# Copyright (c) 2017-2019 Uber Technologies, Inc.
# SPDX-License-Identifier: Apache-2.0
from torch.distributions.constraints import * # noqa F403
# isort: split
import torch
from torch.distributions.constraints import Constraint
from torch.distributions.constraints import __all__ as torch_constraints
from torch.distributions.constraints import (
independent,
lower_cholesky,
positive,
positive_definite,
)
# TODO move this upstream to torch.distributions
[docs]class _Integer(Constraint):
"""
Constrain to integers.
"""
is_discrete = True
def check(self, value):
return value % 1 == 0
def __repr__(self):
return self.__class__.__name__[1:]
[docs]class _Sphere(Constraint):
"""
Constrain to the Euclidean sphere of any dimension.
"""
event_dim = 1
reltol = 10. # Relative to finfo.eps.
def check(self, value):
eps = torch.finfo(value.dtype).eps
norm = torch.linalg.norm(value, dim=-1)
error = (norm - 1).abs()
return error < self.reltol * eps * value.size(-1) ** 0.5
def __repr__(self):
return self.__class__.__name__[1:]
[docs]class _CorrMatrix(Constraint):
"""
Constrains to a correlation matrix.
"""
event_dim = 2
def check(self, value):
# check for diagonal equal to 1
unit_variance = torch.all(torch.abs(torch.diagonal(value, dim1=-2, dim2=-1) - 1) < 1e-6, dim=-1)
# TODO: fix upstream - positive_definite has an extra dimension in front of output shape
return positive_definite.check(value) & unit_variance
[docs]class _OrderedVector(Constraint):
"""
Constrains to a real-valued tensor where the elements are monotonically
increasing along the `event_shape` dimension.
"""
event_dim = 1
def check(self, value):
if value.ndim == 0:
return torch.tensor(False, device=value.device)
elif value.shape[-1] == 1:
return torch.ones_like(value[..., 0], dtype=bool)
else:
return torch.all(value[..., 1:] > value[..., :-1], dim=-1)
[docs]class _PositiveOrderedVector(Constraint):
"""
Constrains to a positive real-valued tensor where the elements are monotonically
increasing along the `event_shape` dimension.
"""
def check(self, value):
return ordered_vector.check(value) & independent(positive, 1).check(value)
[docs]class _SoftplusPositive(type(positive)):
def __init__(self):
super().__init__(lower_bound=0.0)
[docs]class _SoftplusLowerCholesky(type(lower_cholesky)):
pass
corr_matrix = _CorrMatrix()
integer = _Integer()
ordered_vector = _OrderedVector()
positive_ordered_vector = _PositiveOrderedVector()
sphere = _Sphere()
softplus_positive = _SoftplusPositive()
softplus_lower_cholesky = _SoftplusLowerCholesky()
corr_cholesky_constraint = corr_cholesky # noqa: F405 DEPRECATED
__all__ = [
'corr_cholesky_constraint',
'corr_matrix',
'integer',
'ordered_vector',
'positive_ordered_vector',
'softplus_lower_cholesky',
'softplus_positive',
'sphere',
]
__all__.extend(torch_constraints)
__all__ = sorted(set(__all__))
del torch_constraints
# Create sphinx documentation.
__doc__ = """
Pyro's constraints library extends
:mod:`torch.distributions.constraints`.
"""
__doc__ += "\n".join([
"""
{}
----------------------------------------------------------------
{}
""".format(
_name,
"alias of :class:`torch.distributions.constraints.{}`".format(_name)
if globals()[_name].__module__.startswith("torch") else
".. autoclass:: {}".format(_name if type(globals()[_name]) is type else
type(globals()[_name]).__name__)
)
for _name in sorted(__all__)
])