def test_discard_with_dimod(self):
sample0 = {0: -1, 1: -1, 2: +1}
sample1 = {0: +1, 1: -1, 2: +1}
samples = [sample0, sample1]
# now set up an embedding that works for one sample and not the other
embedding = {'a': {0, 1}, 'b': {2}}
# load the samples into a dimod response
response = dimod.SpinResponse()
response.add_samples_from(samples, (0 for __ in samples))
# specify that majority vote should be used
source_samples = eutil.unembed_samples(
response, embedding, chain_break_method=eutil.discard)
# only the first sample should be returned
self.assertEqual(len(source_samples), 1)
self.assertEqual(source_samples, [{'a': -1, 'b': +1}])
def test_majority_vote_with_dimod(self):
sample0 = {0: -1, 1: -1, 2: +1}
sample1 = {0: +1, 1: -1, 2: +1}
samples = [sample0, sample1]
embedding = {'a': {0, 1, 2}}
# load the samples into a dimod response
response = dimod.SpinResponse()
response.add_samples_from(samples, (0 for __ in samples))
# specify that majority vote should be used
source_samples = eutil.unembed_samples(
response, embedding, chain_break_method=eutil.majority_vote)
source0, source1 = source_samples
self.assertEqual(source0['a'], -1)
self.assertEqual(source1['a'], +1)
def test_typical_dimod_response(self):
"""unembed should be compatible with the dimod response object"""
nodes = range(10)
target_samples = [{v: random.choice((-1, 1))
for v in nodes} for __ in range(100)]
# load the samples into a dimod response
response = dimod.SpinResponse()
response.add_samples_from(target_samples, (0 for __ in target_samples))
# embedding is map half the target vars to one source, and the other half to
# another
embedding = {'a': range(5), 'b': range(5, 10)}
# use the response as the target_samples
source_samples = eutil.unembed_samples(response, embedding)
# not checking correctness of samples, just that they have the correct form
self.assertEqual(len(source_samples), 100)
for sample in source_samples:
self.assertIsInstance(sample, dict)
self.assertEqual(set(sample), {'a', 'b'}) # maps to source vars
self.assertTrue(all(bias in (-1, 1) for bias in sample.values()))
def sample_ising(self, h, J, embedding_tag=None, **sapi_kwargs):
"""Embeds the given problem using sapi's find_embedding then invokes
the given sampler to solve it.
Args:
h (dict/list): The linear terms in the Ising problem. If a
dict, should be of the form {v: bias, ...} where v is
a variable in the Ising problem, and bias is the linear
bias associated with v. If a list, should be of the form
[bias, ...] where the indices of the biases are the
variables in the Ising problem.
J (dict): A dictionary of the quadratic terms in the Ising
problem. Should be of the form {(u, v): bias} where u,
v are variables in the Ising problem and bias is the
quadratic bias associated with u, v.
embedding_tag: Allows the user to specify a tag for the generated
embedding. Useful for when the user wishes to submit multiple
problems with the same logical structure.
Additional keyword parameters are the same as for
SAPI's solve_ising function, see QUBIST documentation.
Returns:
:class:`dimod.SpinResponse`: The unembedded samples.
Examples:
>>> sampler = sapi.EmbeddingComposite(sapi.SAPILocalSampler('c4-sw_optimize'))
>>> response = sampler.sample_ising({}, {(0, 1): 1, (0, 2): 1, (1, 2): 1})
Using the embedding_tag, the embedding is generated only once.
>>> h = {0: .1, 1: 1.3, 2: -1.}
>>> J = {(0, 1): 1, (1, 2): 1, (0, 2): 1}
>>> sampler = sapi.EmbeddingComposite(sapi.SAPILocalSampler('c4-sw_optimize'))
>>> response0 = sampler.sample_ising(h, J, embedding_tag='K3')
>>> response1 = sampler.sample_ising(h, J, embedding_tag='K3')
"""
# get the sampler that is used by the composite
sampler = self._child
# the ising_index_labels decorator converted the keys of h to be indices 0, n-1
# sapi wants h to be a list, so let's make that conversion, using the keys as
# the indices.
h_list = [h[v] for v in range(len(h))]
# get the structure of the child sampler. The first value are the nodes which
# we don't need, the second is the set of edges available.
(__, edgeset) = sampler.structure
if embedding_tag is None or embedding_tag not in self.cached_embeddings:
# we have not previously cached an embedding so we need to determine it
# get the adjacency structure of our problem
S = set(J)
S.update({(v, v) for v in h})
# embed our adjacency structure, S, into the edgeset of the sampler.
embeddings = find_embedding(S, edgeset)
# sometimes it fails, often because the problem is too large
if J and not embeddings:
raise Exception('No embedding found')
# now it is possible that h_list might include nodes not in embedding, so let's
# handle that case here
if len(h_list) > len(embeddings):
emb_qubits = set().union(*embeddings)
while len(h_list) > len(embeddings):
for v in sampler.solver.properties['qubits']:
if v not in emb_qubits:
embeddings.append([v])
emb_qubits.add(v)
break
if embedding_tag is not None:
# save the embedding for posterity
self.cached_embeddings[embedding_tag] = embeddings
else:
# the user has asserted that we can reuse a previously created embedding
embeddings = self.cached_embeddings[embedding_tag]
# embed the problem
h0, j0, jc, new_emb = embed_problem(h_list, J, embeddings, edgeset)
# combine jc and j0
emb_j = j0.copy()
emb_j.update(jc)
# pass the chains we made into the sampler if it wants them
if 'chains' in sampler.solver.properties['parameters'] and 'chains' not in sapi_kwargs:
sapi_kwargs['chains'] = new_emb
# invoke the child sampler
emb_response = sampler.sample_ising(h0, emb_j, **sapi_kwargs)
# we need the samples back into lists for the unembed_answer function
answers = [[sample[i] for i in range(len(sample))] for sample in emb_response]
# unemnbed
solutions = unembed_answer(answers, new_emb, 'minimize_energy', h_list, J)
# and back once again into dicts for dimod...
samples = ({v: sample[v] for v in h} for sample in solutions)
sample_data = (data for __, data in emb_response.samples(data=True))
response = dimod.SpinResponse()
response.add_samples_from(samples, sample_data=sample_data, h=h, J=J)
return response
