# Copyright 2017 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.
"""
Computation manages the interactions between your code and a :term:`solver`, which
manages interactions between the remote resource and your submitted problems.
Your solver instantiates a :class:`Future` object for its calls, via D-Wave Sampler API
(SAPI) servers, to the remote resource.
You can interact through the :class:`Future` object with pending (running) or completed
computation---sampling on a QPU or software solver---executed remotely, monitoring problem status,
waiting for and retrieving results, cancelling enqueued jobs, etc.
Some :class:`Future` methods are blocking.
"""
import time
import threading
import functools
import warnings
from operator import itemgetter
from dateutil.parser import parse
from concurrent.futures import TimeoutError
from dwave.cloud.utils import (
utcnow, datetime_to_timestamp, aliasdict, deprecated)
from dwave.cloud.exceptions import InvalidAPIResponseError
# Use numpy if available for fast decoding
try:
import numpy as np
_numpy = True
except ImportError:
_numpy = False
__all__ = ['Future']
[docs]@functools.total_ordering
class Future(object):
"""Class for interacting with jobs submitted to SAPI.
:class:`~dwave.cloud.solver.Solver` uses :class:`Future` to construct
objects for pending SAPI calls that can wait for requests to complete and
parse returned messages.
Objects are blocked for the duration of any data accessed on the remote
resource.
Warning:
:class:`Future` objects are not intended to be directly
created. Problem submittal is initiated by one of the solvers in
:mod:`~dwave.cloud.solver` module and executed by one of the clients.
Args:
solver (:class:`~dwave.cloud.solver.Solver`):
Solver responsible for this :class:`Future` object.
id_ (str, optional, default=None):
Identification for a query submitted by a solver to SAPI. May be
None following submission until an identification number is set.
return_matrix (bool, optional, default=False):
Return values for this :class:`Future` object are NumPy matrices.
Examples:
This example creates a solver using the local system's default D-Wave
Cloud Client configuration file, submits a simple QUBO problem to a
remote D-Wave resource for 100 samples, and checks a couple of times
whether the sampling is completed.
>>> from dwave.cloud import Client
>>> client = Client.from_config() # doctest: +SKIP
>>> solver = client.get_solver() # doctest: +SKIP
>>> u, v = next(iter(solver.edges)) # doctest: +SKIP
>>> Q = {(u, u): -1, (u, v): 0, (v, u): 2, (v, v): -1} # doctest: +SKIP
>>> computation = solver.sample_qubo(Q, num_reads=100) # doctest: +SKIP
>>> computation.done() # doctest: +SKIP
False
>>> computation.id # doctest: +SKIP
'1cefeb6d-ebd5-4592-87c0-4cc43ec03e27'
>>> computation.done() # doctest: +SKIP
True
>>> client.close() # doctest: +SKIP
"""
def __init__(self, solver, id_, return_matrix=False):
self.solver = solver
# Has the client tried to cancel this job
self._cancel_requested = False
self._cancel_sent = False
self._single_cancel_lock = threading.Lock() # Make sure we only call cancel once
# ID readiness notification
self._id_ready_event = threading.Event()
# Should the results be decoded as python lists or numpy matrices
if return_matrix and not _numpy:
raise ValueError("Matrix result requested without numpy.")
self.return_matrix = return_matrix
#: The id the server will use to identify this problem, None until the id is actually known
self.id = id_
#: Problem label, as (optionally) set on submission. None until parsed from a response.
self.label = None
#: `datetime` the Future was created (immediately before enqueued in Client's submit queue)
self.time_created = utcnow()
#: `datetime` corresponding to the time when the problem was accepted by the server (None before then)
self.time_received = None
#: `datetime` corresponding to the time when the problem was completed by the server (None before then)
self.time_solved = None
#: `datetime` the Future was resolved (marked as done; succeeded or failed), or None before then
self.time_resolved = None
# estimated `earliest_completion_time` as returned on problem submit
self.eta_min = None
# estimated `latest_completion_time` as returned on problem submit
self.eta_max = None
# Track how long it took us to parse the data
self.parse_time = None
# approx. server-client clocks difference in seconds
self.clock_diff = None
# Data from the server before it is parsed
self._message = None
#: Status flag most recently returned by the server
self.remote_status = None
# Data from the server after it's parsed
self._result = None
self._exception = None
# Event(s) to signal when the results are ready
self._results_ready_event = threading.Event()
self._other_events = []
# current poll back-off interval, in seconds
self._poll_backoff = None
# XXX: energy offset carried via Future, until implemented in SAPI
self._offset = 0
# TODO: remove in 0.9.0
@property
def error(self):
"""Deprecated in favor of :meth:`.exception`.
