def test_split_trials(self):
"""Test observed trials can be split based on TPE gamma"""
space = Space()
dim1 = Real("yolo1", "uniform", -3, 6)
space.register(dim1)
tpe = TPE(space, seed=1)
rng = np.random.RandomState(1)
points = numpy.linspace(-3, 3, num=10, endpoint=False).reshape(-1, 1)
objectives = numpy.linspace(0, 1, num=10, endpoint=False)
point_objectives = list(zip(points, objectives))
rng.shuffle(point_objectives)
points, objectives = zip(*point_objectives)
for point, objective in zip(points, objectives):
trial = _array_to_trial(point, space=tpe.space, y=objective)
tpe.observe([trial])
tpe.gamma = 0.25
below_trials, above_trials = tpe.split_trials()
below_points = [
_trial_to_array(t, space=tpe.space) for t in below_trials
]
assert below_points == [[-3.0], [-2.4], [-1.8]]
assert len(above_trials) == 7
tpe.gamma = 0.2
below_trials, above_trials = tpe.split_trials()
below_points = [
_trial_to_array(t, space=tpe.space) for t in below_trials
]
assert below_points == [[-3.0], [-2.4]]
assert len(above_trials) == 8
def test_suggest(self, tpe: TPE):
"""Test suggest with no shape dimensions"""
tpe.n_initial_points = 10
results = numpy.random.random(10)
for i in range(10):
trials = tpe.suggest(1)
assert trials is not None
assert len(trials) == 1
points = [_trial_to_array(t, space=tpe.space) for t in trials]
assert len(points[0]) == 3
assert not isinstance(points[0][0], tuple)
trials[0] = _add_result(trials[0], results[i])
tpe.observe(trials)
trials = tpe.suggest(1)
assert trials is not None
assert len(trials) == 1
points = [_trial_to_array(t, space=tpe.space) for t in trials]
assert len(points[0]) == 3
assert not isinstance(points[0][0], tuple)
def test_suggest_initial_points(self, tpe: TPE, monkeypatch):
"""Test that initial points can be sampled correctly"""
_points = [(i, i - 6, "c") for i in range(1, 12)]
_trials = [
format_trials.tuple_to_trial(point, space=tpe.space)
for point in _points
]
index = 0
def sample(num: int = 1, seed=None) -> list[Trial]:
nonlocal index
result = _trials[index:index + num]
index += num
return result
monkeypatch.setattr(tpe.space, "sample", sample)
tpe.n_initial_points = 10
results = numpy.random.random(10)
for i in range(1, 11):
trials = tpe.suggest(1)
assert trials is not None
trial = trials[0]
assert trial.params == _trials[i]
point = format_trials.trial_to_tuple(trial, space=tpe.space)
assert point == (i, i - 6, "c")
trial.results = [
Trial.Result(name="objective",
type="objective",
value=results[i - 1])
]
tpe.observe([trial])
trials = tpe.suggest(1)
assert trials is not None
trial = trials[0]
assert trial == _trials[-1]
# BUG: This is failing. We expect this trial to be sampled from the model, not from the
# search space.
assert format_trials.trial_to_tuple(trial,
space=tpe.space) != (11, 5, "c")
def test_suggest_ei_candidates(self, tpe: TPE):
"""Test suggest with no shape dimensions"""
tpe.n_initial_points = 2
tpe.n_ei_candidates = 0
results = numpy.random.random(2)
for i in range(2):
trials = tpe.suggest(1)
assert trials is not None
assert len(trials) == 1
points = [format_trials.trial_to_tuple(trials[0], space=tpe.space)]
assert len(points[0]) == 3
assert not isinstance(points[0][0], tuple)
trials[0] = _add_result(trials[0], results[i])
tpe.observe(trials)
trials = tpe.suggest(1)
assert not trials
tpe.n_ei_candidates = 24
trials = tpe.suggest(1)
assert trials is not None
assert len(trials) > 0