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truncatecomposite.py
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truncatecomposite.py
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# Copyright 2019 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.
"""
A composite that truncates the returned :obj:`dimod.SampleSet` based on options
specified by the user.
"""
import numpy as np
from dimod.core.composite import ComposedSampler
__all__ = 'TruncateComposite',
class TruncateComposite(ComposedSampler):
"""Composite to truncate the returned sample set.
Inherits from :class:`dimod.ComposedSampler`.
Post-processing can be expensive and sometimes you might want to only
handle the lowest-energy samples. This composite layer allows you to
pre-select the samples within a multi-composite pipeline.
Args:
child_sampler (:obj:`dimod.Sampler`):
A dimod sampler.
n (int):
Maximum number of rows in the returned sample set.
sorted_by (str/None, optional, default='energy'):
Selects the record field used to sort the samples before
truncating. Note that sample order is maintained in the
underlying array.
aggregate (bool, optional, default=False):
If True, aggregates the samples before truncating and sets the value
of the ``num_occurrences`` field in the returned :class:`~dimod.SampleSet`
to the number of accumulated samples for each occurrence.
Examples:
>>> sampler = dimod.TruncateComposite(dimod.RandomSampler(), n=2, aggregate=True)
>>> bqm = dimod.BinaryQuadraticModel.from_ising({"a": 1, "b": 2}, {("a", "b"): -1})
>>> sampleset = sampler.sample(bqm, num_reads=100)
>>> print(sampleset) # doctest:+SKIP
a b energy num_oc.
0 -1 -1 -4.0 16
1 +1 -1 0.0 22
['SPIN', 2 rows, 38 samples, 2 variables]
"""
def __init__(self, child_sampler, n, sorted_by='energy', aggregate=False):
if n < 1:
raise ValueError('n should be a positive integer, recived {}'.format(n))
self._children = [child_sampler]
self._truncate_kwargs = dict(n=n, sorted_by=sorted_by)
self._aggregate = aggregate
@property
def children(self):
return self._children
@property
def parameters(self):
return self.child.parameters.copy()
@property
def properties(self):
return {'child_properties': self.child.properties.copy()}
def sample(self, bqm, **kwargs):
"""Sample from the binary quadratic model and truncate returned sample set.
Args:
bqm (:obj:`dimod.BinaryQuadraticModel`):
Binary quadratic model to be sampled from.
**kwargs:
Parameters for the sampling method, specified by the child
sampler.
Returns:
:obj:`dimod.SampleSet`
"""
tkw = self._truncate_kwargs
if self._aggregate:
return self.child.sample(bqm, **kwargs).aggregate().truncate(**tkw)
else:
return self.child.sample(bqm, **kwargs).truncate(**tkw)