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Implement the unwarp method of half rank component warper. This implementation mostly follows cpp output unwarping. #620

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Implement the unwarp method of half rank component warper. This implementation mostly follows cpp output unwarping. #620

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84 changes: 69 additions & 15 deletions vizier/_src/algorithms/designers/gp/output_warpers.py
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Original file line number Diff line number Diff line change
Expand Up @@ -29,17 +29,26 @@
from tensorflow_probability.substrates import jax as tfp


def _validate_and_deepcopy(labels_arr: chex.Array) -> chex.Array:
def _validate_labels(
labels_arr: chex.Array, warping: bool = True
) -> chex.Array:
"""Checks and modifies the shape and values of the labels."""
labels_arr = labels_arr.astype(float)
labels_arr_copy = copy.deepcopy(labels_arr)
if not (labels_arr.ndim == 2 and labels_arr.shape[-1] == 1):
raise ValueError('Labels need to be an array of shape (num_points, 1).')
if np.isposinf(labels_arr).any():
raise ValueError('Inifinity metric value is not valid.')
if np.isneginf(labels_arr).any():
labels_arr_copy[np.isneginf(labels_arr)] = np.nan
return labels_arr_copy
labels_arr[np.isneginf(labels_arr)] = np.nan
if (
np.unique(labels_arr[np.isfinite(labels_arr).flatten(), :]).size <= 1
and np.isnan(labels_arr).sum() == 0
) and warping:
raise ValueError(
'Labels need to include at least two finite unique value in the absence'
' of infeaible points.'
)
return labels_arr


class OutputWarper(abc.ABC):
Expand Down Expand Up @@ -103,7 +112,9 @@ def warp(self, labels_arr: chex.Array) -> chex.Array:
Returns:
(num_points, 1) shaped array of warped labels.
"""
labels_arr = _validate_and_deepcopy(labels_arr)
labels_arr = copy.deepcopy(labels_arr)
if np.isneginf(labels_arr).any():
labels_arr[np.isneginf(labels_arr)] = np.nan
if np.isfinite(labels_arr).all() and len(
np.unique(labels_arr).flatten()) == 1:
return np.zeros(labels_arr.shape)
Expand All @@ -127,7 +138,7 @@ def unwarp(self, labels_arr: chex.Array) -> chex.Array:
Returns:
(num_points, 1) shaped array of unwarped labels.
"""
labels_arr = _validate_and_deepcopy(labels_arr)
labels_arr = copy.deepcopy(labels_arr)
if (
np.isfinite(labels_arr).all()
and len(np.unique(labels_arr).flatten()) == 1
Expand Down Expand Up @@ -195,6 +206,11 @@ class HalfRankComponent(OutputWarper):
untouched.
"""

_median: Optional[float] = attr.field(default=None)
_stddev: Optional[float] = attr.field(default=None)
_dedup_median_index: Optional[int] = attr.field(default=None)
_unique_labels: Optional[chex.Array] = attr.field(default=None)

def _estimate_std_of_good_half(
self, unique_labels: chex.Array, threshold: float
) -> float:
Expand All @@ -221,16 +237,20 @@ def _estimate_std_of_good_half(

def warp(self, labels_arr: chex.Array) -> chex.Array:
"""See base class."""
labels_arr = _validate_and_deepcopy(labels_arr)
labels_arr = _validate_labels(labels_arr)
if labels_arr.size == 1:
return labels_arr
labels_arr = labels_arr.flatten()
# Compute median, unique labels, and ranks.
median = np.nanmedian(labels_arr)
self._median = median
self._stddev = np.nanstd(labels_arr)
unique_labels = np.unique(labels_arr[np.isfinite(labels_arr)])
self._unique_labels = unique_labels
ranks = stats.rankdata(labels_arr, method='dense') # nans ranked last.

dedup_median_index = unique_labels.searchsorted(median, 'left')
self._dedup_median_index = dedup_median_index
denominator = dedup_median_index + (unique_labels[dedup_median_index]
== median) * .5
estimated_std = self._estimate_std_of_good_half(unique_labels, median)
Expand All @@ -248,10 +268,43 @@ def warp(self, labels_arr: chex.Array) -> chex.Array:
return np.reshape(labels_arr, [-1, 1])

def unwarp(self, labels_arr: chex.Array) -> chex.Array:
raise NotImplementedError(
'unwarp method for HalfRankComponent is not implemented yet.'
labels_arr = _validate_labels(labels_arr, warping=False)
if np.isnan(labels_arr).any():
raise ValueError('Array passed to unwarp cannot include nans.')
if self._dedup_median_index == 0:
return self._median + self._stddev * labels_arr
labels_arr[labels_arr >= 0.0] = (
self._median + self._stddev * labels_arr[labels_arr >= 0.0]
)
rank_bad = np.array(
[
2 * stats.norm.cdf(y) * (self._dedup_median_index + 0.5) - 0.5
for y in labels_arr[labels_arr < 0.0]
]
)
if (rank_bad < -0.5).any() or (
rank_bad > 1.0001 * self._dedup_median_index
).any():
raise ValueError('Rank needs to be within [-0.5, 1.0001 * median-index].')
labels_bad = np.ones(labels_arr[labels_arr < 0.0].shape)
scale = self._stddev + self._median - np.min(self._unique_labels)
if scale < 0.0:
raise ValueError('Scale needs to be non-negative.')
r_ints, r_fracs = divmod(rank_bad[rank_bad >= 0.0], 1)
labels_bad[rank_bad >= 0.0] = np.array(
[
self._unique_labels(int(r_int)) * (1 - r_frac)
+ (self._unique_labels(int(r_int) + 1) * r_frac)
for r_int, r_frac in zip(r_ints, r_fracs)
]
)
labels_bad[rank_bad < 0.0] = (
np.min(self._unique_labels) + scale * rank_bad[rank_bad < 0.0]
)

