def setUp(self):
super().setUp()
random.seed(123)
low = 0
high = 100
pos = {
i: (random.randint(low, high), random.randint(low, high))
for i in range(4)
}
self.graph = nx.random_geometric_graph(
4, np.hypot(high - low, high - low) + 1, pos=pos)
for w, v in self.graph.edges:
delta = [
self.graph.nodes[w]["pos"][i] - self.graph.nodes[v]["pos"][i]
for i in range(2)
]
self.graph.edges[w, v]["weight"] = np.rint(
np.hypot(delta[0], delta[1]))
op = QuadraticProgram()
for i in range(16):
op.binary_var()
self.result = OptimizationResult(
x=[1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1],
fval=272,
variables=op.variables,
status=OptimizationResultStatus.SUCCESS,
)
def test_prettyprint(self):
"""test prettyprint"""
with self.subTest("success"):
q_p = QuadraticProgram()
q_p.integer_var_list(3)
result = OptimizationResult(
x=[1.0, 2.0, 3.0],
fval=10.1,
variables=q_p.variables,
status=OptimizationResultStatus.SUCCESS,
)
expected = "\n".join([
"objective function value: 10.1",
"variable values: x0=1.0, x1=2.0, x2=3.0",
"status: SUCCESS",
])
self.assertEqual(result.prettyprint(), expected)
with self.subTest("failure"):
q_p = QuadraticProgram()
q_p.integer_var_list(3)
result = OptimizationResult(
x=[-1.0, 2.0, -3.0],
fval=10.0,
variables=q_p.variables,
status=OptimizationResultStatus.FAILURE,
)
expected = "\n".join([
"objective function value: 10.0",
"variable values: x0=-1.0, x1=2.0, x2=-3.0",
"status: FAILURE",
])
self.assertEqual(result.prettyprint(), expected)
with self.subTest("infeasible"):
q_p = QuadraticProgram()
q_p.integer_var_list(3, name="y", key_format="_{}")
result = OptimizationResult(
x=[1.0, 2.0, -3.0],
fval=11.0,
variables=q_p.variables,
status=OptimizationResultStatus.INFEASIBLE,
)
expected = "\n".join([
"objective function value: 11.0",
"variable values: y_0=1.0, y_1=2.0, y_2=-3.0",
"status: INFEASIBLE",
])
self.assertEqual(result.prettyprint(), expected)
def setUp(self):
"""Set up for the test"""
super().setUp()
self.num_set = [8, 7, 6, 5, 4]
op = QuadraticProgram()
for _ in range(5):
op.binary_var()
self.result = OptimizationResult(
x=[1, 1, 0, 0, 0], fval=0, variables=op.variables,
status=OptimizationResultStatus.SUCCESS)
def setUp(self):
"""Set up for the tests"""
super().setUp()
self.graph = nx.gnm_random_graph(5, 8, 123)
op = QuadraticProgram()
for _ in range(5):
op.binary_var()
self.result = OptimizationResult(
x=[1, 0, 1, 1, 1],
fval=4,
variables=op.variables,
status=OptimizationResultStatus.SUCCESS,
)
self.result_c3 = OptimizationResult(
x=[1, 0, 1, 1, 0],
fval=0,
variables=op.variables,
status=OptimizationResultStatus.SUCCESS,
)
def setUp(self):
"""set up the test class"""
super().setUp()
self.graph = nx.gnm_random_graph(5, 4, 3)
op = QuadraticProgram()
for _ in range(5):
op.binary_var()
self.result = OptimizationResult(
x=[0, 0, 0, 0, 1], fval=1, variables=op.variables,
status=OptimizationResultStatus.SUCCESS)
def setUp(self):
super().setUp()
self.graph = nx.gnm_random_graph(5, 4, 3)
op = QuadraticProgram()
for _ in range(5):
op.binary_var()
self.result = OptimizationResult(
x=[1, 1, 1, 1, 0],
fval=4,
variables=op.variables,
status=OptimizationResultStatus.SUCCESS)
def test_str_repr(self):
"""test str and repr"""
with self.subTest("success"):
q_p = QuadraticProgram()
q_p.integer_var_list(3)
result = OptimizationResult(
x=[1.0, 2.0, 3.0],
fval=10.1,
variables=q_p.variables,
status=OptimizationResultStatus.SUCCESS,
)