Scheduled for removal in 0.9.0.
"""
warnings.warn(
"'Future.error' is deprecated, and it will be removed "
"in 0.9.0. please convert your code to use 'Future.exception()'",
DeprecationWarning)
return self._exception
# make Future ordered
def __lt__(self, other):
return id(self) < id(other)
def __eq__(self, other):
return self is other
def __hash__(self):
return id(self)
def _set_message(self, message):
"""Complete the future with a message from the server.
The message from the server may actually be an error.
Args:
message (dict):
Data from the server from trying to complete query.
"""
self._message = message
self._signal_ready()
def _set_exception(self, exception):
"""Complete the future with an exception.
Args:
exception (Exception):
Exception that caused the failure.
"""
self._exception = exception
self._signal_ready()
def _signal_ready(self):
"""Signal all the events waiting on this future."""
self.time_resolved = utcnow()
self._results_ready_event.set()
[ev.set() for ev in self._other_events]
def _add_event(self, event):
"""Add an event to be signaled after this event completes."""
self._other_events.append(event)
if self.done():
event.set()
def _remove_event(self, event):
"""Remove a completion event from this future."""
self._other_events.remove(event)
def _set_clock_diff(self, server_response, localtime_of_response):
"""Calculate and set the `.clock_diff`, based on headers from a server
response, and the local time of response received.
Based on `clock_diff`, `eta_min`/`eta_max` may or may not make sense.
"""
try:
server_time = datetime_to_timestamp(parse(server_response.headers['date']))
except:
server_time = 0
self.clock_diff = abs(server_time - localtime_of_response)
[docs] @staticmethod
def wait_multiple(futures, min_done=None, timeout=None):
"""Wait for multiple :class:`Future` objects to complete.
Blocking call that uses an event object to emulate multi-wait for Python.
Args:
futures (list of Futures):
List of :class:`Future` objects to await.
min_done (int, optional, default=None):
Minimum required completions to end the waiting. The wait is
terminated when this number of results are ready. If None, waits
for all the :class:`Future` objects to complete.
timeout (float, optional, default=None):
Maximum number of seconds to await completion. If None, waits
indefinitely.
Returns:
Two-tuple of :class:`Future` objects: completed and not completed
submitted tasks. Similar to `concurrent.futures.wait()` method's
returned two-tuple of `done` and `not_done` sets.
See Also:
:func:`as_completed` for a blocking iterable of resolved futures
similar to `concurrent.futures.as_completed()` method.
Examples:
This example creates a solver using the local system's default
D-Wave Cloud Client configuration file, submits a simple QUBO
problem to a remote D-Wave resource 3 times for differing numers of
samples, and waits for sampling to complete on any two of the
submissions. The wait ends with the completion of two submissions
while the third is still in progress. (A more typical approach would
use something like
:code:`first = next(Future.as_completed(computation))` instead.)
