Código fuente para qiskit_machine_learning.algorithms.regressors.qsvr

# This code is part of Qiskit.
# (C) Copyright IBM 2021, 2022.
# 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
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"""Quantum Support Vector Regressor"""

import warnings
from typing import Optional

from sklearn.svm import SVR

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

[documentos]class QSVR(SVR, SerializableModelMixin): r"""Quantum Support Vector Regressor that extends the scikit-learn `sklearn.svm.SVR <>`_ regressor and introduces an additional `quantum_kernel` parameter. This class shows how to use a quantum kernel for regression. The class inherits its methods like ``fit`` and ``predict`` from scikit-learn, see the example below. Read more in the `scikit-learn user guide <>`_. **Example** .. code-block:: qsvr = QSVR(quantum_kernel=qkernel),label_train) qsvr.predict(sample_test) """ def __init__(self, *args, quantum_kernel: Optional[BaseKernel] = None, **kwargs): """ Args: quantum_kernel: Quantum kernel to be used for regression. *args: Variable length argument list to pass to SVR constructor. **kwargs: Arbitrary keyword arguments to pass to SVR constructor. """ if (len(args)) != 0: msg = ( f"Positional arguments ({args}) are deprecated as of version 0.3.0 and " f"will be removed no sooner than 3 months after the release. Instead use " f"keyword arguments." ) warnings.warn(msg, DeprecationWarning, stacklevel=2) 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() super().__init__(kernel=self._quantum_kernel.evaluate, *args, **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 = SVR._get_param_names() names.remove("kernel") return sorted(names + ["quantum_kernel"])