def test_initialization(self):
sampler = dimod.RandomSampler()
dtest.assert_sampler_api(sampler)
self.assertEqual(sampler.properties, {})
self.assertEqual(sampler.parameters, {'num_reads': []})
def test_dimod_solver(self):
"""Run a set of beasely instances with 50 variables."""
qbsolv = dwave_qbsolv.QBSolv()
tests_path = os.path.dirname(os.path.abspath(__file__))
for index, expected_energy in enumerate(self.beasley50_energies):
index += 1
qubo = load_qubo(
self.beasley50_qubo_format.format(tests_path=tests_path,
index=index))
# Python 3 feature
# with self.subTest(index=index, energy=energy):
# Give each problem three shots. One should be enough.
# But this is a non-deterministic test.
for _ in range(3):
results = qbsolv.sample_qubo(qubo,
find_max=True,
solver=dimod.RandomSampler())
self.assertTrue(
any(
abs(datum.energy - expected_energy) < ENERGY_ERROR
for datum in results.data()))
break
else:
self.fail()
def test_sample_ising_kwargs(self):
sampler = dimod.TrackingComposite(dimod.RandomSampler())
h = {'a': -1}
J = {('a', 'b'): -1}
ss = sampler.sample_ising(h, J, num_reads=5)
self.assertEqual(sampler.input, dict(h=h, J=J, num_reads=5))
self.assertEqual(sampler.output, ss)
def test_energies(self):
bqm = dimod.BinaryQuadraticModel({0: 0.0, 1: 0.0, 2: 0.0},
{(0, 1): -1.0, (1, 2): 1.0, (0, 2): 1.0},
1.0,
dimod.SPIN)
sampler = dimod.RandomSampler()
response = sampler.sample(bqm, num_reads=10)
dtest.assert_response_energies(response, bqm)
def test_warnings(self):
G = dnx.chimera_graph(12)
sampler = EmbeddingComposite(
dimod.StructureComposite(dimod.RandomSampler(), G.nodes, G.edges))
# this will need chains lengths > 7
J = {uv: -1 for uv in itertools.combinations(range(40), 2)}
ss = sampler.sample_ising({}, J, warnings='SAVE')
self.assertIn('warnings', ss.info)
def test_warnings_as_class_variable(self):
G = dnx.chimera_graph(12)
sampler = EmbeddingComposite(
dimod.StructureComposite(dimod.RandomSampler(), G.nodes, G.edges))
# this will need chains lengths > 7
J = {uv: -1 for uv in itertools.combinations(range(40), 2)}
EmbeddingComposite.warnings_default = 'SAVE'
ss = sampler.sample_ising({}, J)
self.assertIn('warnings', ss.info)
EmbeddingComposite.warnings_default = 'IGNORE' # restore default
def test_energies(self):
bqm = dimod.BinaryQuadraticModel({
0: 0.0,
1: 0.0,
2: 0.0
}, {
(0, 1): -1.0,
(1, 2): 1.0,
(0, 2): 1.0
}, 1.0, dimod.SPIN)
sampler = dimod.RandomSampler()
response = sampler.sample(bqm, num_reads=10)
# check their energies
for sample, energy in response.data(['sample', 'energy']):
self.assertAlmostEqual(energy, bqm.energy(sample))
def test_warning_chain_strength(self):
G = dnx.chimera_graph(12)
sampler = EmbeddingComposite(
dimod.StructureComposite(dimod.RandomSampler(), G.nodes, G.edges))
J = {(0, 1): 100}
ss = sampler.sample_ising({}, J, warnings='SAVE')
self.assertIn('warnings', ss.info)
count = 0
for warning in ss.info['warnings']:
if issubclass(warning['type'], ChainStrengthWarning):
count += 1
self.assertEqual(count, 1)
def test_chimera(self):
C = dnx.chimera_graph(4)
nodelist = list(C.nodes())
edgelist = list(C.edges())
structsampler = dimod.StructureComposite(dimod.RandomSampler(),
nodelist=nodelist,
edgelist=edgelist)
Q = {(v, v): 0.1 for v in nodelist}
Q.update({edge: -1.0 for edge in edgelist})
sampler = CheckerboardTransformComposite(structsampler, C)
response = sampler.sample_qubo(Q, num_reads=1000)
dit.assert_response_energies(response,
dimod.BinaryQuadraticModel.from_qubo(Q))
def test_keyerror(self):
C16 = dnx.chimera_graph(16)
child_sampler = dimod.RandomSampler()
nodelist = sorted(C16.nodes)
edgelist = sorted(sorted(edge) for edge in C16.edges)
struc_sampler = dimod.StructureComposite(child_sampler, nodelist,
edgelist)
embedding = {0: [391, 386], 1: [390], 2: [385]}
sampler = FixedEmbeddingComposite(struc_sampler, embedding)
with self.assertRaises(ValueError):
sampler.sample_qubo({(1, 4): 1})
sampler.sample_qubo({(1, 2): 1}).record # sample and resolve future
sampler.sample_qubo({(1, 1): 1}).record # sample and resolve future
def test_kwargs(self):
bqm = dimod.BinaryQuadraticModel({}, {}, 0.0, dimod.SPIN)
with self.assertWarns(SamplerUnknownArgWarning):
dimod.RandomSampler().sample(bqm, a=5, b=2)
def setUp(self):
self.sampler = dimod.RandomSampler()
def setUp(self):
self.sampler = dimod.RandomSampler()
self.sampler_factory = dimod.RandomSampler
