def setUp(self):
# mock client
self.client = Client(endpoint='e',
token='t',
solver=dict(name__contains='test'))
self.client._fetch_solvers = lambda **kw: self.solvers
self.client._submit = lambda *pa, **kw: None
self.client.upload_problem_encoded = lambda *pa, **kw: Present(
result=mock_problem_id)
# mock solvers
self.structured_solver = StructuredSolver(
client=self.client,
data={
"properties": {
"supported_problem_types": ["qubo", "ising"],
"qubits": [0, 1, 2],
"couplers": [[0, 1], [0, 2], [1, 2]],
"num_qubits": 3,
"num_reads_range": [0, 100],
"parameters": {
"num_reads": "Number of samples to return.",
"postprocess": "either 'sampling' or 'optimization'"
},
"topology": {
"type": "chimera",
"shape": [16, 16, 4]
},
"category": "qpu",
"tags": ["lower_noise"]
},
"id": "test-qpu-solver",
"description": "A test solver 1",
"status": "online"
})
self.unstructured_solver = UnstructuredSolver(
client=self.client,
data={
"properties": {
"supported_problem_types": ["bqm"],
"parameters": {
"num_reads": "Number of samples to return."
},
"category": "hybrid",
},
"id": "test-unstructured-solver",
"description": "A test unstructured solver"
})
self.solvers = [self.structured_solver]
# we can't use unstructured solvers without dimod installed,
# so don't even try testing it
if dimod:
self.solvers.append(self.unstructured_solver)
# reset all event handlers
from dwave.cloud.events import _client_event_hooks_registry as reg
reg.update({k: [] for k in reg})
def _sample(self, type_, linear, quadratic, offset, params,
label=None, undirected_biases=False):
"""Internal method for `sample_ising`, `sample_qubo` and `sample_bqm`.
Args:
linear (list/dict):
Linear terms of the model.
quadratic (dict[(int, int), float]):
Quadratic terms of the model.
offset (number):
Constant offset applied to the model.
params (dict):
Parameters for the sampling method, solver-specific.
label (str, optional):
Problem label.
undirected_biases (boolean, default=False):
Are (quadratic) biases specified on undirected edges? For
triangular or symmetric matrix of quadratic biases set it to
``True``.
Returns:
:class:`~dwave.cloud.computation.Future`
"""
# Check the problem
if not self.check_problem(linear, quadratic):
raise ProblemStructureError(
f"Problem graph incompatible with {self.id} solver")
# Mix the new parameters with the default parameters
combined_params = dict(self._params)
combined_params.update(params)
# Check the parameters before submitting
for key in combined_params:
if key not in self.parameters and not key.startswith('x_'):
raise KeyError("{} is not a parameter of this solver.".format(key))
# transform some of the parameters in-place
self._format_params(type_, combined_params)
body_dict = {
'solver': self.id,
'data': encode_problem_as_qp(self, linear, quadratic, offset,
undirected_biases=undirected_biases),
'type': type_,
'params': combined_params
}
if label is not None:
body_dict['label'] = label
body_data = json.dumps(body_dict)
logger.trace("Encoded sample request: %s", body_data)
body = Present(result=body_data)
computation = Future(solver=self, id_=None, return_matrix=self.return_matrix)
# XXX: offset is carried on Future until implemented in SAPI
computation._offset = offset
logger.debug("Submitting new problem to: %s", self.id)
self.client._submit(body, computation)
return computation
def mock_upload(self, bqm):
return Present(result=mock_problem_id)
def mock_upload(self, bqm):
return Present(exception=mock_upload_exc)
def _sample(self,
type_,
linear,
quadratic,
params,
undirected_biases=False):
"""Internal method for `sample_ising`, `sample_qubo` and `sample_bqm`.
Args:
linear (list/dict):
Linear terms of the model.
quadratic (dict[(int, int), float]):
Quadratic terms of the model.
params (dict):
Parameters for the sampling method, solver-specific.
undirected_biases (boolean, default=False):
Are (quadratic) biases specified on undirected edges? For
triangular or symmetric matrix of quadratic biases set it to
``True``.
Returns:
:class:`Future`
"""
args = dict(type_=type_,
linear=linear,
quadratic=quadratic,
params=params)
dispatch_event('before_sample', obj=self, args=args)
# Check the problem
if not self.check_problem(linear, quadratic):
raise InvalidProblemError(
"Problem graph incompatible with solver.")
# Mix the new parameters with the default parameters
combined_params = dict(self._params)
combined_params.update(params)
# Check the parameters before submitting
for key in combined_params:
if key not in self.parameters and not key.startswith('x_'):
raise KeyError(
"{} is not a parameter of this solver.".format(key))
# transform some of the parameters in-place
self._format_params(type_, combined_params)
body_data = json.dumps({
'solver':
self.id,
'data':
encode_problem_as_qp(self,
linear,
quadratic,
undirected_biases=undirected_biases),
'type':
type_,
'params':
combined_params
})
logger.trace("Encoded sample request: %s", body_data)
body = Present(result=body_data)
computation = Future(solver=self,
id_=None,
return_matrix=self.return_matrix)
logger.debug("Submitting new problem to: %s", self.id)
self.client._submit(body, computation)
dispatch_event('after_sample',
obj=self,
args=args,
return_value=computation)
return computation
