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quadratic_model.py
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quadratic_model.py
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# Copyright 2021 D-Wave Systems Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
r"""Quadratic models are problems of the form:
.. math::
E(x) = \sum_i a_i x_i + \sum_{i \le j} b_{i, j} x_i x_j + c
where :math:`\{ x_i\}_{i=1, \dots, N}` can be binary\ [#]_ or integer
variables and :math:`a_{i}, b_{ij}, c` are real values.
.. [#]
For binary variables, the range of the quadratic-term summation is
:math:`i < j` because :math:`x^2 = x` for binary values :math:`\{0, 1\}`
and :math:`s^2 = 1` for spin values :math:`\{-1, 1\}`.
"""
from __future__ import annotations
import struct
import tempfile
import typing
from collections.abc import Callable, Set
from copy import deepcopy
from numbers import Number
from typing import Any, Dict, Iterator, Iterable, Mapping, Optional, Sequence, Sized, Tuple, Union
from typing import BinaryIO, ByteString
from typing import TYPE_CHECKING
import numpy as np
try:
from numpy.typing import ArrayLike, DTypeLike
except ImportError:
ArrayLike = Any
DTypeLike = Any
from dimod.decorators import forwarding_method, unique_variable_labels
from dimod.quadratic.cyqm import cyQM_float32, cyQM_float64
from dimod.serialization.fileview import (
SpooledTemporaryFile,
_BytesIO,
LinearSection,
NeighborhoodSection,
OffsetSection,
VariablesSection,
VartypesSection,
load,
read_header,
write_header,
)
from dimod.sym import Eq, Ge, Le, Comparison
from dimod.typing import Variable, Bias, VartypeLike
from dimod.variables import Variables
from dimod.vartypes import Vartype, as_vartype
from dimod.views.quadratic import QuadraticViewsMixin
if TYPE_CHECKING:
# avoid circular imports
from dimod import BinaryQuadraticModel, ConstrainedQuadraticModel
__all__ = ['QuadraticModel', 'QM', 'Integer', 'Integers', 'IntegerArray', 'Real', 'Reals']
QM_MAGIC_PREFIX = b'DIMODQM'
Vartypes = Union[Mapping[Variable, Vartype], Iterable[Tuple[Variable, VartypeLike]]]
class QuadraticModel(QuadraticViewsMixin):
r"A quadratic model."
_DATA_CLASSES = {
np.dtype(np.float32): cyQM_float32,
np.dtype(np.float64): cyQM_float64,
}
DEFAULT_DTYPE = np.float64
"""The default dtype used to construct the class."""
def __init__(self,
linear: Optional[Mapping[Variable, Bias]] = None,
quadratic: Optional[Mapping[Tuple[Variable, Variable], Bias]] = None,
offset: Bias = 0,
vartypes: Optional[Vartypes] = None,
*,
dtype: Optional[DTypeLike] = None):
dtype = np.dtype(self.DEFAULT_DTYPE) if dtype is None else np.dtype(dtype)
self.data = self._DATA_CLASSES[np.dtype(dtype)]()
if vartypes is not None:
if isinstance(vartypes, Mapping):
vartypes = vartypes.items()
for v, vartype in vartypes:
self.add_variable(vartype, v)
self.set_linear(v, 0)
# todo: in the future we can support more types for construction, but
# let's keep it simple for now
if linear is not None:
for v, bias in linear.items():
self.add_linear(v, bias)
if quadratic is not None:
for (u, v), bias in quadratic.items():
self.add_quadratic(u, v, bias)
self.offset += offset
def __deepcopy__(self, memo: Dict[int, Any]) -> 'QuadraticModel':
new = type(self).__new__(type(self))
new.data = deepcopy(self.data, memo)
memo[id(self)] = new
return new
def __repr__(self):
vartypes = {v: self.vartype(v).name for v in self.variables}
return (f"{type(self).__name__}({self.linear}, {self.quadratic}, "
f"{self.offset}, {vartypes}, dtype={self.dtype.name!r})")
def __add__(self, other: Union['QuadraticModel', Bias]) -> 'QuadraticModel':
# in python 3.8+ we could do this is functools.singledispatchmethod
if isinstance(other, QuadraticModel):
new = self.copy()
new.update(other)
return new
if isinstance(other, Number):
new = self.copy()
new.offset += other
return new
return NotImplemented
def __iadd__(self, other: Union['QuadraticModel', Bias]) -> 'QuadraticModel':
