def test_logical_model_invalid_mtype(): with pytest.raises(ValueError): LogicalModel() with pytest.raises(ValueError): LogicalModel(mtype="invalidtype") with pytest.raises(ValueError): LogicalModel(mtype=12345)
def test_sawatabi_solver_with_initial_states_reverse(): model = LogicalModel(mtype="ising") x = model.variables("x", shape=(6,)) for i in range(6): model.add_interaction(x[i], coefficient=10.0) solver = SawatabiSolver() initial_states = [ { "x[0]": -1, "x[1]": -1, "x[2]": -1, "x[3]": -1, "x[4]": -1, "x[5]": -1, }, ] sampleset = solver.solve( model.to_physical(), num_reads=1, num_sweeps=10, initial_states=initial_states, reverse_options={"reverse_period": 5, "reverse_temperature": 10.0}, seed=12345, ) assert len(sampleset.record) == 1 # Check the ground state assert np.array_equal(sampleset.record[0].sample, [1, 1, 1, 1, 1, 1]) assert sampleset.record[0].energy == -60.0 assert sampleset.record[0].num_occurrences == 1
def ising_x44(): model = LogicalModel(mtype="ising") x = model.variables("x", shape=(4, 4)) model.add_interaction(x[0, 0], coefficient=20.0) model.add_interaction((x[1, 1], x[2, 2]), coefficient=21.0, attributes={"foo2": "bar2"}) model.add_interaction(x[3, 3], coefficient=22.0, scale=2.0, timestamp=12345, attributes={"myattr": "mymymymy"}) return model
def physical(): model = LogicalModel(mtype="qubo") x = model.variables("x", shape=(2, )) model.add_interaction(x[0], coefficient=1.0) model.add_interaction((x[0], x[1]), coefficient=-1.0) physical = model.to_physical() return physical
def test_sawatabi_solver_with_initial_states_qubo(): model = LogicalModel(mtype="qubo") x = model.variables("x", shape=(6,)) for i in range(6): model.add_interaction(x[i], coefficient=-1.0) solver = SawatabiSolver() initial_states = [ { "x[0]": 1, "x[1]": 0, "x[2]": 1, "x[3]": 0, "x[4]": 1, "x[5]": 0, }, ] sampleset = solver.solve(model.to_physical(), num_reads=1, num_sweeps=10, cooling_rate=0.5, initial_states=initial_states, seed=12345) assert len(sampleset.record) == 1 # Check the ground state assert np.array_equal(sampleset.record[0].sample, [0, 0, 0, 0, 0, 0]) assert sampleset.record[0].energy == 0.0 assert sampleset.record[0].num_occurrences == 1
def test_logical_model_variables_append_without_initialize(shape): model = LogicalModel(mtype="ising") # The following operation will be successful with a UserWarning. with pytest.warns(UserWarning): model.append("x", shape=shape) assert "x" in model.get_variables() assert model.get_variables_by_name("x").shape == shape
def test_logical_model_to_physical(mtype): model = LogicalModel(mtype=mtype) x = model.variables("x", shape=(2,)) model.add_interaction(x[0], coefficient=1.0) model.add_interaction((x[0], x[1]), coefficient=-1.0) model._update_interactions_dataframe_from_arrays() # Update the interactions DataFrame for debug assert model._interactions[model._interactions["name"] == "x[0]"]["dirty"].values[0] assert model._interactions[model._interactions["name"] == "x[0]*x[1]"]["dirty"].values[0] physical = model.to_physical() model._update_interactions_dataframe_from_arrays() # Update the interactions DataFrame for debug assert physical.get_mtype() == mtype assert len(physical._raw_interactions[constants.INTERACTION_LINEAR]) == 1 assert len(physical._raw_interactions[constants.INTERACTION_QUADRATIC]) == 1 assert physical._raw_interactions[constants.INTERACTION_LINEAR]["x[0]"] == 1.0 assert physical._raw_interactions[constants.INTERACTION_QUADRATIC][("x[0]", "x[1]")] == -1.0 assert not model._interactions[model._interactions["name"] == "x[0]"]["dirty"].values[0] assert not model._interactions[model._interactions["name"] == "x[0]*x[1]"]["dirty"].values[0] assert physical._label_to_index["x[0]"] == 0 assert physical._label_to_index["x[1]"] == 1 assert len(physical._label_to_index) == 2 assert physical._index_to_label[0] == "x[0]" assert physical._index_to_label[1] == "x[1]" assert len(physical._index_to_label) == 2
