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qiskit_machine_learning.utils.loss_functions.kernel_loss_functions のソースコード

# 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 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.

""" Kernel Loss utilities """

from abc import ABC, abstractmethod
from typing import Sequence

import numpy as np
from sklearn.svm import SVC

# Prevent circular dependencies caused from type checking
from ...kernels import TrainableKernel


[ドキュメント]class KernelLoss(ABC): """ Abstract base class for computing the loss of a kernel function. Unlike many loss functions, which only take into account the labels and predictions of a model, kernel loss functions may be a function of internal model parameters or quantities that are generated during training. """
[ドキュメント] def __call__( self, parameter_values: Sequence[float], quantum_kernel: TrainableKernel, data: np.ndarray, labels: np.ndarray, ) -> float: """ This method calls the ``evaluate`` method. This is a convenient method to compute loss. """ return self.evaluate(parameter_values, quantum_kernel, data, labels)
[ドキュメント] @abstractmethod def evaluate( self, parameter_values: Sequence[float], quantum_kernel: TrainableKernel, data: np.ndarray, labels: np.ndarray, ) -> float: """ An abstract method for evaluating the loss of a kernel function on a labeled dataset. Args: parameter_values: an array of values to assign to the user params quantum_kernel: A ``QuantumKernel`` object to evaluate data: An ``(N, M)`` matrix containing the data ``N = # samples, M = dimension of data`` labels: A length-N array containing the truth labels Returns: A loss value """ raise NotImplementedError
[ドキュメント]class SVCLoss(KernelLoss): r""" This class provides a kernel loss function for classification tasks by fitting an ``SVC`` model from scikit-learn. Given training samples, :math:`x_{i}`, with binary labels, :math:`y_{i}`, and a kernel, :math:`K_{θ}`, parameterized by values, :math:`θ`, the loss is defined as: .. math:: SVCLoss = \sum_{i} a_i - 0.5 \sum_{i,j} a_i a_j y_{i} y_{j} K_θ(x_i, x_j) where :math:`a_i` are the optimal Lagrange multipliers found by solving the standard SVM quadratic program. Note that the hyper-parameter ``C`` for the soft-margin penalty can be specified through the keyword args. Minimizing this loss over the parameters, :math:`θ`, of the kernel is equivalent to maximizing a weighted kernel alignment, which in turn yields the smallest upper bound to the SVM generalization error for a given parameterization. See https://arxiv.org/abs/2105.03406 for further details. """ def __init__(self, **kwargs): """ Args: **kwargs: Arbitrary keyword arguments to pass to SVC constructor within SVCLoss evaluation. """ self.kwargs = kwargs
[ドキュメント] def evaluate( self, parameter_values: Sequence[float], quantum_kernel: TrainableKernel, data: np.ndarray, labels: np.ndarray, ) -> float: # Bind training parameters quantum_kernel.assign_training_parameters(parameter_values) # Get estimated kernel matrix kmatrix = quantum_kernel.evaluate(np.array(data)) # Train a quantum support vector classifier svc = SVC(kernel="precomputed", **self.kwargs) svc.fit(kmatrix, labels) # Get dual coefficients dual_coefs = svc.dual_coef_[0] # Get support vectors support_vecs = svc.support_ # Prune kernel matrix of non-support-vector entries kmatrix = kmatrix[support_vecs, :][:, support_vecs] # Calculate loss loss = np.sum(np.abs(dual_coefs)) - (0.5 * (dual_coefs.T @ kmatrix @ dual_coefs)) return loss

© Copyright 2018, 2022, Qiskit Machine Learning Development Team.

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