expected = "fval=10.1, x0=1.0, x1=2.0, x2=3.0, status=SUCCESS"
self.assertEqual(str(result), expected)
self.assertEqual(repr(result), f"<OptimizationResult: {expected}>")
with self.subTest("failure"):
q_p = QuadraticProgram()
q_p.integer_var_list(3)
result = OptimizationResult(
x=[-1.0, 2.0, -3.0],
fval=10.0,
variables=q_p.variables,
status=OptimizationResultStatus.FAILURE,
)
expected = "fval=10.0, x0=-1.0, x1=2.0, x2=-3.0, status=FAILURE"
self.assertEqual(str(result), expected)
self.assertEqual(repr(result), f"<OptimizationResult: {expected}>")
with self.subTest("infeasible"):
q_p = QuadraticProgram()
q_p.integer_var_list(3, name="y", key_format="_{}")
result = OptimizationResult(
x=[1.0, 2.0, -3.0],
fval=11.0,
variables=q_p.variables,
status=OptimizationResultStatus.INFEASIBLE,
)
expected = "fval=11.0, y_0=1.0, y_1=2.0, y_2=-3.0, status=INFEASIBLE"
self.assertEqual(str(result), expected)
self.assertEqual(repr(result), f"<OptimizationResult: {expected}>")
def setUp(self):
"""Set up for the tests"""
super().setUp()
self.values = [10, 40, 30, 50]
self.weights = [5, 4, 6, 3]
self.max_weight = 10
op = QuadraticProgram()
for _ in range(4):
op.binary_var()
self.result = OptimizationResult(
x=[0, 1, 0, 1], fval=90, variables=op.variables,
status=OptimizationResultStatus.SUCCESS)
def setUp(self):
super().setUp()
self.total_set = [1, 2, 3, 4, 5]
self.list_of_subsets = [[1, 2, 3], [2, 3, 4], [4, 5], [1, 3], [2]]
op = QuadraticProgram()
for _ in range(5):
op.binary_var()
self.result = OptimizationResult(
x=[0, 0, 1, 1, 1],
fval=3,
variables=op.variables,
status=OptimizationResultStatus.SUCCESS)
def setUp(self):
"""Set up for the tests"""
super().setUp()
self.total_set = [1, 2, 3, 4, 5]
self.list_of_subsets = [[1, 4], [2, 3, 4], [1, 5], [2, 3]]
op = QuadraticProgram()
for _ in range(4):
op.binary_var()
self.result = OptimizationResult(
x=[0, 1, 1, 0],
fval=2,
variables=op.variables,
status=OptimizationResultStatus.SUCCESS)
def setUp(self):
"""Set up for the tests"""
super().setUp()
self.weights = [16, 9, 23]
self.max_weight = 40
self.max_number_of_bins = 2
op = QuadraticProgram()
for _ in range(12):
op.binary_var()
self.result = OptimizationResult(
x=[1, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 1],
fval=2.0,
variables=op.variables,
status=OptimizationResultStatus.SUCCESS,
)
def test_init(self):
"""test init"""
q_p = QuadraticProgram()
q_p.integer_var_list(3)
result = OptimizationResult(x=[1, 2, 3],
fval=10,
variables=q_p.variables,
status=OptimizationResultStatus.SUCCESS)
np.testing.assert_allclose(result.x, [1, 2, 3])
self.assertAlmostEqual(result.fval, 10)
self.assertEqual(result.status, OptimizationResultStatus.SUCCESS)
self.assertIsNone(result.raw_results)
self.assertEqual(len(result.samples), 1)
sample = result.samples[0]
np.testing.assert_allclose(sample.x, [1, 2, 3])
self.assertAlmostEqual(sample.fval, 10)
self.assertEqual(sample.status, OptimizationResultStatus.SUCCESS)
self.assertAlmostEqual(sample.probability, 1)
def setUp(self):
super().setUp()
self._num_of_sites = 2
self._seed = 0
self._graph = nx.convert_matrix.from_numpy_matrix(
np.array([[0, -1], [-1, 0]]))
self._new_disorder_graph = nx.convert_matrix.from_numpy_matrix(
np.array([[0, 1], [1, 0]]))
op = QuadraticProgram()
for _ in range(2):
op.binary_var()
self._result = OptimizationResult(
x=[0, 0],
fval=-1 / np.sqrt(2),
variables=op.variables,
status=OptimizationResultStatus.SUCCESS,
)