>>> import dwave.cloud as dc
>>> client = dc.Client.from_config() # doctest: +SKIP
>>> solver = client.get_solver() # doctest: +SKIP
>>> u, v = next(iter(solver.edges)) # doctest: +SKIP
>>> Q = {(u, u): -1, (u, v): 0, (v, u): 2, (v, v): -1} # doctest: +SKIP
>>> computation = [solver.sample_qubo(Q, num_reads=1000),
... solver.sample_qubo(Q, num_reads=50),
... solver.sample_qubo(Q, num_reads=10)] # doctest: +SKIP
>>> dc.computation.Future.wait_multiple(computation, min_done=1) # doctest: +SKIP
([<dwave.cloud.computation.Future at 0x17dde518>,
<dwave.cloud.computation.Future at 0x17ddee80>],
[<dwave.cloud.computation.Future at 0x15078080>])
>>> print(computation[0].done()) # doctest: +SKIP
False
>>> print(computation[1].done()) # doctest: +SKIP
True
>>> print(computation[2].done()) # doctest: +SKIP
True
>>> client.close() # doctest: +SKIP
"""
if min_done is None:
min_done = len(futures)
if timeout is None:
timeout = float('inf')
# Track the exit conditions
finish = time.time() + timeout
done = []
# Keep track of what futures haven't finished
remaining = list(futures)
# Insert our event into all the futures
event = threading.Event()
[f._add_event(event) for f in remaining]
# Check the exit conditions
while len(done) < min_done and finish > time.time():
# Prepare to wait on any of the jobs finishing
event.clear()
# Check if any of the jobs have finished. After the clear just in
# case one finished and we erased the signal it by calling clear above
finished_futures = {f for f in remaining if f.done()}
if len(finished_futures) > 0:
# If we did make a mistake reseting the event, undo that now
# so that we double check the finished list before a wait blocks
event.set()
# Update our exit conditions
done.extend(finished_futures)
remaining = [f for f in remaining if f not in finished_futures]
continue
# Block on any of the jobs finishing
wait_time = finish - time.time() if abs(finish) != float('inf') else None
event.wait(wait_time)
# Clean up after ourselves
[f._remove_event(event) for f in futures]
return done, remaining
[docs] @staticmethod
def as_completed(fs, timeout=None):
"""Yield Futures objects as they complete.
Returns an iterator over the specified list of :class:`Future` objects
that yields those objects as they complete. Completion occurs when the
submitted job is finished or cancelled.
Emulates the behavior of the `concurrent.futures.as_completed()`
function.
Args:
fs (list):
List of :class:`Future` objects to iterate over.
timeout (float, optional, default=None):
Maximum number of seconds to await completion. If None, awaits
indefinitely.
Returns:
Generator (:class:`Future` objects):
Listed :class:`Future` objects as they complete.
Raises:
`concurrent.futures.TimeoutError` is raised if per-future timeout is
exceeded.
Examples:
This example creates a solver using the local system's default D-Wave
Cloud Client configuration file, submits a simple QUBO problem to a
remote D-Wave resource 3 times for differing numers of samples, and
yields timing information for each job as it completes.
>>> import dwave.cloud as dc
>>> client = dc.Client.from_config() # doctest: +SKIP
>>> solver = client.get_solver() # doctest: +SKIP
>>> u, v = next(iter(solver.edges)) # doctest: +SKIP
>>> Q = {(u, u): -1, (u, v): 0, (v, u): 2, (v, v): -1} # doctest: +SKIP
>>> computation = [solver.sample_qubo(Q, num_reads=1000),
... solver.sample_qubo(Q, num_reads=50),
... solver.sample_qubo(Q, num_reads=10)] # doctest: +SKIP
>>> for tasks in dc.computation.Future.as_completed(computation, timeout=10)
... print(tasks.timing) # doctest: +SKIP
...
{'total_real_time': 17318, ... 'qpu_readout_time_per_sample': 123}
{'total_real_time': 10816, ... 'qpu_readout_time_per_sample': 123}
{'total_real_time': 26285, ... 'qpu_readout_time_per_sample': 123}
...
>>> client.close() # doctest: +SKIP
"""
not_done = fs
while not_done:
done, not_done = Future.wait_multiple(not_done, min_done=1, timeout=timeout)
if not done:
raise TimeoutError
for f in done:
yield f
[docs] def wait(self, timeout=None):
"""Wait for the solver to receive a response for a submitted problem.
Blocking call that waits for a :class:`Future` object to complete.
Args:
timeout (float, optional, default=None):
Maximum number of seconds to await completion. If None, waits
indefinitely.
Returns:
Boolean: True if solver received a response.
Examples:
This example creates a solver using the local system's default
D-Wave Cloud Client configuration file, submits a simple QUBO
problem to a remote D-Wave resource for 100 samples, and tries
waiting for 10 seconds for sampling to complete.
