Merge pull request qiskit-community/qiskit-aqua#851 from manoelmarques/refactor

Refactor classes and file names from classical to numpy

Author: manoelmarques

qiskit/optimization/ising/docplex.py

@@ -52,7 +52,7 @@
     qubitOp, offset = get_operator(mdl)
 
     # Calculate with the generated Ising Hamiltonian.
-    ee = ClassicalMinimumEigensolver(qubitOp)
+    ee = NumPyMinimumEigensolver(qubitOp)
     result = ee.run()
     print('get_operator')
     print('tsp objective:', result['energy'] + offset)

test/optimization/test_clique.py

@@ -21,7 +21,7 @@
 from qiskit.aqua import aqua_globals, QuantumInstance
 from qiskit.optimization.ising import clique
 from qiskit.optimization.ising.common import random_graph, sample_most_likely
-from qiskit.aqua.algorithms import ClassicalMinimumEigensolver, VQE
+from qiskit.aqua.algorithms import NumPyMinimumEigensolver, VQE
 from qiskit.aqua.components.optimizers import COBYLA
 from qiskit.aqua.components.variational_forms import RY
 
@@ -57,7 +57,7 @@ def bitfield(n, length):
 
     def test_clique(self):
         """ Clique test """
-        algo = ClassicalMinimumEigensolver(self.qubit_op, aux_operators=[])
+        algo = NumPyMinimumEigensolver(self.qubit_op, aux_operators=[])
         result = algo.run()
         x = sample_most_likely(result.eigenstate)
         ising_sol = clique.get_graph_solution(x)

test/optimization/test_docplex.py

@@ -23,7 +23,7 @@
 from qiskit.quantum_info import Pauli
 
 from qiskit.aqua import AquaError, aqua_globals
-from qiskit.aqua.algorithms import ClassicalMinimumEigensolver
+from qiskit.aqua.algorithms import NumPyMinimumEigensolver
 from qiskit.optimization.ising import docplex, tsp
 from qiskit.aqua.operators import WeightedPauliOperator
 
@@ -230,10 +230,10 @@ def test_docplex_maxcut(self):
         mdl.maximize(maxcut_func)
         qubit_op, offset = docplex.get_operator(mdl)
 
-        e_e = ClassicalMinimumEigensolver(qubit_op)
+        e_e = NumPyMinimumEigensolver(qubit_op)
         result = e_e.run()
 
-        ee_expected = ClassicalMinimumEigensolver(QUBIT_OP_MAXCUT)
+        ee_expected = NumPyMinimumEigensolver(QUBIT_OP_MAXCUT)
         expected_result = ee_expected.run()
 
         # Compare objective
@@ -264,10 +264,10 @@ def test_docplex_tsp(self):
             mdl.add_constraint(mdl.sum(x[(i, j)] for i in range(num_node)) == 1)
         qubit_op, offset = docplex.get_operator(mdl)
 
-        e_e = ClassicalMinimumEigensolver(qubit_op)
+        e_e = NumPyMinimumEigensolver(qubit_op)
         result = e_e.run()
 
-        ee_expected = ClassicalMinimumEigensolver(QUBIT_OP_TSP)
+        ee_expected = NumPyMinimumEigensolver(QUBIT_OP_TSP)
         expected_result = ee_expected.run()
 
         # Compare objective
@@ -284,7 +284,7 @@ def test_docplex_integer_constraints(self):
         mdl.add_constraint(mdl.sum(i * x[i] for i in range(1, 5)) == 3)
         qubit_op, offset = docplex.get_operator(mdl)
 
-        e_e = ClassicalMinimumEigensolver(qubit_op)
+        e_e = NumPyMinimumEigensolver(qubit_op)
         result = e_e.run()
 
         expected_result = -2
@@ -313,7 +313,7 @@ def test_docplex_constant_and_quadratic_terms_in_object_function(self):
         mdl.minimize(ising_func)
         qubit_op, offset = docplex.get_operator(mdl)
 
-        e_e = ClassicalMinimumEigensolver(qubit_op)
+        e_e = NumPyMinimumEigensolver(qubit_op)
         result = e_e.run()
 
         expected_result = -22

test/optimization/test_exact_cover.py

@@ -22,7 +22,7 @@
 from qiskit.aqua import aqua_globals, QuantumInstance
 from qiskit.optimization.ising import exact_cover
 from qiskit.optimization.ising.common import sample_most_likely
-from qiskit.aqua.algorithms import ClassicalMinimumEigensolver, VQE
+from qiskit.aqua.algorithms import NumPyMinimumEigensolver, VQE
 from qiskit.aqua.components.optimizers import COBYLA
 from qiskit.aqua.components.variational_forms import RYRZ
 
@@ -57,7 +57,7 @@ def bitfield(n, length):
 
     def test_exact_cover(self):
         """ Exact Cover test """
-        algo = ClassicalMinimumEigensolver(self.qubit_op, aux_operators=[])
+        algo = NumPyMinimumEigensolver(self.qubit_op, aux_operators=[])
         result = algo.run()
         x = sample_most_likely(result.eigenstate)
         ising_sol = exact_cover.get_solution(x)

test/optimization/test_graph_partition.py

@@ -20,7 +20,7 @@
 from qiskit.aqua import aqua_globals, QuantumInstance
 from qiskit.optimization.ising import graph_partition
 from qiskit.optimization.ising.common import random_graph, sample_most_likely
-from qiskit.aqua.algorithms import ClassicalMinimumEigensolver, VQE
+from qiskit.aqua.algorithms import NumPyMinimumEigensolver, VQE
 from qiskit.aqua.components.variational_forms import RY
 from qiskit.aqua.components.optimizers import SPSA
 
