r"""
The following constraints are implemented:
- ``constraints.boolean``
- ``constraints.cat``
- ``constraints.dependent``
- ``constraints.greater_than(lower_bound)``
- ``constraints.integer_interval(lower_bound, upper_bound)``
- ``constraints.interval(lower_bound, upper_bound)``
- ``constraints.lower_cholesky``
- ``constraints.lower_triangular``
- ``constraints.nonnegative_integer``
- ``constraints.positive``
- ``constraints.positive_definite``
- ``constraints.positive_integer``
- ``constraints.real``
- ``constraints.real_vector``
- ``constraints.simplex``
- ``constraints.stack``
- ``constraints.unit_interval``
"""
import torch
__all__ = [
'Constraint',
'boolean',
'cat',
'dependent',
'dependent_property',
'greater_than',
'greater_than_eq',
'integer_interval',
'interval',
'half_open_interval',
'is_dependent',
'less_than',
'lower_cholesky',
'lower_triangular',
'nonnegative_integer',
'positive',
'positive_definite',
'positive_integer',
'real',
'real_vector',
'simplex',
'stack',
'unit_interval',
]
[docs]class Constraint(object):
"""
Abstract base class for constraints.
A constraint object represents a region over which a variable is valid,
e.g. within which a variable can be optimized.
"""
[docs] def check(self, value):
"""
Returns a byte tensor of `sample_shape + batch_shape` indicating
whether each event in value satisfies this constraint.
"""
raise NotImplementedError
def __repr__(self):
return self.__class__.__name__[1:] + '()'
class _Dependent(Constraint):
"""
Placeholder for variables whose support depends on other variables.
These variables obey no simple coordinate-wise constraints.
"""
def check(self, x):
raise ValueError('Cannot determine validity of dependent constraint')
def is_dependent(constraint):
return isinstance(constraint, _Dependent)
class _DependentProperty(property, _Dependent):
"""
Decorator that extends @property to act like a `Dependent` constraint when
called on a class and act like a property when called on an object.
Example::
class Uniform(Distribution):
def __init__(self, low, high):
self.low = low
self.high = high
@constraints.dependent_property
def support(self):
return constraints.interval(self.low, self.high)
"""
pass
class _Boolean(Constraint):
"""
Constrain to the two values `{0, 1}`.
"""
def check(self, value):
return (value == 0) | (value == 1)
class _IntegerInterval(Constraint):
"""
Constrain to an integer interval `[lower_bound, upper_bound]`.
"""
def __init__(self, lower_bound, upper_bound):
self.lower_bound = lower_bound
self.upper_bound = upper_bound
def check(self, value):
return (value % 1 == 0) & (self.lower_bound <= value) & (value <= self.upper_bound)
def __repr__(self):
fmt_string = self.__class__.__name__[1:]
fmt_string += '(lower_bound={}, upper_bound={})'.format(self.lower_bound, self.upper_bound)
return fmt_string
class _IntegerLessThan(Constraint):
"""
Constrain to an integer interval `(-inf, upper_bound]`.
"""
def __init__(self, upper_bound):
self.upper_bound = upper_bound
def check(self, value):
return (value % 1 == 0) & (value <= self.upper_bound)
def __repr__(self):
fmt_string = self.__class__.__name__[1:]
fmt_string += '(upper_bound={})'.format(self.upper_bound)
return fmt_string
class _IntegerGreaterThan(Constraint):
"""
Constrain to an integer interval `[lower_bound, inf)`.
"""
def __init__(self, lower_bound):
self.lower_bound = lower_bound
def check(self, value):
return (value % 1 == 0) & (value >= self.lower_bound)
def __repr__(self):
fmt_string = self.__class__.__name__[1:]
fmt_string += '(lower_bound={})'.format(self.lower_bound)
return fmt_string
class _Real(Constraint):
"""
Trivially constrain to the extended real line `[-inf, inf]`.
"""
def check(self, value):
return value == value # False for NANs.
class _GreaterThan(Constraint):
"""
Constrain to a real half line `(lower_bound, inf]`.
"""
def __init__(self, lower_bound):
self.lower_bound = lower_bound
def check(self, value):
return self.lower_bound < value
def __repr__(self):
fmt_string = self.__class__.__name__[1:]
fmt_string += '(lower_bound={})'.format(self.lower_bound)
return fmt_string
class _GreaterThanEq(Constraint):
"""
Constrain to a real half line `[lower_bound, inf)`.
"""
def __init__(self, lower_bound):
self.lower_bound = lower_bound
def check(self, value):
return self.lower_bound <= value
def __repr__(self):
fmt_string = self.__class__.__name__[1:]
fmt_string += '(lower_bound={})'.format(self.lower_bound)
return fmt_string
class _LessThan(Constraint):
"""
Constrain to a real half line `[-inf, upper_bound)`.
"""
def __init__(self, upper_bound):
self.upper_bound = upper_bound
def check(self, value):
return value < self.upper_bound
def __repr__(self):
fmt_string = self.__class__.__name__[1:]
fmt_string += '(upper_bound={})'.format(self.upper_bound)
return fmt_string
class _Interval(Constraint):
"""
Constrain to a real interval `[lower_bound, upper_bound]`.