>>> from dwave.cloud import Client
>>> client = Client.from_config() # doctest: +SKIP
>>> solver = client.get_solver() # doctest: +SKIP
>>> u, v = next(iter(solver.edges)) # doctest: +SKIP
>>> Q = {(u, u): -1, (u, v): 0, (v, u): 2, (v, v): -1} # doctest: +SKIP
>>> computation = solver.sample_qubo(Q, num_reads=100) # doctest: +SKIP
>>> computation.wait(timeout=10) # doctest: +SKIP
False
>>> computation.remote_status # doctest: +SKIP
'IN_PROGRESS'
>>> computation.wait(timeout=10) # doctest: +SKIP
True
>>> computation.remote_status # doctest: +SKIP
'COMPLETED'
>>> client.close() # doctest: +SKIP
"""
return self._results_ready_event.wait(timeout)
[docs] def done(self):
"""Check whether the solver received a response for a submitted problem.
Non-blocking call that checks whether the solver has received a response
from the remote resource.
Returns:
Boolean: True if solver received a response.
Examples:
This example creates a solver using the local system's default
D-Wave Cloud Client configuration file, submits a simple QUBO
problem to a remote D-Wave resource for 100 samples, and checks a
couple of times whether sampling is completed.
>>> from dwave.cloud import Client
>>> client = Client.from_config() # doctest: +SKIP
>>> solver = client.get_solver() # doctest: +SKIP
>>> u, v = next(iter(solver.edges)) # doctest: +SKIP
>>> Q = {(u, u): -1, (u, v): 0, (v, u): 2, (v, v): -1} # doctest: +SKIP
>>> computation = solver.sample_qubo(Q, num_reads=100) # doctest: +SKIP
>>> computation.done() # doctest: +SKIP
False
>>> computation.done() # doctest: +SKIP
True
>>> client.close() # doctest: +SKIP
"""
return self._results_ready_event.is_set()
[docs] def cancel(self):
"""Try to cancel the problem corresponding to this result.
Non-blocking call to the remote resource in a best-effort attempt to
prevent execution of a problem.
Examples:
This example creates a solver using the local system's default
D-Wave Cloud Client configuration file, submits a simple QUBO
problem to a remote D-Wave resource for 100 samples, and tries
(and in this case succeeds) to cancel it.
>>> from dwave.cloud import Client
>>> client = Client.from_config() # doctest: +SKIP
>>> solver = client.get_solver() # doctest: +SKIP
>>> u, v = next(iter(solver.edges)) # doctest: +SKIP
>>> Q = {(u, u): -1, (u, v): 0, (v, u): 2, (v, v): -1} # doctest: +SKIP
>>> computation = solver.sample_qubo(Q, num_reads=100) # doctest: +SKIP
>>> computation.cancel() # doctest: +SKIP
>>> computation.done() # doctest: +SKIP
True
>>> computation.remote_status # doctest: +SKIP
'CANCELLED'
>>> client.close() # doctest: +SKIP
"""
# Don't need to cancel something already finished
if self.done():
return
with self._single_cancel_lock:
# Already done
if self._cancel_requested:
return
# Set the cancel flag
self._cancel_requested = True
# The cancel request will be sent here, or by the solver when it
# gets a status update for this problem (in the case where the id hasn't been set yet)
if self.id is not None and not self._cancel_sent:
self._cancel_sent = True
self.solver.client._cancel(self.id, self)
[docs] def wait_id(self, timeout=None):
"""Blocking id getter.
Return the submitted problem ID, but unlike :meth:`.id`, block until the
ID becomes known, or until `timeout` expires.
Args:
timeout (float, default=None):
Timeout in seconds. By default, wait indefinitely for problem
id to become known/available.
Returns:
str:
Problem ID, as returned by SAPI.
Raises:
:exc:`concurrent.futures.TimeoutError`:
When `timeout` exceeded, and problem id not ready.
