Source code for gemseo_mlearning.adaptive.criteria.optimum.criterion_max

# 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 for the maximum.

Statistics:

.. math::

   EI[x] = E[\max(Y(x)-y_{max},0)]

where :math:`y_{max}=\max_{1\leq i \leq n}~y^{(i)}`.

Bootstrap estimator:

.. math::

   \widehat{EI}[x] = \frac{1}{B}\sum_{b=1}^B \max(Y_b(x)-f_{max},0)

"""
from __future__ import annotations

from numpy import ndarray

from gemseo_mlearning.adaptive.criterion import MLDataAcquisitionCriterion


class MaxExpectedImprovement(MLDataAcquisitionCriterion):
    """Expected Improvement of the regression model for the maximum.

    This criterion is scaled by the output range.
    """

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

            Args:
                input_data: The model input data.

            Returns:
                The acquisition criterion value.
            """
            data = self.algo_distribution.learning_set
            maximum_output = max(data.get_data_by_group(data.OUTPUT_GROUP))
            expected_improvement = self.algo_distribution.compute_expected_improvement(
                input_data, maximum_output, True
            )
            return expected_improvement / self.output_range

        return func