Modified test_qsvr.py and test_fidelity_quantum_kernel_qsvr.py files for regression tasks

test/algorithms/regressors/test_fidelity_quantum_kernel_qsvr.py

@@ -1,129 +1,156 @@
-# This code is part of a Qiskit project.
-#
-# (C) Copyright IBM 2021, 2024.
-#
-# 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 QSVR on fidelity quantum kernel."""
-
-import os
-import tempfile
-import unittest
-
-from test import QiskitMachineLearningTestCase
-
-import numpy as np
-from qiskit.circuit.library import ZZFeatureMap
-from qiskit.primitives import Sampler
-from qiskit_machine_learning.utils import algorithm_globals
-
-from qiskit_machine_learning.algorithms import QSVR, SerializableModelMixin
-from qiskit_machine_learning.exceptions import QiskitMachineLearningWarning
-from qiskit_machine_learning.kernels import FidelityQuantumKernel
-
-
-class TestQSVR(QiskitMachineLearningTestCase):
-    """Test QSVR Algorithm on fidelity quantum kernel."""
-
-    def setUp(self):
-        super().setUp()
-
-        algorithm_globals.random_seed = 10598
-
-        self.sampler = Sampler()
-        self.feature_map = ZZFeatureMap(feature_dimension=2, reps=2)
-
-        self.sample_train = np.asarray(
-            [
-                [3.07876080, 1.75929189],
-                [6.03185789, 5.27787566],
-                [6.22035345, 2.70176968],
-                [0.18849556, 2.82743339],
-            ]
-        )
-        self.label_train = np.asarray([0, 0, 1, 1])
-
-        self.sample_test = np.asarray([[2.199114860, 5.15221195], [0.50265482, 0.06283185]])
-        self.label_test = np.asarray([0, 1])
-
-    def test_qsvr(self):
-        """Test QSVR"""
-        qkernel = FidelityQuantumKernel(feature_map=self.feature_map)
-
-        qsvr = QSVR(quantum_kernel=qkernel)
-        qsvr.fit(self.sample_train, self.label_train)
-        score = qsvr.score(self.sample_test, self.label_test)
-
-        self.assertAlmostEqual(score, 0.38, places=2)
-
-    def test_change_kernel(self):
-        """Test QSVR with QuantumKernel later"""
-        qkernel = FidelityQuantumKernel(feature_map=self.feature_map)
-
-        qsvr = QSVR()
-        qsvr.quantum_kernel = qkernel
-        qsvr.fit(self.sample_train, self.label_train)
-        score = qsvr.score(self.sample_test, self.label_test)
-
-        self.assertAlmostEqual(score, 0.38, places=2)
-
-    def test_qsvr_parameters(self):
-        """Test QSVR with extra constructor parameters"""
-        qkernel = FidelityQuantumKernel(feature_map=self.feature_map)
-
-        qsvr = QSVR(quantum_kernel=qkernel, tol=1e-4, C=0.5)
-        qsvr.fit(self.sample_train, self.label_train)
-        score = qsvr.score(self.sample_test, self.label_test)
-
-        self.assertAlmostEqual(score, 0.38, places=2)
-
-    def test_qsvc_to_string(self):
-        """Test QSVR print works when no *args passed in"""
-        qsvr = QSVR()
-        _ = str(qsvr)
-
-    def test_with_kernel_parameter(self):
-        """Test QSVC with the `kernel` argument."""
-        with self.assertWarns(QiskitMachineLearningWarning):
-            QSVR(kernel=1)
-
-    def test_save_load(self):
-        """Tests save and load models."""
-        features = np.array([[0, 0], [0.1, 0.1], [0.4, 0.4], [1, 1]])
-        labels = np.array([0, 0.1, 0.4, 1])
-
-        quantum_kernel = FidelityQuantumKernel(feature_map=ZZFeatureMap(2))
-        regressor = QSVR(quantum_kernel=quantum_kernel)
-        regressor.fit(features, labels)
-
-        # predicted labels from the newly trained model
-        test_features = np.array([[0.5, 0.5]])
-        original_predicts = regressor.predict(test_features)
-
-        # save/load, change the quantum instance and check if predicted values are the same
-        with tempfile.TemporaryDirectory() as dir_name:
-            file_name = os.path.join(dir_name, "qsvr.model")
-            regressor.save(file_name)
-
-            regressor_load = QSVR.load(file_name)
-            loaded_model_predicts = regressor_load.predict(test_features)
-
-            np.testing.assert_array_almost_equal(original_predicts, loaded_model_predicts)
-
-            # test loading warning
-            class FakeModel(SerializableModelMixin):
-                """Fake model class for test purposes."""
-
-                pass
-
-            with self.assertRaises(TypeError):
-                FakeModel.load(file_name)
-
-
-if __name__ == "__main__":
-    unittest.main()
+# This code is part of a Qiskit project.
+#
+# (C) Copyright IBM 2021, 2025.
+#
+# 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 QSVR on fidelity quantum kernel."""
