi), 3)
opt.ask()
assert_equal(len(opt.models), 3)
assert_equal(len(opt.Xi), 3)
assert_equal(opt.ask(), opt.ask())
def test_optimizer_base_estimator_string_smoke(base_estimator):
opt = Optimizer([(-2.0, 2.0)],
base_estimator=base_estimator,
n_initial_points=1,
acq_func="EI")
opt.run(func=lambda x: x[0]**2, n_iter=3)
def test_optimizer_base_estimator_string_smoke(base_estimator):
opt = Optimizer([(-2.0, 2.0)], base_estimator=base_estimator,
n_initial_points=1, acq_func="EI")
opt.run(func=lambda x: x[0]**2, n_iter=3)
