Source code for greedy.sampler

# Copyright 2019 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.

"""A dimod sampler that uses the steepest gradient descent."""

from numbers import Integral

import dimod
import numpy as np

from greedy.descent import steepest_gradient_descent

__all__ = ["SteepestDescentSolver", "SteepestDescentSampler"]


[docs]class SteepestDescentSolver(dimod.Sampler, dimod.Initialized): """Steepest descent solver/sampler for binary quadratic models. Steepest descent is the discrete analogue of gradient descent, but the best move is computed using a local minimization rather rather than computing a gradient. At each step, we determine the dimension along which to descend based on the highest energy drop caused by a variable flip. Solves convex problems to optimality. Number of downhill runs (samples produced) is determined by ``num_reads``, number of ``initial_states``, or a combination of the two, depending on the ``initial_states_generator``. For a given input model's graph :math:`G = (V, E)`, :math:`V` being a set of graph vertices and :math:`E` a set of edges, runtime complexity of the underlying C++ implementation is :math:`O(|E|)` for initialization phase and :math:`O(|V|)` per downhill step. In the ``large_sparse_opt`` mode, runtime complexity on sparse graphs is :math:`O(|V|*log|V|)` for initialization and :math:`O(max\_degree * log|V|)` per downhill step. Aliased as :class:`~greedy.sampler.SteepestDescentSampler`. Examples: Solve a simple Ising problem: >>> import greedy ... >>> sampler = greedy.SteepestDescentSampler() >>> samples = sampler.sample_ising({0: 2, 1: 2}, {(0, 1): -1}) ... >>> print(samples) 0 1 energy num_oc. 0 -1 -1 -5.0 1 ['SPIN', 1 rows, 1 samples, 2 variables] Post-processes samples generated by another sampler (simulated annealing in this example): .. code-block:: python import neal import dimod import greedy import networkx bqm = dimod.generators.ran_r(5, networkx.complete_graph('abc')) samples = neal.SimulatedAnnealingSampler().sample(bqm) postprocessed = greedy.SteepestDescentSampler().sample(bqm, initial_states=samples) For additional examples, see :meth:`.sample`. """ parameters = None """dict: Keyword arguments accepted by the sampling methods. Exactly equal to:: { 'num_reads': [], 'initial_states': [], 'initial_states_generator': ['initial_states_generators'], 'seed': [], 'large_sparse_opt': ['large_sparse_opt_values'], } """ properties = None """dict: Values for parameters accepted by the sampling methods. Exactly equal to:: { 'initial_states_generators': ('none', 'tile', 'random'), 'large_sparse_opt_values': (True, False), } """ def __init__(self): # create a copy (isolate from subclass) self.parameters = { 'num_reads': [], 'initial_states': [], 'initial_states_generator': ['initial_states_generators'], 'seed': [], 'large_sparse_opt': ['large_sparse_opt_values'], } self.properties = { 'initial_states_generators': ('none', 'tile', 'random'), 'large_sparse_opt_values': (True, False), }
[docs] def sample(self, bqm, num_reads=None, initial_states=None, initial_states_generator="random", seed=None, large_sparse_opt=False, **kwargs): """Sample from a binary quadratic model. Starts from ``initial_states``, and converges to local minima using discrete steepest-descent method. Args: bqm (:class:`~dimod.BinaryQuadraticModel`): The binary quadratic model to be sampled. num_reads (int, optional, default=len(initial_states) or 1): Number of reads. Each read is generated by one run of the steepest descent algorithm. If `num_reads` is not explicitly given, it is selected to match the number of initial states given. If initial states are not provided, only one read is performed. initial_states (samples-like, optional, default=None): One or more samples, each defining an initial state for all the problem variables. Initial states are given one per read, but if fewer than `num_reads` initial