Source code for gemseo.mlearning.qual_measure.mse_measure
# -*- coding: utf-8 -*-
# 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 - initial API and implementation and/or initial
# documentation
# :author: Syver Doving Agdestein
# OTHER AUTHORS - MACROSCOPIC CHANGES
"""
Mean squared error measure
==========================
The :mod:`~gemseo.mlearning.qual_measure.mse_measure` module
implements the concept of means squared error measures
for machine learning algorithms.
This concept is implemented through the
:class:`.MSEMeasure` class and
overloads the :meth:`!MLErrorMeasure._compute_measure` method.
The mean squared error (MSE) is defined by
.. math::
\\operatorname{MSE}(\\hat{y})=\\frac{1}{n}\\sum_{i=1}^n(\\hat{y}_i-y_i)^2,
where
:math:`\\hat{y}` are the predictions and
:math:`y` are the data points.
"""
from __future__ import absolute_import, division, unicode_literals
from future import standard_library
from sklearn.metrics import mean_squared_error
from gemseo.mlearning.qual_measure.error_measure import MLErrorMeasure
standard_library.install_aliases()
[docs]class MSEMeasure(MLErrorMeasure):
""" Mean Squared Error measure for machine learning. """
def _compute_measure(self, outputs, predictions, multioutput=True):
"""Compute MSE.
:param ndarray outputs: reference outputs.
:param ndarray predictions: predicted outputs.
:param bool multioutput: if True, return the error measure for each
output component. Otherwise, average these errors. Default: True.
:return: MSE value.
"""
multioutput = "raw_values" if multioutput else "uniform_average"
return mean_squared_error(outputs, predictions, multioutput=multioutput)