def test_branch_with_single_component(self):
"""Traits requirements from inner runnable must be reflected in branch."""
class ValidSISO(Runnable, traits.SISO):
def next(self, state):
return state
with self.assertRaises(traits.StateDimensionalityError):
Branch(components=(ValidSISO(),)).run(States()).result()
self.assertEqual(Branch(components=(ValidSISO(),)).run(State(x=1)).result().x, 1)
class InvalidSISO(Runnable, traits.SISO):
def next(self, state):
return States(state, state)
with self.assertRaises(traits.StateDimensionalityError):
Branch(components=(InvalidSISO(),)).run(State()).result()
Branch(components=(InvalidSISO(),)).run(States()).result()
# input: list of states with subproblem
# output: list of states with subsamples
class SubproblemSamplerMIMO(Runnable, traits.MIMO, traits.SubproblemSampler):
def next(self, states):
return States(State(subsamples=1), State(subsamples=2))
with self.assertRaises(traits.StateDimensionalityError):
Branch(components=(SubproblemSamplerMIMO(),)).run(State()).result()
with self.assertRaises(traits.StateTraitMissingError):
Branch(components=(SubproblemSamplerMIMO(),)).run(States(State())).result()
r = Branch(components=(SubproblemSamplerMIMO(),)).run(States(State(subproblem=True))).result()
self.assertEqual(r[0].subsamples, 1)
self.assertEqual(r[1].subsamples, 2)
def next(self, state, **runopts):
runopts.update(racing_context=True)
futures = [
branch.run(state.updated(), **runopts) for branch in self.branches
]
# as soon as one is done, stop all others
done, _ = concurrent.futures.wait(
futures, return_when=concurrent.futures.FIRST_COMPLETED)
logger.trace(
"RacingBranches done set: {}. Stopping remaining.".format(done))
self.stop()
# debug info
idx = futures.index(done.pop())
branch = self.branches[idx]
logger.debug("{name} won idx={idx} branch={branch!r}".format(
name=self.name, idx=idx, branch=branch))
# collect resolved states (in original order, not completion order!)
states = States()
for f in futures:
states.append(f.result())
return states
def next(self, state):
output = States()
while True:
try:
state = self.runnable.run(state).result()
output.append(state)
except EndOfStream:
break
return output
def test_set_states_var(self):
"""All States have variable set."""
wrk = Const(x=1)
inp = States(State(), State(x=0), State(x=None))
exp = States(State(x=1), State(x=1), State(x=1))
self.assertEqual(wrk.run(inp).result(), exp)
wrk = Const(x=None)
inp = States(State(), State(x=1))
exp = States(State(x=None), State(x=None))
self.assertEqual(wrk.run(inp).result(), exp)
def next(self, state, **runopts):
samples = state.samples
if not samples:
raise ValueError("no input samples")
states = States()
n = len(samples)
for start, stop in zip(range(n), range(1, n + 1)):
sample = samples.slice(start, stop, sorted_by=None)
states.append(state.updated(samples=sample))
return states
def next(self, state, **runopts):
output = States()
runopts.update(executor=immediate_executor, silent_rewind=False)
while True:
try:
state = self.runnable.run(state, **runopts).result()
output.append(state)
except EndOfStream:
break
return output
def next(self, state, **runopts):
futures = [
branch.run(state.updated(), **runopts) for branch in self.branches
]
# wait for all branches to finish
concurrent.futures.wait(futures,
return_when=concurrent.futures.ALL_COMPLETED)
# collect resolved states (in original order, not completion order)
states = States()
for f in futures:
states.append(f.result())
return states
def test_mimo_with_specific_state_traits(self):
# input: list of states with subproblem
# output: list of states with subsamples
class SubproblemSamplerMIMO(Runnable, traits.MIMO, traits.SubproblemSampler):
def next(self, states):
return States(State(subsamples=1), State(subsamples=2))
with self.assertRaises(traits.StateDimensionalityError):
SubproblemSamplerMIMO().run(State()).result()
with self.assertRaises(traits.StateTraitMissingError):
SubproblemSamplerMIMO().run(States(State())).result()
r = SubproblemSamplerMIMO().run(States(State(subproblem=True))).result()
self.assertEqual(r[0].subsamples, 1)
self.assertEqual(r[1].subsamples, 2)
def test_validation(self):
bqm1 = dimod.BinaryQuadraticModel({'a': 1}, {}, 0, dimod.SPIN)
bqm2 = dimod.BinaryQuadraticModel({'b': 1}, {}, 0, dimod.SPIN)
s1 = State.from_sample({'a': +1}, bqm1)
s2 = State.from_sample({'b': -1}, bqm2)
# two input states required
with self.assertRaises(ValueError):
inp = States(s1, s1, s1)
IsoenergeticClusterMove().run(inp).result()
# variables must match
with self.assertRaises(ValueError):
inp = States(s1, s2)
IsoenergeticClusterMove().run(inp).result()
def next(self, state, **runopts):
output = States()
runopts.update(executor=immediate_executor, silent_rewind=False)