# in python 3.8+ we could do this is functools.singledispatchmethod
if isinstance(other, QuadraticModel):
self.update(other)
return self
if isinstance(other, Number):
self.offset += other
return self
return NotImplemented
def __radd__(self, other: Bias) -> 'QuadraticModel':
# should only miss on number
if isinstance(other, Number):
new = self.copy()
new.offset += other
return new
return NotImplemented
def __mul__(self, other: Union['QuadraticModel', Bias]) -> 'QuadraticModel':
if isinstance(other, QuadraticModel):
if not (self.is_linear() and other.is_linear()):
raise TypeError(
"cannot multiply QMs with interactions")
# todo: performance
new = type(self)(dtype=self.dtype)
for v in self.variables:
new.add_variable(self.vartype(v), v,
lower_bound=self.lower_bound(v),
upper_bound=self.upper_bound(v))
for v in other.variables:
new.add_variable(other.vartype(v), v,
lower_bound=other.lower_bound(v),
upper_bound=other.upper_bound(v))
self_offset = self.offset
other_offset = other.offset
for u, ubias in self.linear.items():
for v, vbias in other.linear.items():
if u == v:
u_vartype = self.vartype(u)
if u_vartype is Vartype.BINARY:
new.add_linear(u, ubias*vbias)
elif u_vartype is Vartype.SPIN:
new.offset += ubias * vbias
elif u_vartype is Vartype.INTEGER or u_vartype is Vartype.REAL:
new.add_quadratic(u, v, ubias*vbias)
else:
raise RuntimeError("unexpected vartype")
else:
new.add_quadratic(u, v, ubias * vbias)
new.add_linear(u, ubias * other_offset)
for v, bias in other.linear.items():
new.add_linear(v, bias*self_offset)
new.offset += self_offset*other_offset
return new
if isinstance(other, Number):
new = self.copy()
new.scale(other)
return new
return NotImplemented
def __imul__(self, other: Bias) -> 'QuadraticModel':
# in-place multiplication is only defined for numbers
if isinstance(other, Number):
self.scale(other)
return self
return NotImplemented
def __rmul__(self, other: Bias) -> 'QuadraticModel':
# should only miss on number
if isinstance(other, Number):
return self * other # communative
return NotImplemented
def __neg__(self: 'QuadraticModel') -> 'QuadraticModel':
new = self.copy()
new.scale(-1)
return new
def __pow__(self, other: int) -> 'QuadraticModel':
if isinstance(other, int):
if other != 2:
raise ValueError("the only supported power for quadratic models is 2")
if not self.is_linear():
raise ValueError("only linear models can be squared")
return self * self
return NotImplemented
def __sub__(self, other: Union['QuadraticModel', Bias]) -> 'QuadraticModel':
if isinstance(other, QuadraticModel):
new = self.copy()
new.scale(-1)
new.update(other)
new.scale(-1)
return new
if isinstance(other, Number):
new = self.copy()
new.offset -= other
return new
return NotImplemented
def __isub__(self, other: Union['QuadraticModel', Bias]) -> 'QuadraticModel':
if isinstance(other, QuadraticModel):
self.scale(-1)
self.update(other)
self.scale(-1)
return self
if isinstance(other, Number):
self.offset -= other
return self
return NotImplemented
def __rsub__(self, other: Bias) -> 'QuadraticModel':
# should only miss on a number
if isinstance(other, Number):
new = self.copy()
new.scale(-1)
new += other
return new
return NotImplemented
def __truediv__(self, other: Bias) -> 'BQM':
return self * (1 / other)
def __itruediv__(self, other: Bias) -> 'BQM':
self *= (1 / other)
return self
def __eq__(self, other: Number) -> Comparison:
if isinstance(other, Number):
return Eq(self, other)
return NotImplemented
def __ge__(self, other: Bias) -> Comparison:
if isinstance(other, Number):
return Ge(self, other)
return NotImplemented
def __le__(self, other: Bias) -> Comparison:
if isinstance(other, Number):
return Le(self, other)
return NotImplemented
@property
def dtype(self) -> np.dtype:
"""Data-type of the model's biases."""
return self.data.dtype
@property
def num_interactions(self) -> int:
"""Number of interactions in the model.
The complexity is linear in the number of variables.