def test_local_solver_sa_ising(): model = LogicalModel(mtype="ising") s = model.variables("s", shape=(2,)) model.add_interaction(s[0], coefficient=1.0) model.add_interaction(s[1], coefficient=2.0) model.add_interaction((s[0], s[1]), coefficient=-3.0) model.offset(10.0) solver = LocalSolver() sampleset1 = solver.solve(model.to_physical(), seed=12345) assert sampleset1.variables == ["s[0]", "s[1]"] assert len(sampleset1.record) == 1 # Check the ground state assert np.array_equal(sampleset1.record[0].sample, [-1, 1]) assert sampleset1.record[0].energy == 6.0 assert sampleset1.record[0].num_occurrences == 1 # Check the second solve with the same solver instance sampleset2 = solver.solve(model.to_physical(), seed=54321) assert sampleset2.variables == ["s[0]", "s[1]"] assert len(sampleset2.record) == 1 # Check the ground state assert np.array_equal(sampleset2.record[0].sample, [-1, 1]) assert sampleset2.record[0].energy == 6.0 assert sampleset2.record[0].num_occurrences == 1
def test_sawatabi_solver_with_initial_states_ising(): model = LogicalModel(mtype="ising") x = model.variables("x", shape=(12,)) for i in range(12): model.add_interaction(x[i], coefficient=-1.0) solver = SawatabiSolver() initial_states = [ { "x[0]": 1, "x[1]": -1, "x[2]": -1, "x[3]": -1, "x[4]": -1, "x[5]": -1, "x[6]": -1, "x[7]": -1, "x[8]": -1, "x[9]": -1, "x[10]": -1, "x[11]": -1, }, ] sampleset = solver.solve(model.to_physical(), num_reads=1, num_sweeps=1, pickup_mode="sequential", initial_temperature=1e-9, initial_states=initial_states) assert len(sampleset.record) == 1 # Check the ground state assert np.array_equal(sampleset.record[0].sample, [-1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1]) assert sampleset.record[0].energy == -12.0 assert sampleset.record[0].num_occurrences == 1
def test_logical_model_variables_append(initial_shape, additional_shape, expected_shape): model = LogicalModel(mtype="ising") model.variables("x", shape=initial_shape) assert "x" in model.get_variables() assert model.get_variables_by_name("x").shape == initial_shape model.append("x", shape=additional_shape) assert model.get_variables_by_name("x").shape == expected_shape
def _create_ising_model_for_eq(): model = LogicalModel(mtype="ising") x = model.variables(name="x", shape=(3, )) model.delete_variable(x[0]) model.add_interaction(x[1], coefficient=1.0) model.add_interaction(x[2], coefficient=2.0) model.add_interaction((x[1], x[2]), coefficient=3.0) return model.to_physical()
def qubo(): model = LogicalModel(mtype="qubo") x = model.variables(name="x", shape=(3, )) model.delete_variable(x[0]) model.add_interaction(x[1], coefficient=1.0) model.add_interaction(x[2], coefficient=2.0) model.add_interaction((x[1], x[2]), coefficient=3.0) return model.to_physical()
def test_optigan_solver_with_ising_model_fails(): model = LogicalModel(mtype="ising") s = model.variables("s", shape=(2, )) model.add_interaction((s[0], s[1]), coefficient=-1.0) physical = model.to_physical() solver = OptiganSolver() with pytest.raises(ValueError): solver.solve(physical)
def test_optigan_solver_with_empty_config_fails(): model = LogicalModel(mtype="qubo") x = model.variables("x", shape=(2, )) model.add_interaction((x[0], x[1]), coefficient=-1.0) physical = model.to_physical() solver = OptiganSolver(config="/tmp/.optigan.yml") with pytest.raises(FileNotFoundError): solver.solve(physical)
