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_invalid_tell_arguments_list():
base_estimator = ExtraTreesRegressor(random_state=2)
opt = Optimizer([(-2.0, 2.0)],
base_estimator,
n_initial_points=1,
acq_optimizer="sampling")
assert_raises(ValueError, opt.tell, [[1.0], [2.0]], [1.0, None])
def test_invalid_tell_arguments():
base_estimator = ExtraTreesRegressor(random_state=2)
opt = Optimizer([(-2.0, 2.0)],
base_estimator,
n_initial_points=1,
acq_optimizer="sampling")
# can't have single point and multiple values for y
assert_raises(ValueError, opt.tell, [1.0], [1.0, 1.0])
def test_returns_result_object():
base_estimator = ExtraTreesRegressor(random_state=2)
opt = Optimizer([(-2.0, 2.0)],
base_estimator,
n_initial_points=1,
acq_optimizer="sampling")
result = opt.tell([1.5], 2.0)
assert isinstance(result, OptimizeResult)
assert_equal(len(result.x_iters), len(result.func_vals))
assert_equal(np.min(result.func_vals), result.fun)
def test_dump_and_load_optimizer():
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)
with tempfile.TemporaryFile() as f:
dump(opt, f)
f.seek(0)
load(f)
def test_bounds_checking_1D():
low = -2.0
high = 2.0
base_estimator = ExtraTreesRegressor(random_state=2)
opt = Optimizer([(low, high)],
base_estimator,
n_initial_points=1,
acq_optimizer="sampling")
assert_raises(ValueError, opt.tell, [high + 0.5], 2.0)
assert_raises(ValueError, opt.tell, [low - 0.5], 2.0)
# feed two points to tell() at once
assert_raises(ValueError, opt.tell, [high + 0.5, high], (2.0, 3.0))
assert_raises(ValueError, opt.tell, [low - 0.5, high], (2.0, 3.0))
def test_bounds_checking_2D():
low = -2.0
high = 2.0
base_estimator = ExtraTreesRegressor(random_state=2)
opt = Optimizer(
[(low, high), (low + 4, high + 4)],
base_estimator,
n_initial_points=1,
acq_optimizer="sampling",
)
assert_raises(ValueError, opt.tell, [high + 0.5, high + 4.5], 2.0)
assert_raises(ValueError, opt.tell, [low - 0.5, low - 4.5], 2.0)
# first out, second in
assert_raises(ValueError, opt.tell, [high + 0.5, high + 0.5], 2.0)
assert_raises(ValueError, opt.tell, [low - 0.5, high + 0.5], 2.0)
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_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())
opt.update_next()
assert_equal(opt.ask(), opt.ask())
def test_bounds_checking_2D_multiple_points():
low = -2.0
high = 2.0
base_estimator = ExtraTreesRegressor(random_state=2)
opt = Optimizer(
[(low, high), (low + 4, high + 4)],
base_estimator,
n_initial_points=1,
acq_optimizer="sampling",
)
# first component out, second in
assert_raises(
ValueError,
opt.tell,
[(high + 0.5, high + 0.5), (high + 0.5, high + 0.5)],
[2.0, 3.0],
)
assert_raises(
ValueError,
opt.tell,
[(low - 0.5, high + 0.5), (low - 0.5, high + 0.5)],
[2.0, 3.0],
)
def test_n_random_starts_Optimizer():
# n_random_starts got renamed in v0.4
et = ExtraTreesRegressor(random_state=2)
with pytest.deprecated_call():
Optimizer([(0, 1.0)], et, n_random_starts=10, acq_optimizer="sampling")
def test_extra_forest():
# toy sample
X = [[-2, -1], [-1, -1], [-1, -2], [1, 1], [1, 2], [2, 1]]
y = [-1, -1, -1, 1, 1, 1]
T = [[-1, -1], [2, 2], [3, 2]]
true_result = [-1, 1, 1]
clf = ExtraTreesRegressor(n_estimators=10, random_state=1)
clf.fit(X, y)
assert_array_equal(clf.predict(T), true_result)
assert 10 == len(clf)
clf = ExtraTreesRegressor(n_estimators=10,
min_impurity_decrease=0.1,
random_state=1)
clf.fit(X, y)
assert_array_equal(clf.predict(T), true_result)
assert 10 == len(clf)
clf = ExtraTreesRegressor(
n_estimators=10,
criterion="mse",
max_depth=None,
min_samples_split=2,
min_samples_leaf=1,
min_weight_fraction_leaf=0.0,
max_features="auto",
max_leaf_nodes=None,
min_impurity_decrease=0.0,
bootstrap=False,
oob_score=False,
n_jobs=1,
random_state=1,
verbose=0,
warm_start=False,
)
clf.fit(X, y)
assert_array_equal(clf.predict(T), true_result)
assert 10 == len(clf)
clf = ExtraTreesRegressor(n_estimators=10, max_features=1, random_state=1)
clf.fit(X, y)
assert_array_equal(clf.predict(T), true_result)
assert 10 == len(clf)
# also test apply
leaf_indices = clf.apply(X)
assert leaf_indices.shape == (len(X), clf.n_estimators)
from sklearn.multioutput import MultiOutputRegressor
from numpy.testing import assert_array_equal
from numpy.testing import assert_equal
from numpy.testing import assert_raises
from deephyper.skopt import gp_minimize
from deephyper.skopt import forest_minimize
from deephyper.skopt.benchmarks import bench1, bench1_with_time
from deephyper.skopt.benchmarks import branin
from deephyper.skopt.learning import ExtraTreesRegressor, RandomForestRegressor
from deephyper.skopt.learning import GradientBoostingQuantileRegressor
from deephyper.skopt.optimizer import Optimizer
from scipy.optimize import OptimizeResult
TREE_REGRESSORS = (
ExtraTreesRegressor(random_state=2),
RandomForestRegressor(random_state=2),
GradientBoostingQuantileRegressor(random_state=2),
)
ACQ_FUNCS_PS = ["EIps", "PIps"]
ACQ_FUNCS_MIXED = ["EI", "EIps"]
ESTIMATOR_STRINGS = [
"GP",
"RF",
"ET",
"GBRT",
"DUMMY",
"gp",
"rf",
"et",
"gbrt",