# Source code for gemseo.mlearning.qual_measure.f1_measure

# 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 F1 to measure the quality of a classification algorithm.
The F1 is defined by
.. math::
F_1 = 2\\frac{\\mathit{precision}\\mathit{recall}}
{\\mathit{precision}+\\mathit{recall}}
where
:math:`\\mathit{precision}` is the number of correctly predicted positives
divided by the total number of *predicted* positives
and :math:`\\mathit{recall}` is the number of correctly predicted positives
divided by the total number of *true* positives.
"""
from __future__ import annotations
from numpy import ndarray
from sklearn.metrics import f1_score
from gemseo.mlearning.classification.classification import MLClassificationAlgo
from gemseo.mlearning.qual_measure.error_measure import MLErrorMeasure
[docs]class F1Measure(MLErrorMeasure):
"""The F1 measure for machine learning."""
SMALLER_IS_BETTER = False
def __init__(
self,
algo: MLClassificationAlgo,
fit_transformers: bool = MLErrorMeasure._FIT_TRANSFORMERS,
) -> None:
"""
Args:
algo: A machine learning algorithm for classification.
"""
super().__init__(algo, fit_transformers=fit_transformers)
def _compute_measure(
self,
outputs: ndarray,
predictions: ndarray,
multioutput: bool = True,
) -> float | ndarray:
if multioutput:
raise NotImplementedError("F1 is only defined for single target.")
return f1_score(outputs, predictions, average="weighted")