"""
return self.data.num_interactions()
@property
def num_variables(self) -> int:
"""Number of variables in the model."""
return self.data.num_variables()
@property
def offset(self) -> np.number:
"""Constant energy offset associated with the model."""
return self.data.offset
@offset.setter
def offset(self, offset):
self.data.offset = offset
@property
def shape(self) -> Tuple[int, int]:
"""A 2-tuple of :attr:`num_variables` and :attr:`num_interactions`."""
return self.num_variables, self.num_interactions
@property
def variables(self) -> Variables:
"""The variables of the quadratic model.
Examples:
>>> qm = dimod.QuadraticModel()
>>> qm.add_variable('INTEGER', 'i')
'i'
>>> qm.add_variable('BINARY')
1
>>> qm.add_variable('BINARY', 'y')
'y'
>>> qm.variables
Variables(['i', 1, 'y'])
"""
return self.data.variables
@forwarding_method
def add_linear(self, v: Variable, bias: Bias, *,
default_vartype=None,
default_lower_bound=None,
default_upper_bound=None,
):
"""Add a linear bias to an existing variable or a new variable with
specified vartype.
Args:
v: Variable label.
bias: Linear bias for the variable.
default_vartype: The vartype of any variables not already in the
model. If ``default_vartype`` is ``None`` then missing
variables raise a ``ValueError``.
default_lower_bound: The lower bound of any variables not already
in the model. Ignored if ``default_vartype`` is ``None`` or
when the variable is :class:`~dimod.Vartype.BINARY` or
:class:`~dimod.Vartype.SPIN`.
default_upper_bound: The upper bound of any variables not already
in the model. Ignored if ``default_vartype`` is ``None`` or
when the variable is :class:`~dimod.Vartype.BINARY` or
:class:`~dimod.Vartype.SPIN`.
Raises:
ValueError: If the variable is not in the model and
``default_vartype`` is ``None``.
"""
return self.data.add_linear
def add_linear_from(self,
linear: Union[Mapping[Variable, Bias], Iterable[Tuple[Variable, Bias]]],
*,
default_vartype=None,
default_lower_bound=None,
default_upper_bound=None,
):
"""Add variables and linear biases to a quadratic model.
Args:
linear:
Variables and their associated linear biases, as either a dict of
form ``{v: bias, ...}`` or an iterable of ``(v, bias)`` pairs,
where ``v`` is a variable and ``bias`` is its associated linear
bias.
default_vartype: The vartype of any variables not already in the
model. If ``default_vartype`` is ``None`` then missing
variables raise a ``ValueError``.
default_lower_bound: The lower bound of any variables not already
in the model. Ignored if ``default_vartype`` is ``None`` or
when the variable is :class:`~dimod.Vartype.BINARY` or
:class:`~dimod.Vartype.SPIN`.
default_upper_bound: The upper bound of any variables not already
in the model. Ignored if ``default_vartype`` is ``None`` or
when the variable is :class:`~dimod.Vartype.BINARY` or
:class:`~dimod.Vartype.SPIN`.
Raises:
ValueError: If the variable is not in the model and
``default_vartype`` is ``None``.
"""
add_linear = self.data.add_linear
if isinstance(linear, Mapping):
linear = linear.items()
# checking whether the keyword arguments are present actually
# results in a pretty shocking performance difference, almost x2
# for when they are not there
# I did try using functools.partial() as well
if default_vartype is None:
if default_lower_bound is None and default_upper_bound is None:
for v, bias in linear:
add_linear(v, bias)
else:
for v, bias in linear:
add_linear(v, bias,
default_lower_bound=default_lower_bound,
default_upper_bound=default_upper_bound,
)
else:
default_vartype = as_vartype(default_vartype, extended=True)
if default_lower_bound is None and default_upper_bound is None:
for v, bias in linear:
add_linear(v, bias,
default_vartype=default_vartype,
)
else:
for v, bias in linear:
add_linear(v, bias,
default_vartype=default_vartype,
default_lower_bound=default_lower_bound,
default_upper_bound=default_upper_bound,
)
@forwarding_method
def add_quadratic(self, u: Variable, v: Variable, bias: Bias):
"""Add quadratic bias to a pair of variables.
Args:
u: Variable in the quadratic model.
v: Variable in the quadratic model.
bias: Quadratic bias for the interaction.
Raises:
ValueError: If a specified variable is not in the model.
ValueError: If any self-loops are given on binary-valued variables.
