# -*- 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: Matthias De Lozzo
# OTHER AUTHORS - MACROSCOPIC CHANGES
"""The root mean squared error to measure the quality of a regression algorithm.
The :mod:`~gemseo.mlearning.qual_measure.mse_measure` module
implements the concept of root mean squared error measures
for machine learning algorithms.
This concept is implemented through the
:class:`.RMSEMeasure` class and
overloads the :meth:`!MSEMeasure.evaluate_*` methods.
The root mean squared error (RMSE) is defined by
.. math::
\\operatorname{RMSE}(\\hat{y})=\\sqrt{\\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 Optional, Sequence, Union
from numpy import ndarray
from gemseo.core.dataset import Dataset
from gemseo.mlearning.qual_measure.mse_measure import MSEMeasure
from gemseo.mlearning.regression.regression import MLRegressionAlgo
[docs]class RMSEMeasure(MSEMeasure):
"""The root mean Squared Error measure for machine learning."""
def __init__(
self,
algo, # type: MLRegressionAlgo
): # type: (...) -> None
"""
Args:
algo: A machine learning algorithm for regression.
"""
super(RMSEMeasure, self).__init__(algo)
[docs] def evaluate_learn(
self,
samples=None, # type: Optional[Sequence[int]]
multioutput=True, # type: bool
): # type: (...) -> Union[float,ndarray]
mse = super(RMSEMeasure, self).evaluate_learn(
samples=samples, multioutput=multioutput
)
return mse ** 0.5
[docs] def evaluate_test(
self,
test_data, # type:Dataset
samples=None, # type: Optional[Sequence[int]]
multioutput=True, # type: bool
): # type: (...) -> Union[float,ndarray]
mse = super(RMSEMeasure, self).evaluate_test(
test_data, samples=samples, multioutput=multioutput
)
return mse ** 0.5
[docs] def evaluate_kfolds(
self,
n_folds=5, # type: int
samples=None, # type: Optional[Sequence[int]]
multioutput=True, # type: bool
randomize=False, # type:bool
): # type: (...) -> Union[float,ndarray]
mse = super(RMSEMeasure, self).evaluate_kfolds(
n_folds=n_folds,
samples=samples,
multioutput=multioutput,
randomize=randomize,
)
return mse ** 0.5
[docs] def evaluate_bootstrap(
self,
n_replicates=100, # type: int
samples=None, # type: Optional[Sequence[int]]
multioutput=True, # type: bool
): # type: (...) -> Union[float,ndarray]
mse = super(RMSEMeasure, self).evaluate_bootstrap(
n_replicates=n_replicates, samples=samples, multioutput=multioutput
)
return mse ** 0.5
