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

# Copyright 2021 IRT Saint Exupéry, https://www.irt-saintexupery.com
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# This program is free software; you can redistribute it and/or
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# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
# Lesser General Public License for more details.
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# You should have received a copy of the GNU Lesser General Public License
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# Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
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# Contributors:
#    INITIAL AUTHORS - API and implementation and/or documentation
#        :author: Matthias De Lozzo
#    OTHER AUTHORS   - MACROSCOPIC CHANGES
r"""Variance of the regression model.

Statistics:

.. math::

   V[x] = E[(Y(x)-E[Y(x)])^2]

Bootstrap estimator:

.. math::

   \widehat{V}[x] = \frac{1}{B-1}\sum_{b=1}^B (Y_b(x)-\widehat{E}[x])^2

where :math:`\widehat{E}[x]= \frac{1}{B}\sum_{b=1}^B Y_b(x)`.
"""
from __future__ import annotations

from numpy import ndarray

from gemseo_mlearning.adaptive.criterion import MLDataAcquisitionCriterion


[docs]class Variance(MLDataAcquisitionCriterion): """Variance of the regression model. 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. """ variance = self.algo_distribution.compute_variance(input_data) return variance / self.output_range**2 return func