E.g. ``(u, u, bias)`` is not a valid triplet for spin variables.
"""
return self.data.add_quadratic
def add_quadratic_from(self, quadratic: Union[Mapping[Tuple[Variable, Variable], Bias],
Iterable[Tuple[Variable, Variable, Bias]]]):
"""Add quadratic biases.
Args:
quadratic:
Interactions and their associated quadratic biases, as either a
dict of form ``{(u, v): bias, ...}`` or an iterable of
``(u, v, bias)`` triplets, where ``u`` and ``v`` are variables in
the model and ``bias`` is the associated quadratic bias.
If the interaction already exists, the bias is added.
Raises:
ValueError: If a specified variable is not in the model.
ValueError: If any self-loops are given on binary-valued variables.
E.g. ``(u, u, bias)`` is not a valid triplet for spin variables.
"""
if isinstance(quadratic, Mapping):
self.data.add_quadratic_from_iterable(
(u, v, bias) for (u, v), bias in quadratic.items())
else:
self.data.add_quadratic_from_iterable(quadratic)
@forwarding_method
def add_variable(self, vartype: VartypeLike, v: Optional[Variable] = None,
*,
lower_bound: float = 0,
upper_bound: Optional[float] = None,
) -> Variable:
"""Add a variable to the quadratic model.
Args:
vartype:
Variable type. One of:
* :class:`~dimod.Vartype.SPIN`, ``'SPIN'``, ``{-1, 1}``
* :class:`~dimod.Vartype.BINARY`, ``'BINARY'``, ``{0, 1}``
* :class:`~dimod.Vartype.INTEGER`, ``'INTEGER'``
* :class:`~dimod.Vartype.REAL`, ``'REAL'``
v:
Label for the variable. Defaults to the length of the
quadratic model, if that label is available. Otherwise defaults
to the lowest available positive integer label.
lower_bound:
Lower bound on the variable. Ignored when the variable is
:class:`~dimod.Vartype.BINARY` or :class:`~dimod.Vartype.SPIN`.
upper_bound:
Upper bound on the variable. Ignored when the variable is
:class:`~dimod.Vartype.BINARY` or :class:`~dimod.Vartype.SPIN`.
Returns:
The variable label.
"""
return self.data.add_variable
def add_variables_from(self, vartype: VartypeLike, variables: Iterable[Variable]):
"""Add multiple variables of the same type to the quadratic model.
Args:
vartype: Variable type. One of:
* :class:`~dimod.Vartype.SPIN`, ``'SPIN'``, ``{-1, 1}``
* :class:`~dimod.Vartype.BINARY`, ``'BINARY'``, ``{0, 1}``
* :class:`~dimod.Vartype.INTEGER`, ``'INTEGER'``
* :class:`~dimod.Vartype.REAL`, ``'REAL'``
variables: Iterable of variable labels.
Examples:
>>> from dimod import QuadraticModel, Binary
>>> qm = QuadraticModel()
>>> qm.add_variables_from('BINARY', ['x', 'y'])
"""
vartype = as_vartype(vartype, extended=True)
add_variable = self.data.add_variable
for v in variables:
add_variable(vartype, v)
def add_variables_from_model(self,
model: Union[BinaryQuadraticModel,
ConstrainedQuadraticModel,
QuadraticModel],
*,
variables: Optional[Iterable[Variable]] = None,
):
"""Add variables from another model.
Args:
model: A binary quadratic model, constrained quadratic model or
quadratic model.
variables: The variables from the model to add. If not specified
all of the variables are added.
Examples:
>>> qm0 = dimod.Integer('i', lower_bound=5, upper_bound=10) + dimod.Binary('x')
>>> qm1 = dimod.QuadraticModel()
>>> qm1.add_variables_from_model(qm0)
>>> qm1.variables
Variables(['i', 'x'])
>>> qm1.lower_bound('i')
5.0
"""
# avoid circular import
from dimod.binary import BinaryQuadraticModel
from dimod.constrained import ConstrainedQuadraticModel
if variables is None:
variables = model.variables
vartype = model.vartype if callable(model.vartype) else lambda v: model.vartype
for v in variables:
vt = vartype(v)
if vt is Vartype.SPIN or vt is Vartype.BINARY:
self.add_variable(vt, v)
else:
self.add_variable(vt, v,
lower_bound=model.lower_bound(v),
upper_bound=model.upper_bound(v))
def change_vartype(self, vartype: VartypeLike, v: Variable) -> "QuadraticModel":
"""Change the variable type of the given variable, updating the biases.
