Source code for qiskit_machine_learning.algorithms.regressors.neural_network_regressor

# 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.
"""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


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)

        objective = self._get_objective(function)

        return self._optimizer.minimize(
            fun=objective,
            x0=self._choose_initial_point(),
            jac=function.gradient,
        )

    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)

    def score(
        self, X: np.ndarray, y: np.ndarray, sample_weight: Optional[np.ndarray] = None
    ) -> float:
        return RegressorMixin.score(self, X, y, sample_weight)