+
+import os
+import tempfile
+import unittest
+
+from test import QiskitMachineLearningTestCase
+
+import numpy as np
+from sklearn.metrics import mean_squared_error
+
+from qiskit.primitives import Sampler
+from qiskit.circuit.library import ZZFeatureMap
+
+from qiskit_machine_learning.utils import algorithm_globals
+from qiskit_machine_learning.algorithms import QSVR, SerializableModelMixin
+from qiskit_machine_learning.exceptions import QiskitMachineLearningWarning
+from qiskit_machine_learning.kernels import FidelityQuantumKernel
+
+
+class TestQSVR(QiskitMachineLearningTestCase):
+    """Test QSVR Algorithm on fidelity quantum kernel."""
+
+    def setUp(self):
+        super().setUp()
+
+        algorithm_globals.random_seed = 10598
+
+        self.sampler = Sampler()
+        self.feature_map = ZZFeatureMap(feature_dimension=2, reps=2)
+
+        self.sample_train = np.asarray(
+            [
+                [-0.36572221, 0.90579879],
+                [-0.41816432, 0.03011426],
+                [-0.48806982, 0.87208714],
+                [-0.67078436, -0.91017876],
+                [-0.12980588, 0.98475113],
+                [0.78335453, 0.49721604],
+                [0.78158498, 0.78689328],
+                [0.03771672, -0.3681419],
+                [0.54402486, 0.32332253],
+                [-0.25268454, -0.81106666],
+            ]
+        )
+        self.label_train = np.asarray(
+            [
+                0.07045477,
+                0.80047778,
+                0.04493319,
+                -0.30427998,
+                -0.02430856,
+                0.17224315,
+                -0.26474769,
+                0.83097582,
+                0.60943777,
+                0.31577759,
+            ]
+        )
+
+        self.sample_test = np.asarray(
+            [
+                [-0.60713067, -0.37935265],
+                [0.55480968, 0.94365285],
+                [0.00148237, -0.71220499],
+                [-0.97212742, -0.54068794],
+            ]
+        )
+        self.label_test = np.asarray([0.45066614, -0.18052862, 0.4549451, -0.23674218])
+
+    def test_qsvr(self):
+        """Test QSVR"""
+        qkernel = FidelityQuantumKernel(feature_map=self.feature_map)
+        qsvr = QSVR(quantum_kernel=qkernel)
+        qsvr.fit(self.sample_train, self.label_train)
+
+        predictions = qsvr.predict(self.sample_test)
+        mse = mean_squared_error(self.label_test, predictions)
+        self.assertAlmostEqual(mse, 0.04964456790383482, places=4)
+
+    def test_change_kernel(self):
+        """Test QSVR with QuantumKernel later"""
+        qkernel = FidelityQuantumKernel(feature_map=self.feature_map)
+
+        qsvr = QSVR()
+        qsvr.quantum_kernel = qkernel
+        qsvr.fit(self.sample_train, self.label_train)
+
+        predictions = qsvr.predict(self.sample_test)
+        mse = mean_squared_error(self.label_test, predictions)
+        self.assertAlmostEqual(mse, 0.04964456790383482, places=4)
+
+    def test_qsvr_parameters(self):
+        """Test QSVR with extra constructor parameters"""
+        qkernel = FidelityQuantumKernel(feature_map=self.feature_map)
+
+        qsvr = QSVR(quantum_kernel=qkernel, tol=1e-3, C=1.0)
+        qsvr.fit(self.sample_train, self.label_train)
+
+        predictions = qsvr.predict(self.sample_test)
+        mse = mean_squared_error(self.label_test, predictions)
+        self.assertAlmostEqual(mse, 0.04964456790383482, places=4)
+
+    def test_qsvc_to_string(self):
+        """Test QSVR print works when no *args passed in"""
+        qsvr = QSVR()
+        _ = str(qsvr)
+
+    def test_with_kernel_parameter(self):
+        """Test QSVC with the `kernel` argument."""
+        with self.assertWarns(QiskitMachineLearningWarning):
+            QSVR(kernel=1)
+
+    def test_save_load(self):
+        """Tests save and load models."""
+        features = np.array([[0, 0], [0.1, 0.1], [0.4, 0.4], [1, 1]])
+        labels = np.array([0, 0.1, 0.4, 1])
+
+        quantum_kernel = FidelityQuantumKernel(feature_map=ZZFeatureMap(2))
+        regressor = QSVR(quantum_kernel=quantum_kernel)
+        regressor.fit(features, labels)
+
+        test_features = np.array([[0.5, 0.5]])
+        original_predicts = regressor.predict(test_features)
+
+        with tempfile.TemporaryDirectory() as dir_name:
+            file_name = os.path.join(dir_name, "qsvr.model")
+            regressor.save(file_name)
+
+            regressor_load = QSVR.load(file_name)
+            loaded_model_predicts = regressor_load.predict(test_features)
+
+            np.testing.assert_array_almost_equal(original_predicts, loaded_model_predicts)
+
+            class FakeModel(SerializableModelMixin):
+                """Fake model class for test purposes."""
+
+                pass
+
+            with self.assertRaises(TypeError):
+                FakeModel.load(file_name)
+
+
+if __name__ == "__main__":
+    unittest.main()