qiskit_machine_learning.algorithms.classifiers.qsvc öğesinin kaynak kodu

# This code is part of a Qiskit project.
# (C) Copyright IBM 2021, 2023.
# 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
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"""Quantum Support Vector Classifier"""

import warnings
from typing import Optional

from qiskit_algorithms.utils import algorithm_globals
from sklearn.svm import SVC

from qiskit_machine_learning.algorithms.serializable_model import SerializableModelMixin
from qiskit_machine_learning.exceptions import QiskitMachineLearningWarning
from qiskit_machine_learning.kernels import BaseKernel, FidelityQuantumKernel

[belgeler]class QSVC(SVC, SerializableModelMixin): r"""Quantum Support Vector Classifier that extends the scikit-learn `sklearn.svm.SVC <https://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html>`_ classifier and introduces an additional `quantum_kernel` parameter. This class shows how to use a quantum kernel for classification. The class inherits its methods like ``fit`` and ``predict`` from scikit-learn, see the example below. Read more in the `scikit-learn user guide <https://scikit-learn.org/stable/modules/svm.html#svm-classification>`_. **Example** .. code-block:: qsvc = QSVC(quantum_kernel=qkernel) qsvc.fit(sample_train,label_train) qsvc.predict(sample_test) """ def __init__(self, *, quantum_kernel: Optional[BaseKernel] = None, **kwargs): """ Args: quantum_kernel: A quantum kernel to be used for classification. Has to be ``None`` when a precomputed kernel is used. If None, default to :class:`~qiskit_machine_learning.kernels.FidelityQuantumKernel`. *args: Variable length argument list to pass to SVC constructor. **kwargs: Arbitrary keyword arguments to pass to SVC constructor. """ if "kernel" in kwargs: msg = ( "'kernel' argument is not supported and will be discarded, " "please use 'quantum_kernel' instead." ) warnings.warn(msg, QiskitMachineLearningWarning, stacklevel=2) # if we don't delete, then this value clashes with our quantum kernel del kwargs["kernel"] self._quantum_kernel = quantum_kernel if quantum_kernel else FidelityQuantumKernel() if "random_state" not in kwargs: kwargs["random_state"] = algorithm_globals.random_seed super().__init__(kernel=self._quantum_kernel.evaluate, **kwargs) @property def quantum_kernel(self) -> BaseKernel: """Returns quantum kernel""" return self._quantum_kernel @quantum_kernel.setter def quantum_kernel(self, quantum_kernel: BaseKernel): """Sets quantum kernel""" self._quantum_kernel = quantum_kernel self.kernel = self._quantum_kernel.evaluate # we override this method to be able to pretty print this instance @classmethod def _get_param_names(cls): names = SVC._get_param_names() names.remove("kernel") return sorted(names + ["quantum_kernel"])