Args:
vartype: Variable type. One of:
* :class:`~dimod.Vartype.SPIN`, ``'SPIN'``, ``{-1, 1}``
* :class:`~dimod.Vartype.BINARY`, ``'BINARY'``, ``{0, 1}``
* :class:`~dimod.Vartype.INTEGER`, ``'INTEGER'``
* :class:`~dimod.Vartype.REAL`, ``'REAL'``
v: Variable to change to the specified ``vartype``.
Example:
>>> qm = dimod.QuadraticModel()
>>> a = qm.add_variable('SPIN', 'a')
>>> qm.set_linear(a, 1.5)
>>> qm.energy({a: +1})
1.5
>>> qm.energy({a: -1})
-1.5
>>> qm.change_vartype('BINARY', a)
QuadraticModel({'a': 3.0}, {}, -1.5, {'a': 'BINARY'}, dtype='float64')
>>> qm.energy({a: 1})
1.5
>>> qm.energy({a: 0})
-1.5
"""
self.data.change_vartype(vartype, v)
return self
def clear(self) -> None:
"""Remove the offset and all variables and interactions from the model."""
self.data.clear()
def copy(self):
"""Return a copy."""
return deepcopy(self)
@forwarding_method
def degree(self, v: Variable) -> int:
"""Return the degree of specified variable.
The degree is the number of interactions that contain a variable, ``v``.
Args:
v: Variable in the quadratic model.
"""
return self.data.degree
def energies(self, samples_like, dtype: Optional[DTypeLike] = None) -> np.ndarray:
"""Determine the energies of the given samples-like.
Args:
samples_like (samples_like):
Raw samples. `samples_like` is an extension of
NumPy's `array_like`_ structure. See :func:`.as_samples`.
dtype:
Desired NumPy data type for the energy.
Defaults to :class:`~numpy.float64`.
Returns:
Energies for the samples.
Examples:
>>> from dimod import QuadraticModel, Binary
>>> qm = QuadraticModel()
>>> qm.add_variables_from('BINARY', ['x', 'y'])
>>> qm.add_quadratic('x', 'y', -2)
>>> qm.energies([{'x': 1, 'y': 0}, {'x': 0, 'y': 0}, {'x': 1, 'y': 1}])
array([ 0., 0., -2.])
.. _`array_like`: https://numpy.org/doc/stable/user/basics.creation.html
"""
return self.data.energies(samples_like, dtype=dtype)
def energy(self, sample, dtype=None) -> Bias:
"""Determine the energy of the given sample.
Args:
sample (samples_like):
Raw sample. `samples_like` is an extension of
NumPy's `array_like`_ structure. See :func:`.as_samples`.
dtype:
Desired NumPy data type for the energy.
Defaults to :class:`~numpy.float64`.
Returns:
Energy for the sample.
Examples:
>>> from dimod import QuadraticModel, Binary
>>> qm = QuadraticModel()
>>> qm.add_variables_from('BINARY', ['x', 'y'])
>>> qm.add_quadratic('x', 'y', -2)
>>> qm.energy([{'x': 1, 'y': 1}])
-2.0
.. _`array_like`: https://numpy.org/doc/stable/user/basics.creation.html
"""
energies = self.energies(sample, dtype=dtype)
if not len(energies): # the empty case, happens with []
return self.dtype.type(0)
energy, = energies
return energy
def flip_variable(self, v: Variable):
"""Flip the specified binary-valued variable.
Args:
v: Binary-valued (:math:`\{0, 1\}` or :math:`\{-1, 1\}`) variable in
the quadratic model.
Raises:
ValueError: If ``v`` is not a variable in the model.
ValueError: If ``v`` is not a :class:`Vartype.SPIN` or
:class:`Vartype.BINARY` variable.
Examples:
In this example we flip the value of a binary variable ``x``. That
is we substitute ``(1 - x)`` which always takes the opposite value.
>>> x = dimod.Binary('x')
>>> s = dimod.Spin('s')
>>> qm = x + 2*s + 3*x*s
>>> qm.flip_variable('x')
>>> qm.is_equal((1-x) + 2*s + 3*(1-x)*s)
True
In this example we flip the value of a spin variable ``s``. That
is we substitute ``-s`` which always takes the opposite value.
