Rename var_form to ansatz (#31)

* change var_form to ansatz

* fix lint of test/regressor

test/algorithms/regressors/__init__.py

@@ -0,0 +1,11 @@
+# This code is part of Qiskit.
+#
+# (C) Copyright IBM 2021.
+#
+# This code is licensed under the Apache License, Version 2.0. You may
+# obtain a copy of this license in the LICENSE.txt file in the root directory
+# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.
+#
+# Any modifications or derivative works of this code must retain this
+# copyright notice, and modified files need to carry a notice indicating
+# that they have been altered from the originals.

test/algorithms/regressors/test_neural_network_regressor.py

@@ -11,17 +11,17 @@
 # that they have been altered from the originals.
 
 """ Test Neural Network Regressor """
-from ddt import ddt, data
-from qiskit import QuantumCircuit, Aer
-from qiskit.algorithms.optimizers import L_BFGS_B, COBYLA
+from test import QiskitMachineLearningTestCase
+
+import numpy as np
+from ddt import data, ddt
+
+from qiskit import Aer, QuantumCircuit
+from qiskit.algorithms.optimizers import COBYLA, L_BFGS_B
 from qiskit.circuit import Parameter
 from qiskit.utils import QuantumInstance
-
 from qiskit_machine_learning.algorithms.regressors import NeuralNetworkRegressor
 from qiskit_machine_learning.neural_networks import TwoLayerQNN
-from test import QiskitMachineLearningTestCase
-
-import numpy as np
 
 
 @ddt
@@ -43,6 +43,7 @@ def setUp(self):
         num_samples = 20
         eps = 0.2
 
+        # pylint: disable=invalid-name
         lb, ub = -np.pi, np.pi
         self.X = (ub - lb) * np.random.rand(num_samples, 1) + lb
         self.y = np.sin(self.X[:, 0]) + eps * (2 * np.random.rand(num_samples) - 1)
@@ -55,7 +56,8 @@ def setUp(self):
         ('bfgs', 'statevector'),
         ('bfgs', 'qasm'),
     )
-    def test_classifier_with_opflow_qnn(self, config):
+    def test_regressor_with_opflow_qnn(self, config):
+        """ Test Neural Network Regressor with Opflow QNN (Two Layer QNN)."""
         opt, q_i = config
 
         num_qubits = 1

test/algorithms/regressors/test_qsvr.py

@@ -13,7 +13,6 @@
 """ Test QSVR """
 
 import unittest
-
 from test import QiskitMachineLearningTestCase
 
 import numpy as np
@@ -22,8 +21,8 @@
 from qiskit.circuit.library import ZZFeatureMap
 from qiskit.utils import QuantumInstance
 from qiskit_machine_learning.algorithms import QSVR
-from qiskit_machine_learning.kernels import QuantumKernel
 from qiskit_machine_learning.exceptions import QiskitMachineLearningError
+from qiskit_machine_learning.kernels import QuantumKernel
 
 
 class TestQSVR(QiskitMachineLearningTestCase):

test/algorithms/regressors/test_vqr.py

@@ -13,17 +13,15 @@
 """ Test Neural Network Regressor """
 
 import unittest
-
-from qiskit.circuit import Parameter
-
 from test import QiskitMachineLearningTestCase
 
 import numpy as np
-from ddt import ddt, data
+from ddt import data, ddt
+
 from qiskit import Aer, QuantumCircuit
 from qiskit.algorithms.optimizers import COBYLA, L_BFGS_B
+from qiskit.circuit import Parameter
 from qiskit.utils import QuantumInstance
-
 from qiskit_machine_learning.algorithms import VQR
 
 
@@ -47,6 +45,7 @@ def setUp(self):
         num_samples = 20
         eps = 0.2
 
+        # pylint: disable=invalid-name
         lb, ub = -np.pi, np.pi
         self.X = (ub - lb) * np.random.rand(num_samples, 1) + lb
         self.y = np.sin(self.X[:, 0]) + eps * (2 * np.random.rand(num_samples) - 1)
@@ -68,7 +67,7 @@ def setUp(self):
     def test_vqr(self, config):
         """ Test VQR."""
 
-        opt, q_i, has_var_form = config
+        opt, q_i, has_ansatz = config
 
         if q_i == 'statevector':
             quantum_instance = self.sv_quantum_instance
@@ -86,16 +85,16 @@ def test_vqr(self, config):
         feature_map = QuantumCircuit(num_qubits, name='fm')
         feature_map.ry(param_x, 0)
 
-        if has_var_form:
+        if has_ansatz:
             param_y = Parameter('y')
-            var_form = QuantumCircuit(num_qubits, name='vf')
-            var_form.ry(param_y, 0)
+            ansatz = QuantumCircuit(num_qubits, name='vf')
+            ansatz.ry(param_y, 0)
         else:
-            var_form = None
+            ansatz = None
 
         # construct regressor
         regressor = VQR(feature_map=feature_map,
-                        var_form=var_form,
+                        ansatz=ansatz,
                         optimizer=optimizer,
                         quantum_instance=quantum_instance)