Source code for dimod.testing.asserts

# Copyright 2018 D-Wave Systems Inc.
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# =============================================================================
import collections.abc as abc

import dimod


[docs]def assert_sampler_api(sampler): """Assert that an instantiated sampler exposes correct properties and methods. Args: sampler (:obj:`.Sampler`): User-made dimod sampler. Raises: AssertionError: If the given sampler does not match the sampler API. See also: :class:`.Sampler` for the abstract base class that defines the sampler API. """ # abstract base class assert isinstance(sampler, dimod.Sampler), "instantiated sampler must be a dimod Sampler object" # sample methods assert hasattr(sampler, 'sample'), "instantiated sampler must have a 'sample' method" assert callable(sampler.sample), "instantiated sampler must have a 'sample' method" assert hasattr(sampler, 'sample_ising'), "instantiated sampler must have a 'sample_ising' method" assert callable(sampler.sample_ising), "instantiated sampler must have a 'sample_ising' method" assert hasattr(sampler, 'sample_qubo'), "instantiated sampler must have a 'sample_qubo' method" assert callable(sampler.sample_qubo), "instantiated sampler must have a 'sample_qubo' method" # properties msg = "instantiated sampler must have a 'parameters' property, set to a Mapping" assert hasattr(sampler, 'parameters'), msg assert not callable(sampler.parameters), msg assert isinstance(sampler.parameters, abc.Mapping), msg msg = "instantiated sampler must have a 'properties' property, set to a Mapping" assert hasattr(sampler, 'properties'), msg assert not callable(sampler.properties), msg assert isinstance(sampler.properties, abc.Mapping), msg
[docs]def assert_composite_api(composed_sampler): """Assert that an instantiated composed sampler exposes correct composite properties and methods. Args: sampler (:obj:`.Composite`): User-made dimod composed sampler. Raises: AssertionError: If the given sampler does not match the composite API. See also: :class:`.Composite` for the abstract base class that defines the composite API. :obj:`~.assert_sampler_api` to assert that the composed sampler matches the sampler API. """ assert isinstance(composed_sampler, dimod.Composite) # properties msg = "instantiated composed sampler must have a 'children' property, set to a list (or Sequence)" assert hasattr(composed_sampler, 'children'), msg assert isinstance(composed_sampler.children, abc.Sequence), msg msg = "instantiated composed sampler must have a 'child' property, set to one of sampler.children" assert hasattr(composed_sampler, 'child'), msg assert composed_sampler.child in composed_sampler.children, msg
[docs]def assert_structured_api(sampler): """Assert that an instantiated structured sampler exposes correct composite properties and methods. Args: sampler (:obj:`.Structured`): User-made dimod structured sampler. Raises: AssertionError: If the given sampler does not match the structured API. See also: :class:`.Structured` for the abstract base class that defines the structured API. :obj:`~.assert_sampler_api` to assert that the structured sampler matches the sampler API. """ assert isinstance(sampler, dimod.Structured), "must be a Structured sampler" # properties msg = ("instantiated structured sampler must have an 'adjacency' property formatted as a dict " "where the keys are the nodes and the values are sets of all node adjacency to the key") assert hasattr(sampler, 'adjacency'), msg assert isinstance(sampler.adjacency, abc.Mapping), msg for u, neighborhood in sampler.adjacency.items(): assert isinstance(neighborhood, abc.Set), msg for v in neighborhood: assert v in sampler.adjacency, msg assert u in sampler.adjacency[v], msg msg = "instantiated structured sampler must have a 'nodelist' property, set to a list" assert hasattr(sampler, 'nodelist'), msg assert isinstance(sampler.nodelist, abc.Sequence), msg for v in sampler.nodelist: assert v in sampler.adjacency, msg msg = "instantiated structured sampler must have a 'edge' property, set to a list of 2-lists/tuples" assert hasattr(sampler, 'edgelist'), msg assert isinstance(sampler.edgelist, abc.Sequence), msg for edge in sampler.edgelist: assert isinstance(edge, abc.Sequence), msg assert len(edge) == 2, msg u, v = edge assert v in sampler.adjacency, msg assert u in sampler.adjacency, msg assert u != v, msg
[docs]def assert_response_energies(response, bqm, precision=7): """Assert that each sample in the given response has the correct energy. Args: response (:obj:`.SampleSet`): Response as returned by a dimod sampler. bqm (:obj:`.BinaryQuadraticModel`): Binary quadratic model (BQM) used to generate the samples. precision (int, optional, default=7): Equality of energy is tested by calculating the difference between the `response`'s sample energy and that returned by BQM's :meth:`~.BinaryQuadraticModel.energy`, rounding to the closest multiple of 10 to the power of minus `precision`. Raises: AssertionError: If any of the samples in the response do not match their associated energy. See also: :func:`.assert_sampleset_energies` """ return assert_sampleset_energies(response, bqm, precision)