@@ -58,7 +58,7 @@ def bitfield(n, length):
 
     def test_graph_partition(self):
         """ Graph Partition test """
-        algo = ClassicalMinimumEigensolver(self.qubit_op, aux_operators=[])
+        algo = NumPyMinimumEigensolver(self.qubit_op, aux_operators=[])
         result = algo.run()
         x = sample_most_likely(result.eigenstate)
         # check against the oracle

test/optimization/test_partition.py

@@ -20,7 +20,7 @@
 from qiskit.aqua import aqua_globals, QuantumInstance
 from qiskit.optimization.ising import partition
 from qiskit.optimization.ising.common import read_numbers_from_file, sample_most_likely
-from qiskit.aqua.algorithms import ClassicalMinimumEigensolver, VQE
+from qiskit.aqua.algorithms import NumPyMinimumEigensolver, VQE
 from qiskit.aqua.components.optimizers import SPSA
 from qiskit.aqua.components.variational_forms import RY
 
@@ -36,7 +36,7 @@ def setUp(self):
 
     def test_partition(self):
         """ Partition test """
-        algo = ClassicalMinimumEigensolver(self.qubit_op, aux_operators=[])
+        algo = NumPyMinimumEigensolver(self.qubit_op, aux_operators=[])
         result = algo.run()
         x = sample_most_likely(result.eigenstate)
         np.testing.assert_array_equal(x, [0, 1, 0])

test/optimization/test_readme_sample.py

@@ -27,7 +27,12 @@ class TestReadmeSample(QiskitOptimizationTestCase):
 
     def test_readme_sample(self):
         """ readme sample test """
-        # pylint: disable=import-outside-toplevel
+        # pylint: disable=import-outside-toplevel,redefined-builtin
+
+        def print(*args):
+            """ overloads print to log values """
+            if args:
+                self.log.debug(args[0], *args[1:])
 
         # --- Exact copy of sample code ----------------------------------------
 
@@ -75,10 +80,10 @@ def test_readme_sample(self):
                                            seed_transpiler=seed)
         result = qaoa.run(quantum_instance)
 
-        x = sample_most_likely(result['eigvecs'][0])
-        print('energy:', result['energy'])
-        print('time:', result['eval_time'])
-        print('max-cut objective:', result['energy'] + offset)
+        x = sample_most_likely(result.eigenstate)
+        print('energy:', result.eigenvalue.real)
+        print('time:', result.optimizer_time)
+        print('max-cut objective:', result.eigenvalue.real + offset)
         print('solution:', max_cut.get_graph_solution(x))
         print('solution objective:', max_cut.max_cut_value(x, w))
 

test/optimization/test_set_packing.py

@@ -21,7 +21,7 @@
 from qiskit.optimization.ising import set_packing
 from qiskit.optimization.ising.common import sample_most_likely
 from qiskit.aqua import QuantumInstance, aqua_globals
-from qiskit.aqua.algorithms import ClassicalMinimumEigensolver, VQE
+from qiskit.aqua.algorithms import NumPyMinimumEigensolver, VQE
 from qiskit.aqua.components.optimizers import SPSA
 from qiskit.aqua.components.variational_forms import RY
 
@@ -55,7 +55,7 @@ def bitfield(n, length):
 
     def test_set_packing(self):
         """ set packing test """
-        algo = ClassicalMinimumEigensolver(self.qubit_op, aux_operators=[])
+        algo = NumPyMinimumEigensolver(self.qubit_op, aux_operators=[])
         result = algo.run()
         x = sample_most_likely(result.eigenstate)
         ising_sol = set_packing.get_solution(x)

test/optimization/test_stable_set.py

@@ -21,7 +21,7 @@
 from qiskit.aqua import aqua_globals, QuantumInstance
 from qiskit.optimization.ising import stable_set
 from qiskit.optimization.ising.common import random_graph, sample_most_likely
-from qiskit.aqua.algorithms import ClassicalMinimumEigensolver, VQE
+from qiskit.aqua.algorithms import NumPyMinimumEigensolver, VQE
 from qiskit.aqua.components.optimizers import L_BFGS_B
 from qiskit.aqua.components.variational_forms import RYRZ
 
@@ -39,7 +39,7 @@ def setUp(self):
 
     def test_stable_set(self):
         """ Stable set test """
-        algo = ClassicalMinimumEigensolver(self.qubit_op, aux_operators=[])
+        algo = NumPyMinimumEigensolver(self.qubit_op, aux_operators=[])
         result = algo.run()
         x = sample_most_likely(result.eigenstate)
         self.assertAlmostEqual(result.eigenvalue.real, -29.5)

test/optimization/test_tsp.py

@@ -20,7 +20,7 @@
 from qiskit.aqua import aqua_globals
 from qiskit.optimization.ising import tsp
 from qiskit.optimization.ising.common import sample_most_likely
-from qiskit.aqua.algorithms import ClassicalMinimumEigensolver
+from qiskit.aqua.algorithms import NumPyMinimumEigensolver
 
 
 class TestTSP(QiskitOptimizationTestCase):
@@ -36,7 +36,7 @@ def setUp(self):
 
     def test_tsp(self):
         """ TSP test """
-        algo = ClassicalMinimumEigensolver(self.qubit_op)
+        algo = NumPyMinimumEigensolver(self.qubit_op)
         result = algo.run()
         x = sample_most_likely(result.eigenstate)
         order = tsp.get_tsp_solution(x)