# 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
# that they have been altered from the originals.
"""An implementation of quantum neural network regressor."""
from typing import Optional
import numpy as np
from qiskit_algorithms.optimizers import OptimizerResult
from sklearn.base import RegressorMixin
from ..objective_functions import (
BinaryObjectiveFunction,
MultiClassObjectiveFunction,
ObjectiveFunction,
)
from ..trainable_model import TrainableModel
[documentos]class NeuralNetworkRegressor(TrainableModel, RegressorMixin):
"""Implements a basic quantum neural network regressor. Implements Scikit-Learn compatible
methods for regression and extends ``RegressorMixin``.
See `Scikit-Learn <https://scikit-learn.org>`__ for more details.
"""
def _fit_internal(
self, X: np.ndarray, y: np.ndarray
) -> OptimizerResult: # pylint: disable=invalid-name
# mypy definition
function: ObjectiveFunction = None
if self._neural_network.output_shape == (1,):
function = BinaryObjectiveFunction(X, y, self._neural_network, self._loss)
else:
function = MultiClassObjectiveFunction(X, y, self._neural_network, self._loss)
return self._minimize(function)
[documentos] def predict(self, X: np.ndarray) -> np.ndarray: # pylint: disable=invalid-name
self._check_fitted()
return self._neural_network.forward(X, self._fit_result.x)
[documentos] def score(
self, X: np.ndarray, y: np.ndarray, sample_weight: Optional[np.ndarray] = None
) -> float:
return RegressorMixin.score(self, X, y, sample_weight)