"""
if not self._id_ready_event.wait(timeout=timeout):
raise TimeoutError("problem id not available yet")
return self._id
@property
def id(self):
"""Simple non-blocking id getter for backward compat."""
return self._id
@id.setter
def id(self, value):
"""Sets the problem ID, notifying the related event."""
self._id = value
# notify ID is set/ready
if value is not None:
self._id_ready_event.set()
[docs] def result(self):
"""Results for a submitted job.
Retrives raw result data in a :class:`Future` object that the solver
submitted to a remote resource. First calls to access this data are
blocking.
Returns:
dict: Results of the submitted job. Should be considered read-only.
Note:
Helper properties on :class:`Future` object are preferred to reading
raw results, as they abstract away the differences in response
between some solvers like. Available methods are: :meth:`samples`,
:meth:`energies`, :meth:`occurrences`, :meth:`variables`,
:meth:`timing`, :meth:`problem_type`, :meth:`sampleset` (only if
dimod package is installed).
Warning:
The dictionary returned by :meth:`result` depends on the solver
used. Starting with version 0.7.0 we will not try to standardize
them anymore, on client side. For QPU solvers, please replace
`'samples'` with `'solutions'` and `'occurrences'` with
`'num_occurrences'`. Better yet, use :meth:`Future.samples` and
:meth:`Future.num_occurrences` instead.
Examples:
This example creates a solver using the local system's default
D-Wave Cloud Client configuration file, submits a simple QUBO
problem (representing a Boolean NOT gate by a penalty function)
to a remote D-Wave resource for 5 samples, and prints part
of the returned result (the relevant samples).
>>> from dwave.cloud import Client
>>> with Client.from_config() as client: # doctest: +SKIP
... solver = client.get_solver()
... u, v = next(iter(solver.edges))
... Q = {(u, u): -1, (u, v): 0, (v, u): 2, (v, v): -1}
... computation = solver.sample_qubo(Q, num_reads=5)
... for i in range(5):
... result = computation.result()
... print(result['solutions'][i][u], result['solutions'][i][v])
...
...
(0, 1)
(1, 0)
(1, 0)
(0, 1)
(0, 1)
"""
self._load_result()
return self._result
[docs] def exception(self):
if self._exception is not None:
raise self._exception
@property
def energies(self):
"""Energy buffer for the submitted job.
First calls to access data of a :class:`Future` object are blocking;
subsequent access to this property is non-blocking.
Returns:
list or NumPy matrix of doubles: Energies for each set of samples.
Examples:
This example creates a solver using the local system's default
D-Wave Cloud Client configuration file, submits a random Ising
problem (+1 or -1 values of linear and quadratic biases on all nodes
and edges, respectively, of the solver's garph) to a remote D-Wave
resource for 10 samples, and prints the returned energies.
>>> import random
>>> from dwave.cloud import Client
>>> with Client.from_config() as client: # doctest: +SKIP
... solver = client.get_solver()
... linear = {index: random.choice([-1, 1]) for index in solver.nodes}
... quad = {key: random.choice([-1, 1]) for key in solver.undirected_edges}
... computation = solver.sample_ising(linear, quad, num_reads=10)
... print(computation.energies)
...
[-3976.0, -3974.0, -3972.0, -3970.0, -3968.0, -3968.0, -3966.0,
-3964.0, -3964.0, -3960.0]
"""
# return energies from sampleset, if already constructed
result = self.result()
if 'sampleset' in result:
return result['sampleset'].record.energy
# fallback to energies from response
return result['energies']
@property
def samples(self):
"""State buffer for the submitted job.
First calls to access data of a :class:`Future` object are blocking;
subsequent access to this property is non-blocking.
Returns:
list of lists or NumPy matrix: Samples on the nodes of solver's graph.
Examples:
This example creates a solver using the local system's default
D-Wave Cloud Client configuration file, submits a simple QUBO
problem (representing a Boolean NOT gate by a penalty function) to a
remote D-Wave resource for 5 samples, and prints part of the
returned result (the relevant samples).
>>> from dwave.cloud import Client
>>> with Client.from_config() as client: # doctest: +SKIP
... solver = client.get_solver()
... u, v = next(iter(solver.edges))
... Q = {(u, u): -1, (u, v): 0, (v, u): 2, (v, v): -1}
... computation = solver.sample_qubo(Q, num_reads=5)
... for i in range(5):
... print(computation.samples[i][u], computation.samples[i][v])
...