"""
def __init__(self, lower_bound, upper_bound):
self.lower_bound = lower_bound
self.upper_bound = upper_bound
def check(self, value):
return (self.lower_bound <= value) & (value <= self.upper_bound)
def __repr__(self):
fmt_string = self.__class__.__name__[1:]
fmt_string += '(lower_bound={}, upper_bound={})'.format(self.lower_bound, self.upper_bound)
return fmt_string
class _HalfOpenInterval(Constraint):
"""
Constrain to a real interval `[lower_bound, upper_bound)`.
"""
def __init__(self, lower_bound, upper_bound):
self.lower_bound = lower_bound
self.upper_bound = upper_bound
def check(self, value):
return (self.lower_bound <= value) & (value < self.upper_bound)
def __repr__(self):
fmt_string = self.__class__.__name__[1:]
fmt_string += '(lower_bound={}, upper_bound={})'.format(self.lower_bound, self.upper_bound)
return fmt_string
class _Simplex(Constraint):
"""
Constrain to the unit simplex in the innermost (rightmost) dimension.
Specifically: `x >= 0` and `x.sum(-1) == 1`.
"""
def check(self, value):
return torch.all(value >= 0, dim=-1) & ((value.sum(-1) - 1).abs() < 1e-6)
class _LowerTriangular(Constraint):
"""
Constrain to lower-triangular square matrices.
"""
def check(self, value):
value_tril = value.tril()
return (value_tril == value).view(value.shape[:-2] + (-1,)).min(-1)[0]
class _LowerCholesky(Constraint):
"""
Constrain to lower-triangular square matrices with positive diagonals.
"""
def check(self, value):
value_tril = value.tril()
lower_triangular = (value_tril == value).view(value.shape[:-2] + (-1,)).min(-1)[0]
positive_diagonal = (value.diagonal(dim1=-2, dim2=-1) > 0).min(-1)[0]
return lower_triangular & positive_diagonal
class _PositiveDefinite(Constraint):
"""
Constrain to positive-definite matrices.
"""
def check(self, value):
matrix_shape = value.shape[-2:]
batch_shape = value.unsqueeze(0).shape[:-2]
# TODO: replace with batched linear algebra routine when one becomes available
# note that `symeig()` returns eigenvalues in ascending order
flattened_value = value.reshape((-1,) + matrix_shape)
return torch.stack([v.symeig(eigenvectors=False)[0][:1] > 0.0
for v in flattened_value]).view(batch_shape)
class _RealVector(Constraint):
"""
Constrain to real-valued vectors. This is the same as `constraints.real`,
but additionally reduces across the `event_shape` dimension.
"""
def check(self, value):
return torch.all(value == value, dim=-1) # False for NANs.
class _Cat(Constraint):
"""
Constraint functor that applies a sequence of constraints
`cseq` at the submatrices at dimension `dim`,
each of size `lengths[dim]`, in a way compatible with :func:`torch.cat`.
"""
def __init__(self, cseq, dim=0, lengths=None):
assert all(isinstance(c, Constraint) for c in cseq)
self.cseq = list(cseq)
if lengths is None:
lengths = [1] * len(self.cseq)
self.lengths = list(lengths)
assert len(self.lengths) == len(self.cseq)
self.dim = dim
def check(self, value):
assert -value.dim() <= self.dim < value.dim()
checks = []
start = 0
for constr, length in zip(self.cseq, self.lengths):
v = value.narrow(self.dim, start, length)
checks.append(constr.check(v))
start = start + length # avoid += for jit compat
return torch.cat(checks, self.dim)
class _Stack(Constraint):
"""
Constraint functor that applies a sequence of constraints
`cseq` at the submatrices at dimension `dim`,
in a way compatible with :func:`torch.stack`.
"""
def __init__(self, cseq, dim=0):
assert all(isinstance(c, Constraint) for c in cseq)
self.cseq = list(cseq)
self.dim = dim
def check(self, value):
assert -value.dim() <= self.dim < value.dim()
vs = [value.select(self.dim, i) for i in range(value.size(self.dim))]
return torch.stack([constr.check(v)
for v, constr in zip(vs, self.cseq)], self.dim)
# Public interface.
dependent = _Dependent()
dependent_property = _DependentProperty
boolean = _Boolean()
nonnegative_integer = _IntegerGreaterThan(0)
positive_integer = _IntegerGreaterThan(1)
integer_interval = _IntegerInterval
real = _Real()
real_vector = _RealVector()
positive = _GreaterThan(0.)
greater_than = _GreaterThan
greater_than_eq = _GreaterThanEq
less_than = _LessThan
unit_interval = _Interval(0., 1.)
interval = _Interval
half_open_interval = _HalfOpenInterval
simplex = _Simplex()
lower_triangular = _LowerTriangular()
lower_cholesky = _LowerCholesky()
positive_definite = _PositiveDefinite()
cat = _Cat
stack = _Stack
