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Add zeta param to control TR eps init #832

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Mar 21, 2024
Merged

Add zeta param to control TR eps init #832

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6ba773d
Add zeta param to control eps init
khurram-ghani Mar 20, 2024
d365717
Reword zeta docstring
khurram-ghani Mar 21, 2024
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46 changes: 35 additions & 11 deletions tests/unit/acquisition/test_rule.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1387,7 +1387,8 @@ def test_trust_region_box_get_dataset_min_outside_search_space() -> None:
npt.assert_array_equal(y_min, tf.constant([np.inf], dtype=tf.float64))


def test_trust_region_box_initialize() -> None:
@pytest.mark.parametrize("zeta", [None, 0.1, 0.7])
def test_trust_region_box_initialize(zeta: Optional[float]) -> None:
"""Initialize sets the box to a random location, and sets the eps and y_min values."""
search_space = Box([0.0, 0.0], [1.0, 1.0])
datasets = {
Expand All @@ -1397,10 +1398,15 @@ def test_trust_region_box_initialize() -> None:
)
}
# Includes a quick test of input_active_dims. The irrelevant input dimension should be ignored.
trb = SingleObjectiveTrustRegionBox(search_space, input_active_dims=[1, 2])
if zeta is not None:
trb = SingleObjectiveTrustRegionBox(search_space, zeta=zeta, input_active_dims=[1, 2])
else:
trb = SingleObjectiveTrustRegionBox(search_space, input_active_dims=[1, 2])
trb.initialize(datasets=datasets)

exp_eps = 0.5 * (search_space.upper - search_space.lower) / 5.0 ** (1.0 / 2.0)
exp_zeta = zeta if zeta is not None else 0.5 # Default value.
exp_eps = exp_zeta * (search_space.upper - search_space.lower)

npt.assert_array_equal(trb.eps, exp_eps)
npt.assert_array_compare(np.less_equal, search_space.lower, trb.location)
npt.assert_array_compare(np.less_equal, trb.location, search_space.upper)
Expand All @@ -1419,7 +1425,7 @@ def test_trust_region_box_requires_initialization() -> None:
tf.constant([[0.7], [0.9]], dtype=tf.float64),
)
}
trb = SingleObjectiveTrustRegionBox(search_space, min_eps=0.5)
trb = SingleObjectiveTrustRegionBox(search_space, min_eps=0.7)
trb.initialize(datasets=datasets)
location = trb.location

Expand All @@ -1446,9 +1452,12 @@ def test_trust_region_box_update_no_initialize() -> None:
)
}
# Includes a quick test of input_active_dims. The irrelevant input dimension should be ignored.
trb = SingleObjectiveTrustRegionBox(search_space, min_eps=0.1, input_active_dims=[0, 2])
trb = SingleObjectiveTrustRegionBox(
search_space, zeta=0.3, min_eps=0.1, input_active_dims=[0, 2]
)
trb.initialize(datasets=datasets)
trb.location = tf.constant([0.5, 0.5], dtype=tf.float64)
trb._update_bounds()
location = trb.location

assert not trb.requires_initialization
Expand Down Expand Up @@ -1641,12 +1650,13 @@ def __init__(
global_search_space: SearchSpace,
beta: float = 0.7,
kappa: float = 1e-4,
zeta: float = 0.5,
min_eps: float = 1e-2,
init_eps: float = 0.07,
):
self._location = fixed_location
self._init_eps_val = init_eps
super().__init__(global_search_space, beta, kappa, min_eps)
super().__init__(global_search_space, beta=beta, kappa=kappa, zeta=zeta, min_eps=min_eps)

@property
def location(self) -> TensorType:
Expand All @@ -1667,7 +1677,11 @@ def test_multi_trust_region_box_inits_regions_that_need_it() -> None:

subspaces = [
TestTrustRegionBox(
tf.constant([0.5 + i * 0.1], dtype=tf.float64), search_space, min_eps=0.3, init_eps=0.4
tf.constant([0.5 + i * 0.1], dtype=tf.float64),
search_space,
zeta=0.1,
min_eps=0.3,
init_eps=0.4,
)
for i in range(3)
]
Expand Down Expand Up @@ -2087,7 +2101,10 @@ def test_trust_region_discrete_get_dataset_min_outside_search_space(
npt.assert_array_equal(y_min, tf.constant([np.inf], dtype=tf.float64))


def test_trust_region_discrete_initialize(discrete_search_space: DiscreteSearchSpace) -> None:
@pytest.mark.parametrize("zeta", [None, 0.1, 0.7])
def test_trust_region_discrete_initialize(
discrete_search_space: DiscreteSearchSpace, zeta: Optional[float]
) -> None:
"""Check initialize sets the region to a random location, and sets the eps and y_min values."""
datasets = {
OBJECTIVE: Dataset( # Points outside the search space should be ignored.
Expand All @@ -2096,10 +2113,17 @@ def test_trust_region_discrete_initialize(discrete_search_space: DiscreteSearchS
)
}
# Includes a quick test of input_active_dims. The irrelevant input dimension should be ignored.
tr = SingleObjectiveTrustRegionDiscrete(discrete_search_space, input_active_dims=[1, 2])
if zeta is not None:
tr = SingleObjectiveTrustRegionDiscrete(
discrete_search_space, zeta=zeta, input_active_dims=[1, 2]
)
else:
tr = SingleObjectiveTrustRegionDiscrete(discrete_search_space, input_active_dims=[1, 2])
tr.initialize(datasets=datasets)

