# 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
"""The mean squared error to measure the quality of a regression algorithm.

The :mod:~gemseo.mlearning.qual_measure.mse_measure module
implements the concept of mean 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 division, unicode_literals

from typing import Union

from numpy import ndarray
from sklearn.metrics import mean_squared_error

from gemseo.mlearning.qual_measure.error_measure import MLErrorMeasure
from gemseo.mlearning.regression.regression import MLRegressionAlgo

[docs]class MSEMeasure(MLErrorMeasure):
"""The Mean Squared Error measure for machine learning."""

def __init__(
self,
algo,  # type: MLRegressionAlgo
):  # type: (...) -> None
"""
Args:
algo: A machine learning algorithm for regression.
"""
super(MSEMeasure, self).__init__(algo)

def _compute_measure(
self,
outputs,  # type: ndarray
predictions,  # type: ndarray
multioutput=True,  # type: bool
):  # type: (...) -> Union[float,ndarray]
multioutput = "raw_values" if multioutput else "uniform_average"
return mean_squared_error(outputs, predictions, multioutput=multioutput)