states are defined, additional values are generated as specified by `initial_states_generator`. See :func:`dimod.as_samples` for a description of "samples-like". initial_states_generator ({'none', 'tile', 'random'}, optional, default='random'): Defines the expansion of `initial_states` if fewer than `num_reads` are specified: * "none": If the number of initial states specified is smaller than `num_reads`, raises ValueError. * "tile": Reuses the specified initial states if fewer than `num_reads` or truncates if greater. * "random": Expands the specified initial states with randomly generated states if fewer than `num_reads` or truncates if greater. seed (int (32-bit unsigned integer), optional): Seed to use for the PRNG. Specifying a particular seed with a constant set of parameters produces identical results. If not provided, a random seed is chosen. large_sparse_opt (bool, optional, default=False): Use optimizations for large and sparse problems (search tree for next descent variable instead of linear array). Returns: :class:`dimod.SampleSet`: A `dimod` :class:`~dimod.SampleSet` object. Examples: This example samples a simple two-variable Ising model. >>> import dimod >>> bqm = dimod.BQM.from_ising({}, {'ab': 1}) ... >>> import greedy >>> sampler = greedy.SteepestDescentSampler() ... >>> samples = sampler.sample(bqm) >>> samples.first.energy -1.0 Run steepest descent one million times (takes ~150ms on an average laptop), converging to local minima, each time starting from a random state: >>> bqm = dimod.BQM.from_ising({}, {'ab': 1}) >>> sampler = greedy.SteepestDescentSampler() ... >>> samples = sampler.sample(bqm, num_reads=10**6) >>> print(samples.aggregate()) # doctest: +SKIP a b energy num_oc. 0 -1 +1 -1.0 499416 1 +1 -1 -1.0 500584 ['SPIN', 2 rows, 1000000 samples, 2 variables] Use a combination of one fixed initial state and two randomly generated ones to sample from a simple convex Ising problem with global minimum at ``(-1,-1)``: >>> bqm = dimod.BQM.from_ising({'x': 2, 'y': 2}, {'xy': -1}) >>> sampler = greedy.SteepestDescentSampler() ... >>> samples = sampler.sample(bqm, initial_states=([1, 1], 'xy'), num_reads=3) >>> print(samples.aggregate()) x y energy num_oc. 0 -1 -1 -5.0 3 ['SPIN', 1 rows, 3 samples, 2 variables] """ # get the original vartype so we can return consistently original_vartype = bqm.vartype # convert to spin if bqm.vartype is not dimod.SPIN: bqm = bqm.change_vartype(dimod.SPIN, inplace=False) # validate seed if not (seed is None or isinstance(seed, Integral)): raise TypeError("'seed' should be None or a positive 32-bit integer") if isinstance(seed, Integral) and not 0 <= seed <= 2**32 - 1: raise ValueError("'seed' should be an integer between 0 and 2**32 - 1 inclusive") # parse initial_states et al parsed_initial_states = self.parse_initial_states( bqm, num_reads=num_reads, initial_states=initial_states, initial_states_generator=initial_states_generator, seed=seed) num_reads = parsed_initial_states.num_reads initial_states = parsed_initial_states.initial_states # get linear/quadratic data linear, (coupler_starts, coupler_ends, coupler_weights), offset = \ bqm.to_numpy_vectors( variable_order=initial_states.variables, dtype=np.double, index_dtype=np.intc) # we need initial states as contiguous numpy array initial_states_array = \ np.ascontiguousarray(initial_states.record.sample, dtype=np.int8) # run the steepest descent samples, energies, num_steps = steepest_gradient_descent( num_reads, linear, coupler_starts, coupler_ends, coupler_weights, initial_states_array, large_sparse_opt) # sampling info info = dict(num_steps=num_steps) # resulting sampleset result = dimod.SampleSet.from_samples( (samples, initial_states.variables), energy=energies + offset, vartype=dimod.SPIN, info=info, ) result.change_vartype(original_vartype, inplace=True) return result
SteepestDescentSampler = SteepestDescentSolver