def test_logical_model_variables_from_pyqubo(vartype, mtype): x = pyqubo.Array.create("x", shape=(2, 3), vartype=vartype) model = LogicalModel(mtype=mtype) x_when_applied = model.variables(x) x_from_model = model.get_variables_by_name("x") assert id(x) == id(x_when_applied) assert x == x_when_applied assert id(x) == id(x_from_model) assert x == x_from_model
def test_sawatabi_solver_invalid_pickup_mode(): model = LogicalModel(mtype="ising") x = model.variables("x", shape=(2,)) for i in range(2): model.add_interaction(x[i], coefficient=-1.0) solver = SawatabiSolver() with pytest.raises(ValueError): solver.solve(model.to_physical(), pickup_mode="invalid")
def test_local_solver_default_beta_range(): model = LogicalModel(mtype="ising") s = model.variables("s", shape=(2,)) model.add_interaction(s[0], coefficient=1.0) model.add_interaction(s[1], coefficient=2.0) model.add_interaction((s[0], s[1]), coefficient=-3.0) solver = LocalSolver() beta_range = solver.default_beta_range(model.to_physical()) assert beta_range == [0.13862943611198905, 4.605170185988092]
def test_sawatabi_solver_with_initial_states_fails(): model = LogicalModel(mtype="ising") x = model.variables("x", shape=(2,)) for i in range(2): model.add_interaction(x[i], coefficient=-1.0) solver = SawatabiSolver() initial_states = [{"x[0]": 1, "x[1]": 1}] with pytest.raises(ValueError): solver.solve(model.to_physical(), num_reads=2, initial_states=initial_states)
def _create_n_variable_random_complete_model(n=4, seed=None): if seed is not None: random.seed(seed) model = LogicalModel(mtype="ising") x = model.variables("x", shape=(n, )) for i in range(n): model.add_interaction(x[i], coefficient=random.random()) for i in range(n - 1): for j in range(i + 1, n): model.add_interaction((x[i], x[j]), coefficient=random.random())
def test_logical_model_variables(shape): model = LogicalModel(mtype="ising") x = model.variables("x", shape=shape) assert len(model.get_variables()) == 1 assert "x" in model.get_variables() assert x.shape == shape assert isinstance(x, pyqubo.Array) assert model.get_variables_by_name("x") == x assert id(model.get_variables_by_name("x")) == id(x) assert id(model.get_variables()["x"]) == id(x)
def test_sawatabi_solver_invalid_reverse_options(): model = LogicalModel(mtype="ising") x = model.variables("x", shape=(2,)) for i in range(2): model.add_interaction(x[i], coefficient=-1.0) solver = SawatabiSolver() with pytest.raises(ValueError): solver.solve(model.to_physical(), reverse_options={"reverse_period": 5}) with pytest.raises(ValueError): solver.solve(model.to_physical(), reverse_options={"reverse_temperature": 10.0})
def test_sawatabi_solver_with_stats(): model = LogicalModel(mtype="ising") x = model.variables("x", shape=(2,)) for i in range(2): model.add_interaction(x[i], coefficient=-1.0) solver = SawatabiSolver() sampleset, stats = solver.solve(model.to_physical(), num_reads=1, num_sweeps=10, cooling_rate=0.5, seed=12345, need_stats=True) assert stats[0]["acceptance_history"][-1] == 0 assert stats[0]["energy_history"][-1] == -2.0 assert stats[0]["temperature_history"] == [100.0, 50.0, 25.0, 12.5, 6.25, 3.125, 1.5625, 0.78125, 0.390625, 0.1953125]
def test_logical_model_empty(mtype): model = LogicalModel(mtype=mtype) x = model.variables("x", shape=(10, 10)) model.add_interaction(x[0, 0], coefficient=10.0) empty_model = model.empty() assert empty_model.get_mtype() == mtype assert len(empty_model._variables) == 0 assert len(empty_model._interactions_array) == len(empty_model._default_keys) for k, v in empty_model._interactions_array.items(): assert len(v) == 0 assert empty_model._interactions_length == 0