...
(1, 0)
(0, 1)
(0, 1)
(1, 0)
(0, 1)
"""
# return samples from sampleset, if already constructed
result = self.result()
if 'sampleset' in result:
return result['sampleset'].record.sample
# fallback to samples from response
return result['solutions']
@property
def variables(self):
"""List of active variables in response/answer."""
result = self.result()
if 'active_variables' in result:
return result['active_variables']
if 'sampleset' in result:
return result['sampleset'].variables
raise InvalidAPIResponseError("Active variables not present in the response")
@property
def num_occurrences(self):
"""Number of sample occurrences buffer for the submitted job.
First calls to access data of a :class:`Future` object are blocking;
subsequent access to this property is non-blocking.
Returns:
list or NumPy matrix of doubles: number of occurrences. When
returned results are ordered in a histogram, `num_occurrences`
indicates the number of times a particular solution recurred.
Examples:
This example creates a solver using the local system's default
D-Wave Cloud Client configuration file, submits a simple Ising
problem with several ground states to a remote D-Wave resource for
20 samples, and prints the returned results, which are ordered as a
histogram. The problem's ground states tend to recur frequently,
and so those solutions have `occurrences` greater than 1.
>>> from dwave.cloud import Client
>>> with Client.from_config() as client: # doctest: +SKIP
... solver = client.get_solver()
... quad = {(16, 20): -1, (17, 20): 1, (16, 21): 1, (17, 21): 1}
... computation = solver.sample_ising({}, quad, num_reads=500, answer_mode='histogram')
... for i in range(len(computation.num_occurrences)):
... print(computation.samples[i][16], computation.samples[i][17],
... computation.samples[i][20], computation.samples[i][21],
... ' --> ', computation.energies[i], computation.num_occurrences[i])
...
(-1, 1, -1, -1, ' --> ', -2.0, 41)
(-1, -1, -1, 1, ' --> ', -2.0, 53)
(1, -1, 1, 1, ' --> ', -2.0, 55)
(1, 1, -1, -1, ' --> ', -2.0, 52)
(1, 1, 1, -1, ' --> ', -2.0, 60)
(1, -1, 1, -1, ' --> ', -2.0, 196)
(-1, 1, -1, 1, ' --> ', -2.0, 15)
(-1, -1, 1, 1, ' --> ', -2.0, 28)
"""
# return num_occurrences from sampleset, if already constructed
result = self.result()
if 'sampleset' in result:
return result['sampleset'].record.num_occurrences
# fallback to num_occurrences from response
# (but `occurrences` data is not present if `answer_mode` was set to "raw")
if 'num_occurrences' in result:
return result['num_occurrences']
elif self.return_matrix:
return np.ones((len(result['solutions']),))
else:
return [1] * len(result['solutions'])
# TODO: remove in 0.10.0+
@property
def occurrences(self):
"""Deprecated in favor of Future.num_occurrences property.
Scheduled for removal in 0.10.0.
"""
warnings.warn(
"'Future.occurrences' is deprecated, and it will be removed "
"in 0.10.0+. Please convert your code to use 'Future.num_occurrences'",
DeprecationWarning)
return self.num_occurrences
[docs] def wait_sampleset(self):
"""Blocking sampleset getter."""
# blocking result get
result = self._load_result()
# common problem info: id/label
problem_info = dict(problem_id=self.id)
if self.label is not None:
problem_info.update(problem_label=self.label)
# sapi returned sampleset directly
if 'sampleset' in result:
sampleset = result['sampleset']
sampleset.info.update(problem_info)
return sampleset
# construct sampleset from available data
try:
import dimod
except ImportError:
raise RuntimeError("Can't construct SampleSet without dimod. "
"Re-install the library with 'bqm' support.")