logger.debug("{} unwinding {!r}".format(self.name, self.runnable))
while True:
try:
state = self.runnable.run(state, **runopts).result()
output.append(state)
except EndOfStream:
break
logger.debug("{} collected {} states".format(self.name, len(output)))
return output
def test_map_lambda(self):
states = States(State(cnt=1), State(cnt=2))
result = Map(Lambda(lambda _, s: s.updated(cnt=s.cnt + 1))).run(
states).result()
self.assertEqual(result[0].cnt, states[0].cnt + 1)
self.assertEqual(result[1].cnt, states[1].cnt + 1)
def test_not_validated(self):
class NotValidated(Runnable, traits.NotValidated):
def next(self, state):
return state
self.assertEqual(NotValidated().run(State(x=1)).result().x, 1)
self.assertEqual(NotValidated().run(States(State(x=1))).result()[0].x, 1)
def test_dynamic_validation(self):
class simo(traits.SIMO, Runnable):
def next(self, state):
return States(state, state)
with self.assertRaises(StateDimensionalityError):
Unwind(simo()).run(States()).result()
def next(self, states):
self._futures = [self.runnable.run(state) for state in states]
concurrent.futures.wait(self._futures,
return_when=concurrent.futures.ALL_COMPLETED)
return States(*(f.result() for f in self._futures))
def test_bimodal_cluster_sampling_statistics(self):
bqm = dimod.BQM.from_qubo({'ab': 1, 'bd': 1, 'dc': 1, 'ca': 1})
nodes = sorted(bqm.variables)
s1 = State.from_samples(dict(zip(nodes, [0, 1, 0, 0])), bqm)
s2 = State.from_samples(dict(zip(nodes, [0, 0, 1, 0])), bqm)
exp1 = SampleSet.from_samples_bqm(dict(zip(nodes, [0, 0, 0, 0])), bqm)
exp2 = SampleSet.from_samples_bqm(dict(zip(nodes, [0, 1, 1, 0])), bqm)
icm = IsoenergeticClusterMove(seed=None)
inp = States(s1, s2)
exp = [exp1, exp2]
# split between [exp1, exp2] and [exp2, exp1] as output samples
# should be ~50%
cnt = 0
n = 100
for _ in range(n):
res = icm.run(inp).result()
r1, r2 = pluck(res, 'samples')
# test responses are valid
self.assertIn(r1, exp)
self.assertIn(r2, exp)
# verify total samples energy doesn't change after ICM
self.assertEqual(self.total_energy(inp), self.total_energy(res))
# count responses
if r1 == exp1 and r2 == exp2:
cnt += 1
self.assertLess(cnt, 0.75 * n)
self.assertGreater(cnt, 0.25 * n)
def test_basic(self):
bqm = dimod.BinaryQuadraticModel({}, {'ab': 1, 'bc': -1, 'ca': 1}, 0, dimod.SPIN)
state = State.from_sample(min_sample(bqm), bqm)
antistate = State.from_sample(max_sample(bqm), bqm)
result = GreedyPathMerge().run(States(state, antistate)).result()
self.assertEqual(result.samples.first.energy, -3.0)
def test_unstructured_runnable(self):
initial = State(val=10)
states = States(State(val=2), State(val=3))
multiply = Lambda(next=lambda self, s: s[0].updated(val=s[0].val * s[1].val))
result = Reduce(multiply, initial_state=initial).run(states).result()
self.assertEqual(result.val, 10*2*3)
def test_validated(self):
class Validated(traits.SISO, Runnable):
def next(self, state):
return state
with self.assertRaises(traits.StateDimensionalityError):
Validated().run(States()).result()
self.assertEqual(Validated().run(State(x=1)).result().x, 1)
def test_single(self):
bqm = dimod.BinaryQuadraticModel({}, {'ab': 1}, 0, dimod.SPIN)
states = States(State.from_sample({'a': 1, 'b': -1}, bqm))
state = MergeSamples().run(states).result()
self.assertEqual(state, states[0])
def test_siso(self):
class ValidSISO(traits.SISO, Runnable):
def next(self, state):
return state
with self.assertRaises(traits.StateDimensionalityError):
ValidSISO().run(States()).result()
self.assertEqual(ValidSISO().run(State(x=1)).result().x, 1)
class InvalidSISO(traits.SISO, Runnable):
def next(self, state):
# should return a single State()
return States(state, state)
with self.assertRaises(traits.StateDimensionalityError):
InvalidSISO().run(State()).result()
InvalidSISO().run(States()).result()
def test_invalid_mimo(self):
class InvalidMIMO(traits.MIMO, Runnable):
def next(self, states):
# should return States()
return State()
with self.assertRaises(traits.StateDimensionalityError):
InvalidMIMO().run(States()).result()
def test_default_fold(self):
bqm = dimod.BinaryQuadraticModel({'a': 1}, {}, 0, dimod.SPIN)
states = States(
State.from_sample(min_sample(bqm), bqm), # energy: -1
State.from_sample(max_sample(bqm), bqm), # energy: +1
)
best = ArgMin().run(states).result()
self.assertEqual(best.samples.first.energy, -1)
def test_custom_fold(self):
bqm = dimod.BinaryQuadraticModel({'a': 1}, {}, 0, dimod.SPIN)
states = States(
State.from_sample(min_sample(bqm), bqm), # energy: -1
State.from_sample(max_sample(bqm), bqm), # energy: +1
)
fold = ArgMin(key=lambda s: -s.samples.first.energy)
best = fold.run(states).result()
self.assertEqual(best.samples.first.energy, 1)
def next(self, states):
"""ICM between two first samples in the first two input states."""