>>> x = dimod.Binary('x')
>>> s = dimod.Spin('s')
>>> qm = x + 2*s + 3*x*s
>>> qm.flip_variable('s')
>>> qm.is_equal(x + 2*-s + 3*x*-s)
True
"""
vartype = self.vartype(v)
if vartype is Vartype.SPIN:
for u, bias in self.iter_neighborhood(v):
self.set_quadratic(u, v, -1*bias)
self.set_linear(v, -1*self.get_linear(v))
elif vartype is Vartype.BINARY:
for u, bias in self.iter_neighborhood(v):
self.set_quadratic(u, v, -1*bias)
self.add_linear(u, bias)
self.offset += self.get_linear(v)
self.set_linear(v, -1*self.get_linear(v))
else:
raise ValueError(f"can only flip SPIN and BINARY variables, {v} is {vartype.name}")
@classmethod
def from_bqm(cls, bqm: 'BinaryQuadraticModel') -> 'QuadraticModel':
"""Construct a quadratic model from a binary quadratic model.
Args:
bqm: Binary quadratic model from which to create the quadratic model.
Returns:
Quadratic model.
Examples:
>>> from dimod import QuadraticModel, BinaryQuadraticModel
>>> bqm = BinaryQuadraticModel({'a': 0.1, 'b': 0.2}, {'ab': -1}, 'SPIN')
>>> qm = QuadraticModel.from_bqm(bqm)
"""
obj = cls.__new__(cls)
try:
obj.data = obj._DATA_CLASSES[np.dtype(bqm.dtype)].from_cybqm(bqm.data)
except (TypeError, KeyError):
# not a cybqm or unsupported dtype
obj = cls()
else:
return obj
# fallback to python
for v in bqm.variables:
obj.set_linear(obj.add_variable(bqm.vartype, v), bqm.get_linear(v))
for u, v, bias in bqm.iter_quadratic():
obj.set_quadratic(u, v, bias)
obj.offset = bqm.offset
return obj
@classmethod
def from_file(cls, fp: Union[BinaryIO, ByteString]):
"""Construct a quadratic model from a file-like object.
The inverse of :meth:`~QuadraticModel.to_file`.
"""
if isinstance(fp, ByteString):
file_like: BinaryIO = _BytesIO(fp) # type: ignore[assignment]
else:
file_like = fp
header_info = read_header(file_like, QM_MAGIC_PREFIX)
num_variables, num_interactions = header_info.data['shape']
dtype = np.dtype(header_info.data['dtype'])
itype = np.dtype(header_info.data['itype'])
if header_info.version > (2, 0):
raise ValueError("cannot load a QM serialized with version "
f"{header_info.version!r}, "
"try upgrading your dimod version")
obj = cls(dtype=dtype)
# the vartypes
obj.data._ivartypes_load(VartypesSection.load(file_like), num_variables)
# offset
obj.offset += OffsetSection.load(file_like, dtype=dtype)
# linear
obj.data.add_linear_from_array(
LinearSection.load(file_like, dtype=dtype, num_variables=num_variables))
# quadratic
for vi in range(num_variables):
obj.data._ilower_triangle_load(vi, *NeighborhoodSection.load(file_like))
# labels (if applicable)
if header_info.data['variables']:
obj.relabel_variables(dict(enumerate(VariablesSection.load(file_like))))
return obj
@forwarding_method
def get_linear(self, v: Variable) -> Bias:
"""Get the linear bias of the specified variable.
Args:
v: Variable in the quadratic model.
"""
return self.data.get_linear
@forwarding_method
def get_quadratic(self, u: Variable, v: Variable,
default: Optional[Bias] = None) -> Bias:
"""Get the quadratic bias of the specified pair of variables.
Args:
u: Variable in the quadratic model.
v: Variable in the quadratic model.
default: Value to return if variables ``u`` and ``v`` have no interaction.
"""
return self.data.get_quadratic
def is_almost_equal(self, other: Union['QuadraticModel', 'BinaryQuadraticModel', Bias],
places: int = 7) -> bool:
"""Test for near equality to all biases of a given quadratic model.
Args:
other:
Quadratic model with which to compare biases.
places:
Number of decimal places to which the Python :func:`round`
function calculates approximate equality.
