Source code for gemseo_mlearning.adaptive.criteria.value.criterion

# Copyright 2021 IRT Saint Exupéry, https://www.irt-saintexupery.com
#
# This program is free software; you can redistribute it and/or
# modify it under the terms of the GNU Lesser General Public
# License version 3 as published by the Free Software Foundation.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
# Lesser General Public License for more details.
#
# You should have received a copy of the GNU Lesser General Public License
# along with this program; if not, write to the Free Software Foundation,
# Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
#
# Contributors:
#    INITIAL AUTHORS - API and implementation and/or documentation
#        :author: Matthias De Lozzo
#    OTHER AUTHORS   - MACROSCOPIC CHANGES
r"""Expected improvement of the regression model for a particular value.

Statistics:

.. math::

   EI[x] = E[|q-Y(x)|]

where :math:`q` is a value provided by the user.

Bootstrap estimator:

.. math::

   \widehat{EI}[x] = \frac{1}{B}\sum_{b=1}^B |q-Y_b(x)|

"""
from __future__ import annotations

from typing import ClassVar

from numpy import ndarray

from gemseo_mlearning.adaptive.criterion import MLDataAcquisitionCriterion
from gemseo_mlearning.adaptive.distribution import MLRegressorDistribution


class LimitState(MLDataAcquisitionCriterion):
    """Expected Improvement of the regression model for a particular value."""

    value: float
    """The value of interest."""

    MAXIMIZE: ClassVar[bool] = False

    def __init__(
        self, algo_distribution: MLRegressorDistribution, value: float
    ) -> None:
        """# noqa: D205 D212 D415
        Args:
            value: A value of interest.
        """
        self.value = value
        super().__init__(algo_distribution)

    def _get_func(self):
        def func(input_data: ndarray) -> float:
            """Evaluation function.

            Args:
                input_data: The model input data.

            Returns:
                The acquisition criterion value.
            """
            mean = self.algo_distribution.compute_mean(input_data)
            std = self.algo_distribution.compute_standard_deviation(input_data)
            return abs(self.value - mean) / std

        return func