if len(states) > 2:
raise ValueError("exactly two input states required")
inp1, inp2 = states
bqm = inp1.problem
ss1 = inp1.samples.change_vartype(dimod.BINARY, inplace=False)
ss2 = inp2.samples.change_vartype(dimod.BINARY, inplace=False)
# sanity check: we operate on the same set of variables
if ss1.variables ^ ss2.variables:
raise ValueError(
"input samples not over the same set of variables")
# reorder variables, if necessary
# (use sequence comparison, not set)
variables = list(ss1.variables)
if ss2.variables != variables:
reorder = [ss2.variables.index(v) for v in variables]
record = ss2.record[:, reorder]
ss2 = dimod.SampleSet(record, variables, ss2.info, ss2.vartype)
# samples' symmetric difference (XOR)
# (form clusters of input variables with opposite values)
sample1 = ss1.record.sample[0]
sample2 = ss2.record.sample[0]
symdiff = sample1 ^ sample2
# for cluster detection we'll use a reduced problem graph
graph = bqm.to_networkx_graph()
# note: instead of numpy mask indexing of `notcluster`, we enumerate
# non-cluster variables manually to avoid conversion of potentially
# unhashable variable names to numpy types
notcluster = [v for v, d in zip(variables, symdiff) if d == 0]
graph.remove_nodes_from(notcluster)
# pick a random variable that belongs to a cluster, then select the cluster
node = self.random.choice(list(graph.nodes))
cluster = nx.node_connected_component(graph, node)
# flip variables from `cluster` in both input samples
flipper = np.array([1 if v in cluster else 0 for v in variables])
ss1.record.sample[0] ^= flipper
ss2.record.sample[0] ^= flipper
# change vartype back to input's type
ss1.change_vartype(inp1.samples.vartype)
ss2.change_vartype(inp2.samples.vartype)
# update sampleset's energies
ss1.record.energy = bqm.energies(ss1)
ss2.record.energy = bqm.energies(ss2)
return States(inp1.updated(samples=ss1), inp2.updated(samples=ss2))
def test_map(self):
class A(Runnable, traits.ProblemDecomposer):
def next(self, state):
return state.updated(subproblem=state.problem)
a = A()
m = Map(a)
r = m.run(States(State(problem=1))).result()
self.assertSetEqual(m.inputs, a.inputs)
self.assertSetEqual(m.outputs, a.outputs)
def next(self, states, **runopts):
self._futures = [self.runnable.run(state, **runopts) for state in states]
logger.debug("{} running {!r} on {} input states".format(
self.name, self.runnable, len(states)))
concurrent.futures.wait(self._futures,
return_when=concurrent.futures.ALL_COMPLETED)
return States(*(f.result() for f in self._futures))
def test_branch(self):
class Inc(Runnable):
def next(self, state):
return state.updated(cnt=state.cnt + 1)
states = States(State(cnt=1), State(cnt=2))
branch = Map(Inc()) | ArgMin('cnt')
result = branch.run(states).result()
self.assertEqual(result.cnt, states[0].cnt + 1)
def test_isolated(self):
class Inc(Runnable):
def next(self, state):
return state.updated(cnt=state.cnt + 1)
states = States(State(cnt=1), State(cnt=2))
result = Map(Inc()).run(states).result()
self.assertEqual(result[0].cnt, states[0].cnt + 1)
self.assertEqual(result[1].cnt, states[1].cnt + 1)
def test_mimo(self):
"""Parallel should support branches with States inputs"""
inp = States(State(x=1), State(x=2))
wrk = ParallelBranches(Identity(), Identity(), Identity())
out = wrk.run(inp).result()
self.assertEqual(len(out), 3)
self.assertIsInstance(out, States)
self.assertEqual(out.first, inp)
def next(self, states, **runopts):
futures = [
branch.run(state.updated(), **runopts)
for branch, state in zip(self.branches, states)
]
logger.debug("{} running {} branches in parallel".format(
self.name, len(futures)))
# wait for all branches to finish
concurrent.futures.wait(futures,
return_when=concurrent.futures.ALL_COMPLETED)
# collect resolved states (in original order, not completion order)
states = States()
for f in futures:
states.append(f.result())
return states