labels_arr[labels_arr < 0.0] = labels_bad
return labels_arr


@attr.define
class LogWarperComponent(OutputWarper):
Expand All @@ -267,7 +320,7 @@ class LogWarperComponent(OutputWarper):

def warp(self, labels_arr: chex.Array) -> chex.Array:
"""See base class."""
labels_arr = _validate_and_deepcopy(labels_arr)
labels_arr = _validate_labels(labels_arr)
self._labels_min = np.nanmin(labels_arr)
self._labels_max = np.nanmax(labels_arr)
labels_arr = labels_arr.flatten()
Expand Down Expand Up @@ -303,7 +356,7 @@ class InfeasibleWarperComponent(OutputWarper):
"""Warps the infeasible/nan value to feasible/finite values."""

def warp(self, labels_arr: chex.Array) -> chex.Array:
labels_arr = _validate_and_deepcopy(labels_arr)
labels_arr = _validate_labels(labels_arr)
labels_arr = labels_arr.flatten()
labels_range = np.nanmax(labels_arr) - np.nanmin(labels_arr)
warped_bad_value = np.nanmin(labels_arr) - (0.5 * labels_range + 1)
Expand All @@ -328,7 +381,7 @@ def warp(self, labels_arr: chex.Array) -> chex.Array:
Returns:
(num_points, 1) shaped array of standardize labels.
"""
labels_arr = _validate_and_deepcopy(labels_arr)
labels_arr = _validate_labels(labels_arr)
if np.isnan(labels_arr).all():
raise ValueError('Labels need to have at least one non-NaN entry.')
labels_finite_ind = np.isfinite(labels_arr)
Expand Down Expand Up @@ -360,7 +413,8 @@ def warp(self, labels_arr: chex.Array) -> chex.Array:
Returns:
(num_points, 1) shaped array of normalized labels.
"""
labels_arr = _validate_and_deepcopy(labels_arr)
labels_arr = _validate_labels(labels_arr)

if np.isnan(labels_arr).all():
raise ValueError('Labels need to have at least one non-NaN entry.')
if np.nanmax(labels_arr) == np.nanmax(labels_arr):
Expand Down Expand Up @@ -451,7 +505,7 @@ def _estimate_variance(self, labels_arr: chex.Array) -> float:
(4 * num_points))**2)

def warp(self, labels_arr: chex.Array) -> chex.Array:
labels_arr = _validate_and_deepcopy(labels_arr)
labels_arr = _validate_labels(labels_arr)
labels_finite_ind = np.isfinite(labels_arr)
labels_arr_finite = labels_arr[labels_finite_ind]
labels_median = np.median(labels_arr_finite)
Expand Down Expand Up @@ -496,7 +550,7 @@ def __init__(
self.use_rank = use_rank

def warp(self, labels_arr: chex.Array) -> chex.Array:
labels_arr = _validate_and_deepcopy(labels_arr)
labels_arr = _validate_labels(labels_arr)
labels_arr = np.asarray(labels_arr, dtype=np.float64)
labels_arr_flattened = labels_arr.flatten()
if self.use_rank:
Expand Down
24 changes: 15 additions & 9 deletions vizier/_src/algorithms/designers/gp/output_warpers_test.py
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Original file line number Diff line number Diff line change
Expand Up @@ -102,15 +102,6 @@ def warper(self) -> OutputWarper:
def always_maps_to_finite(self) -> bool:
return True

def test_all_nonfinite_labels(self):
labels_infeaible = np.array([[-np.inf], [np.nan], [np.nan], [-np.inf]])
self.assertTrue(
(
self.warper.warp(labels_infeaible)
== -1 * np.ones(shape=labels_infeaible.shape).flatten()
).all()
)

@parameterized.parameters([
dict(labels=np.zeros(shape=(5, 1))),
dict(labels=np.ones(shape=(5, 1))),
Expand Down Expand Up @@ -376,5 +367,20 @@ def test_known_arrays(self):
# TODO: Add a couple of parameterized test cases.
self.skipTest('No test cases provided')


class OutputWarperPipelineTest(absltest.TestCase):
"""Tests the default outpur warper edge cases."""

def test_all_nonfinite_labels(self):
warper = output_warpers.OutputWarperPipeline()
labels_infeaible = np.array([[-np.inf], [np.nan], [np.nan], [-np.inf]])
self.assertTrue(
(
warper.warp(labels_infeaible)
== -1 * np.ones(shape=labels_infeaible.shape).flatten()
).all()
)


if __name__ == '__main__':
absltest.main()