Source code for gemseo_mlearning.adaptive.criteria.quantile.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"""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 typing import TYPE_CHECKING
from numpy import quantile
from gemseo_mlearning.adaptive.criteria.value.criterion import LimitState
if TYPE_CHECKING:
from gemseo_mlearning.adaptive.distribution import MLRegressorDistribution
[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_view(group_names=dataset.OUTPUT_GROUP), level
)
super().__init__(algo_distribution, limit_state)