# 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
#
# 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 Callable
from typing import ClassVar

from numpy.typing import NDArray

[docs]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) -> Callable[[NDArray[float]], float]:
def func(input_data: NDArray[float]) -> 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