# filter inactive variables from samples
variables = self.variables
samples = [[sample[v] for v in variables] for sample in self.samples]
# infer vartype from problem type
# note: KeyError on unknown problem types. BQM/DQM should be handled above.
vartype_from_problem_type = {'ising': 'SPIN', 'qubo': 'BINARY'}
vartype = vartype_from_problem_type[self.problem_type]
# for QPU jobs, info field is blank; add timing info
info = dict(timing=self.timing)
info.update(problem_info)
sampleset = dimod.SampleSet.from_samples(
(samples, variables), vartype=vartype,
energy=self.energies, num_occurrences=self.num_occurrences,
info=info, sort_labels=True)
# this means that samplesets retrieved BEFORE this function are called
# are not the same object as after, but it is a simpler implementation
self._result['sampleset'] = self._sampleset = sampleset
return sampleset
@property
def sampleset(self):
"""Return :class:`~dimod.SampleSet` representation of the results."""
try:
return self._sampleset
except AttributeError:
pass
try:
import dimod
except ImportError:
raise RuntimeError("Can't construct SampleSet without dimod. "
"Re-install the library with 'bqm' support.")
self._sampleset = sampleset = dimod.SampleSet.from_future(
self, lambda f: f.wait_sampleset())
return sampleset
@property
def timing(self):
"""Timing information about a solver operation.
Mapping from string keys to numeric values representing timing details
for a submitted job as returned from the remote resource. Keys are
dependant on the particular solver.
First calls to access data of a :class:`Future` object are blocking;
subsequent access to this property is non-blocking.
Returns:
dict:
Mapping from string keys to numeric values representing timing
information.
Examples:
This example creates a client using the local system's default
D-Wave Cloud Client configuration file, which is configured to
access a D-Wave 2000Q QPU, submits a simple :term:`Ising` problem
(opposite linear biases on two coupled qubits) for 5 samples, and
prints timing information for the job.
>>> from dwave.cloud import Client
>>> with Client.from_config() as client: # doctest: +SKIP
... solver = client.get_solver()
... u, v = next(iter(solver.edges))
... computation = solver.sample_ising({u: -1, v: 1},{}, num_reads=5)
... print(computation.timing)
...
{'total_real_time': 10961, 'anneal_time_per_run': 20, ...}
"""
return self.result().get('timing', {})
@property
def problem_type(self):
"""Submitted problem type for this computation, as returned by the
solver API. Typical values are 'ising' and 'qubo'.
"""
return self.result()['problem_type']
def __getitem__(self, key):
"""Provide a simple results item getter. Blocks if future is unresolved.
Args:
key: keywords for result fields.
"""
self._load_result()
if key not in self._result:
raise KeyError('{} is not a property of response object'.format(key))
return self._result[key]
def _load_result(self):
"""Get the result, waiting and decoding as needed."""
if self._result is None:
# Wait for the query response
self.wait(timeout=None)
# Check for other error conditions
if self._exception is not None:
raise self._exception
# If someone else took care of this while we were waiting
if self._result is not None:
return self._result
# Prepare results from the response
self._decode()
self._alias_result()
return self._result
def _patch_offset(self):
# XXX: This is a temporary fix, until SAPI starts returning the offset
# in answer (for structured solvers only).
# It will patch `self._message` to include the offset as set in
# `self._offset`, but only if SAPI answer does not contain offset already.
msg = self._message
fmt = msg.get('answer', {}).get('format')
if fmt == 'qp':
if 'offset' not in msg['answer']:
msg['answer']['offset'] = self._offset
def _decode(self):
"""Decode answer data from the response."""
start = time.time()
self._patch_offset()
self._result = self.solver.decode_response(self._message)
self.parse_time = time.time() - start
return self._result
# TODO: schedule for removal
def _alias_result(self):
"""Alias `solutions` and `num_occurrences`.
Deprecated in version 0.8.0.
"""
if not self._result:
return
msg = "'{}' alias has been deprecated in favor of '{}'"
samples_msg = msg.format('samples', 'solutions')
occurrences_msg = msg.format('occurrences', 'num_occurrences')
aliases = dict(
samples=deprecated(samples_msg)(itemgetter('solutions')),
occurrences=deprecated(occurrences_msg)(itemgetter('num_occurrences')))
self._result = aliasdict(self._result)
self._result.alias(aliases)
return self._result