def test_sawatabi_solver_n_hot_ising_with_deleting(n, s, i): # n out of (s - 1) variables should be 1 model = LogicalModel(mtype="ising") x = model.variables("x", shape=(s,)) model.add_constraint(NHotConstraint(variables=x, n=n)) model.delete_variable(x[i]) solver = SawatabiSolver() sampleset = solver.solve(model.to_physical(), seed=12345) result = np.array(sampleset.record[0].sample) assert np.count_nonzero(result == 1) == n assert np.count_nonzero(result == -1) == s - n - 1
def test_sawatabi_solver_reuse_solver_instance(): model = LogicalModel(mtype="ising") x = model.variables("x", shape=(2,)) for i in range(2): model.add_interaction(x[i], coefficient=-1.0) solver = SawatabiSolver() sampleset_1 = solver.solve(model.to_physical(), num_reads=1, num_sweeps=10, cooling_rate=0.5, seed=12345) assert np.array_equal(sampleset_1.record[0].sample, [-1, -1]) assert sampleset_1.record[0].energy == -2.0 sampleset_2 = solver.solve(model.to_physical(), num_reads=1, num_sweeps=10, cooling_rate=0.5, seed=12345) assert np.array_equal(sampleset_2.record[0].sample, [-1, -1]) assert sampleset_2.record[0].energy == -2.0
def _create_nxn_random_lattice_model(n=4, seed=None): if seed is not None: random.seed(seed) model = LogicalModel(mtype="qubo") x = model.variables("x", shape=(n, n)) for i in range(n): for j in range(n): model.add_interaction(x[i, j], coefficient=random.random()) if 0 < i: model.add_interaction((x[i - 1, j], x[i, j]), coefficient=random.random()) if 0 < j: model.add_interaction((x[i, j - 1], x[i, j]), coefficient=random.random())
def test_sawatabi_solver_n_hot_qubo(n, s): # n out of s variables should be 1 model = LogicalModel(mtype="qubo") x = model.variables("x", shape=(s,)) model.add_constraint(NHotConstraint(variables=x, n=n)) solver = SawatabiSolver() sampleset = solver.solve(model.to_physical(), seed=12345) result = np.array(sampleset.record[0].sample) assert np.count_nonzero(result == 1) == n assert np.count_nonzero(result == 0) == s - n # Execution time should be within practical seconds (20 sec). assert sampleset.info["timing"]["execution_sec"] <= 20.0
def test_local_solver_zero_or_one_hot_qubo(n): model = LogicalModel(mtype="qubo") x = model.variables("x", shape=(n,)) model.add_constraint(ZeroOrOneHotConstraint(variables=x)) solver = LocalSolver() physical = model.to_physical() for seed in [11, 22, 33, 44, 55]: sampleset = solver.solve(physical, seed=seed) result = np.array(sampleset.record[0].sample) assert np.count_nonzero(result == 1) in [0, 1] # Execution time should be within practical seconds (20 sec). assert sampleset.info["timing"]["execution_sec"] <= 20.0
def test_logical_model_to_physical_with_n_hot_constraint_qubo(n, s): # n out of s variables should be 1 model = LogicalModel(mtype="qubo") x = model.variables("x", shape=(s,)) model.add_constraint(NHotConstraint(x, n=n)) physical = model.to_physical() for i in range(s): assert physical._raw_interactions[constants.INTERACTION_LINEAR][f"x[{i}]"] == 2 * n - 1.0 for i in range(s): for j in range(s): l1 = f"x[{i}]" l2 = f"x[{j}]" if l1 < l2: assert physical._raw_interactions[constants.INTERACTION_QUADRATIC][(l1, l2)] == -2.0
def _create_ising_model_for_eq(): model = LogicalModel(mtype="ising") x = model.variables(name="x", shape=(4,)) z = model.variables(name="z", shape=(4,)) model.add_interaction(target=x[0], coefficient=1.1) model.add_interaction(target=(x[0], x[1]), coefficient=2.2, scale=3.3, attributes={"foo": "bar"}) model.add_interaction(target=x[2], coefficient=4.4) model.add_interaction(target=x[3], coefficient=5.5) model.remove_interaction(target=x[2]) model.fix_variable(target=x[3], value=1) model.add_constraint(NHotConstraint(variables=z, n=1)) return model