# 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
r"""The root mean squared error to assess the quality of a regressor.
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 annotations
from typing import TYPE_CHECKING
from gemseo.mlearning.regression.quality.mse_measure import MSEMeasure
if TYPE_CHECKING:
from collections.abc import Sequence
from gemseo.datasets.io_dataset import IODataset
from gemseo.mlearning.core.quality.base_ml_algo_quality import MeasureType
from gemseo.mlearning.regression.algos.base_regressor import BaseRegressor
[docs]
class RMSEMeasure(MSEMeasure):
"""The root mean squared error to assess the quality of a regressor."""
def __init__(
self,
algo: BaseRegressor,
fit_transformers: bool = MSEMeasure._FIT_TRANSFORMERS,
) -> None:
"""
Args:
algo: A machine learning algorithm for regression.
""" # noqa: D205 D212
super().__init__(algo, fit_transformers=fit_transformers)
[docs]
def compute_learning_measure( # noqa: D102
self,
samples: Sequence[int] = (),
multioutput: bool = True,
as_dict: bool = False,
) -> MeasureType:
return self.__post_process_measure(
super().compute_learning_measure(
samples=samples, multioutput=multioutput, as_dict=as_dict
)
)
[docs]
def compute_test_measure( # noqa: D102
self,
test_data: IODataset,
samples: Sequence[int] = (),
multioutput: bool = True,
as_dict: bool = False,
) -> MeasureType:
return self.__post_process_measure(
super().compute_test_measure(
test_data, samples=samples, multioutput=multioutput, as_dict=as_dict
)
)
[docs]
def compute_cross_validation_measure( # noqa: D102
self,
n_folds: int = 5,
samples: Sequence[int] = (),
multioutput: bool = True,
randomize: bool = MSEMeasure._RANDOMIZE,
seed: int | None = None,
as_dict: bool = False,
store_resampling_result: bool = False,
) -> MeasureType:
return self.__post_process_measure(
super().compute_cross_validation_measure(
n_folds=n_folds,
samples=samples,
multioutput=multioutput,
randomize=randomize,
seed=seed,
as_dict=as_dict,
store_resampling_result=store_resampling_result,
)
)
[docs]
def compute_bootstrap_measure( # noqa: D102
self,
n_replicates: int = 100,
samples: Sequence[int] = (),
multioutput: bool = True,
seed: int | None = None,
as_dict: bool = False,
store_resampling_result: bool = False,
) -> MeasureType:
return self.__post_process_measure(
super().compute_bootstrap_measure(
n_replicates=n_replicates,
samples=samples,
multioutput=multioutput,
as_dict=as_dict,
store_resampling_result=store_resampling_result,
),
)
@staticmethod
def __post_process_measure(measure: MeasureType) -> MeasureType:
"""Post-process the measure.
Args:
measure: The measure to post-process.
Returns:
The post-processed measure.
"""
if isinstance(measure, dict):
return {k: v**0.5 for k, v in measure.items()}
return measure**0.5
