# 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"""Quantile of the regression model.

Statistics:

.. math::

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

where :math:q is a quantile with level :math:\alpha.

Bootstrap estimator:

.. math::

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

"""
from __future__ import annotations

from numpy import quantile

[docs]class Quantile(LimitState):
"""Expected Improvement of the regression model for a given quantile."""

def __init__(
self, algo_distribution: MLRegressorDistribution, level: float
) -> None:
"""# noqa: D205 D212 D415
Args:
level: A quantile level.
"""
dataset = algo_distribution.learning_set
limit_state = quantile(dataset.get_data_by_group(dataset.OUTPUT_GROUP), level)
super().__init__(algo_distribution, limit_state)