Examples:
>>> from dimod import QuadraticModel
>>> qm1 = QuadraticModel({'x': 0.0, 'i': 0.1234}, {('i', 'x'): -1.1234},
... 0.0, {'x': 'BINARY', 'i': 'INTEGER'})
>>> qm2 = QuadraticModel({'x': 0.0, 'i': 0.1232}, {('i', 'x'): -1.1229},
... 0.0, {'x': 'BINARY', 'i': 'INTEGER'})
>>> qm1.is_almost_equal(qm2, 4)
False
>>> qm1.is_almost_equal(qm2, 3)
True
"""
if isinstance(other, Number):
return not (self.num_variables or round(self.offset - other, places))
def eq(a, b):
return not round(a - b, places)
try:
if callable(other.vartype):
vartype_eq = all(self.vartype(v) == other.vartype(v) for v in self.variables)
else:
vartype_eq = all(self.vartype(v) == other.vartype for v in self.variables)
return (vartype_eq
and self.shape == other.shape
and eq(self.offset, other.offset)
and all(eq(self.get_linear(v), other.get_linear(v))
for v in self.variables)
and all(eq(bias, other.get_quadratic(u, v))
for u, v, bias in self.iter_quadratic())
)
except (AttributeError, ValueError):
# it's not a BQM or variables/interactions don't match
return False
def is_equal(self, other: Union['QuadraticModel', Number]) -> bool:
"""Return True if the given model has the same variables, vartypes and biases.
Args:
other: Quadratic model to compare against.
"""
if isinstance(other, Number):
return not self.num_variables and bool(self.offset == other)
# todo: performance
try:
if callable(other.vartype):
vartype_eq = all(self.vartype(v) == other.vartype(v) for v in self.variables)
else:
vartype_eq = all(self.vartype(v) == other.vartype for v in self.variables)
return (vartype_eq
and self.shape == other.shape # redundant, fast to check
and self.offset == other.offset
and self.linear == other.linear
and self.adj == other.adj)
except AttributeError:
return False
def is_linear(self) -> bool:
"""Return True if the model has no quadratic interactions."""
return self.data.is_linear()
@forwarding_method
def iter_neighborhood(self, v: Variable) -> Iterator[Tuple[Variable, Bias]]:
"""Iterate over the neighbors and quadratic biases of a variable.
Args:
v: Variable in the quadratic model.
Returns:
Neighbors of the specified variable and their quadratic biases.
Examples:
>>> from dimod import QuadraticModel
>>> qm = QuadraticModel()
>>> qm.add_variables_from('BINARY', ['x', 'y', 'z'])
>>> qm.add_quadratic('x', 'y', -2)
>>> qm.add_quadratic('x', 'z', 2)
>>> list(qm.iter_neighborhood('x'))
[('y', -2.0), ('z', 2.0)]
"""
return self.data.iter_neighborhood
@forwarding_method
def iter_quadratic(self) -> Iterator[Tuple[Variable, Variable, Bias]]:
"""Iterate over the interactions of a quadratic model.
Returns:
Interactions of the quadratic model and their biases.
Examples:
>>> from dimod import QuadraticModel
>>> qm = QuadraticModel()
>>> qm.add_variables_from('BINARY', ['x', 'y', 'z'])
>>> qm.add_quadratic('x', 'y', -2)
>>> qm.add_quadratic('x', 'z', 2)
>>> list(qm.iter_quadratic())
[('y', 'x', -2.0), ('z', 'x', 2.0)]
"""
return self.data.iter_quadratic
@forwarding_method
def lower_bound(self, v: Variable) -> Bias:
"""Return the lower bound on the specified variable.
Args:
v: Variable in the quadratic model.
"""
return self.data.lower_bound
def nbytes(self, capacity: bool = False) -> int:
"""Get the total bytes consumed by the biases, vartype info, bounds,
and indices.
Does not include the memory consumed by non-element attributes of
the quadratic model object.
Also does not include the memory consumed by the variable labels.
Args:
capacity: If ``capacity`` is true, also include the ``std::vector::capacity``
of the underlying vectors in the calculation.
Returns:
The number of bytes.
"""
return self.data.nbytes(capacity)
def set_lower_bound(self, v: Variable, lb: float):
"""Set the lower bound for a variable.
Args:
v: Variable in the quadratic model.
lb: Lower bound to set for variable ``v``.
Raises:
ValueError: If ``v`` is a :class:`~dimod.Vartype.SPIN`
or :class:`~dimod.Vartype.BINARY` variable.