[docs]def assert_sampleset_energies(sampleset, bqm, precision=7): """Assert that each sample in the given sample set has the correct energy. Args: sampleset (:obj:`.SampleSet`): Sample set as returned by a dimod sampler. bqm (:obj:`.BinaryQuadraticModel`/:obj:`.BinaryPolynomial`): The binary quadratic model (BQM) or binary polynomial used to generate the samples. precision (int, optional, default=7): Equality of energy is tested by calculating the difference between the `response`'s sample energy and that returned by BQM's :meth:`~.BinaryQuadraticModel.energy`, rounding to the closest multiple of 10 to the power of minus `precision`. Raises: AssertionError: If any of the samples in the sample set do not match their associated energy. Examples: >>> import dimod.testing ... >>> sampler = dimod.ExactSolver() >>> bqm = dimod.BinaryQuadraticModel.from_ising({}, {(0, 1): -1}) >>> sampleset = sampler.sample(bqm) >>> dimod.testing.assert_response_energies(sampleset, bqm) """ assert isinstance(sampleset, dimod.SampleSet), "expected sampleset to be a dimod SampleSet object" for sample, energy in sampleset.data(['sample', 'energy']): assert isinstance(sample, abc.Mapping), "'for sample in sampleset', each sample should be a Mapping" for v, value in sample.items(): assert v in bqm.variables, 'sample contains a variable not in the given bqm' assert value in bqm.vartype.value, 'sample contains a value not of the correct type' for v in bqm.variables: assert v in sample, "bqm contains a variable not in sample" assert round(bqm.energy(sample) - energy, precision) == 0
[docs]def assert_bqm_almost_equal(actual, desired, places=7, ignore_zero_interactions=False): """Test if two binary quadratic models have almost equal biases. Args: actual (:obj:`.BinaryQuadraticModel`): First binary quadratic model. desired (:obj:`.BinaryQuadraticModel`): Second binary quadratic model. places (int, optional, default=7): Bias equality is computed as :code:`round(b0 - b1, places) == 0`. ignore_zero_interactions (bool, optional, default=False): If true, interactions with 0 bias are ignored. """ assert isinstance(actual, dimod.BinaryQuadraticModel), "not a binary quadratic model" assert isinstance(desired, dimod.BinaryQuadraticModel), "not a binary quadratic model" # vartype should match assert actual.vartype is desired.vartype, "unlike vartype" # variables should match variables_diff = set(actual).symmetric_difference(desired) if variables_diff: v = variables_diff.pop() msg = "{!r} is not a shared variable".format(v) raise AssertionError(msg) # offset if round(actual.offset - desired.offset, places): msg = 'offsets {} != {}'.format(desired.offset, actual.offset) raise AssertionError(msg) # linear biases - we already checked variables for v, bias in desired.linear.items(): if round(bias - actual.linear[v], places): msg = 'linear bias associated with {!r} does not match, {!r} != {!r}' raise AssertionError(msg.format(v, bias, actual.linear[v])) default = 0 if ignore_zero_interactions else None for inter, bias in actual.quadratic.items(): other_bias = desired.quadratic.get(inter, default) if other_bias is None: raise AssertionError('{!r} is not a shared interaction'.format(inter)) if round(bias - other_bias, places): msg = 'quadratic bias associated with {!r} does not match, {!r} != {!r}' raise AssertionError(msg.format(inter, bias, other_bias)) for inter, bias in desired.quadratic.items(): other_bias = actual.quadratic.get(inter, default) if other_bias is None: raise AssertionError('{!r} is not a shared interaction'.format(inter)) if round(bias - other_bias, places): msg = 'quadratic bias associated with {!r} does not match, {!r} != {!r}' raise AssertionError(msg.format(inter, bias, other_bias))
def assert_consistent_bqm(bqm): """Test whether a BQM is self-consistent. This is useful when making new BQM subclasses. Asserts that all of the attributes are self-consistent. Args: bqm: A binary quadratic model. """ # adjacency and linear are self-consistent for v in bqm.linear: assert v in bqm.adj for v in bqm.adj: assert v in bqm.linear # adjacency and quadratic are self-consistent for u, v in bqm.quadratic: assert v in bqm.linear assert v in bqm.adj assert u in bqm.adj[v] assert u in bqm.linear assert u in bqm.adj assert v in bqm.adj[u] assert bqm.adj[u][v] == bqm.quadratic[(u, v)] assert bqm.adj[u][v] == bqm.quadratic[(v, u)] assert bqm.adj[v][u] == bqm.adj[u][v] for u, v in bqm.quadratic: assert bqm.get_quadratic(u, v) == bqm.quadratic[(u, v)] assert bqm.get_quadratic(u, v) == bqm.quadratic[(v, u)] assert bqm.get_quadratic(v, u) == bqm.quadratic[(u, v)] assert bqm.get_quadratic(v, u) == bqm.quadratic[(v, u)] for u in bqm.adj: for v in bqm.adj[u]: assert (u, v) in bqm.quadratic assert (v, u) in bqm.quadratic assert len(bqm.quadratic) == bqm.num_interactions assert len(bqm.linear) == bqm.num_variables assert len(bqm.quadratic) == len(set(bqm.quadratic)) assert len(bqm.variables) == len(bqm.linear) assert (bqm.num_variables, bqm.num_interactions) == bqm.shape