exp_eps = 0.5 * (discrete_search_space.upper - discrete_search_space.lower) / 5.0 ** (1.0 / 2.0)
exp_zeta = zeta if zeta is not None else 0.5 # Default value.
exp_eps = exp_zeta * (discrete_search_space.upper - discrete_search_space.lower)

npt.assert_array_equal(tr.eps, exp_eps)
npt.assert_array_compare(np.less_equal, discrete_search_space.lower, tr.location)
npt.assert_array_compare(np.less_equal, tr.location, discrete_search_space.upper)
Expand All @@ -2119,7 +2143,7 @@ def test_trust_region_discrete_requires_initialization(
tf.constant([[0.7], [0.9]], dtype=tf.float64),
)
}
tr = SingleObjectiveTrustRegionDiscrete(discrete_search_space, min_eps=3.0)
tr = SingleObjectiveTrustRegionDiscrete(discrete_search_space, min_eps=4.0)
tr.initialize(datasets=datasets)
tr._location_ix = tf.constant([], dtype=tf.int32)
location = tr.location
Expand Down
16 changes: 10 additions & 6 deletions trieste/acquisition/rule.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1533,6 +1533,7 @@ def __init__(
global_search_space: SearchSpace,
beta: float = 0.7,
kappa: float = 1e-4,
zeta: float = 0.5,
min_eps: float = 1e-2,
region_index: Optional[int] = None,
input_active_dims: Optional[Union[slice, Sequence[int]]] = None,
Expand All @@ -1544,6 +1545,8 @@ def __init__(
:param beta: The inverse of the trust region contraction factor.
:param kappa: Scales the threshold for the minimal improvement required for a step to be
considered a success.
:param zeta: The initial size of the trust region is ``zeta`` times the size of the global
search space.
:param min_eps: The minimal size of the search space. If the size of the search space is
smaller than this, the search space is reinitialized.
:param region_index: The index of the region in a multi-region search space. This is used to
Expand All @@ -1555,6 +1558,7 @@ def __init__(
super().__init__(global_search_space, region_index, input_active_dims)
self._beta = beta
self._kappa = kappa
self._zeta = zeta
self._min_eps = min_eps

self._step_is_success = False
Expand All @@ -1574,9 +1578,7 @@ def requires_initialization(self) -> bool:
return not self._initialized or tf.reduce_any(self.eps < self._min_eps)

def _init_eps(self) -> None:
global_lower = self.global_search_space.lower
global_upper = self.global_search_space.upper
self.eps = 0.5 * (global_upper - global_lower) / (5.0 ** (1.0 / global_lower.shape[-1]))
self.eps = self._zeta * (self.global_search_space.upper - self.global_search_space.lower)

def _update_bounds(self) -> None:
self._lower = tf.reduce_max(
Expand Down Expand Up @@ -2109,6 +2111,7 @@ def __init__(
global_search_space: DiscreteSearchSpace,
beta: float = 0.7,
kappa: float = 1e-4,
zeta: float = 0.5,
min_eps: float = 1e-2,
region_index: Optional[int] = None,
input_active_dims: Optional[Union[slice, Sequence[int]]] = None,
Expand All @@ -2121,6 +2124,8 @@ def __init__(
:param beta: The inverse of the trust region contraction factor.
:param kappa: Scales the threshold for the minimal improvement required for a step to be
considered a success.
:param zeta: The initial size of the trust region is ``zeta`` times the size of the global
search space.
:param min_eps: The minimal size of the search space. If the size of the search space is
smaller than this, the search space is reinitialized.
:param region_index: The index of the region in a multi-region search space. This is used to
Expand All @@ -2132,6 +2137,7 @@ def __init__(
super().__init__(global_search_space, region_index, input_active_dims)
self._beta = beta
self._kappa = kappa
self._zeta = zeta
self._min_eps = min_eps
self._step_is_success = False
self._init_location()
Expand Down Expand Up @@ -2168,9 +2174,7 @@ def _init_location(self) -> None:
)[0]

def _init_eps(self) -> None:
global_lower = self.global_search_space.lower
global_upper = self.global_search_space.upper
self.eps = 0.5 * (global_upper - global_lower) / (5.0 ** (1.0 / global_lower.shape[-1]))
self.eps = self._zeta * (self.global_search_space.upper - self.global_search_space.lower)

def _compute_global_distances(self) -> None:
# Pairwise distances along each axis in the global search space.
Expand Down
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