Source code for gemseo_mlearning.regression.gradient_boosting

# 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
#    OTHER AUTHORS   - MACROSCOPIC CHANGES
"""The gradient boosting for regression.

The gradient boosting model relies on the ``GradientBoostingRegressor`` class
of the `scikit-learn library <https://scikit-learn.org/stable/modules/
generated/sklearn.ensemble.GradientBoostingRegressor.html>`_.
"""
from __future__ import annotations

import logging
from typing import ClassVar
from typing import Iterable
from typing import Mapping

from gemseo.core.dataset import Dataset
from gemseo.mlearning.core.ml_algo import TransformerType
from gemseo.mlearning.regression.regression import MLRegressionAlgo
from gemseo.utils.python_compatibility import Final
from numpy import array
from numpy import ndarray
from sklearn.ensemble import GradientBoostingRegressor as SKLGradientBoosting

LOGGER = logging.getLogger(__name__)


[docs]class GradientBoostingRegressor(MLRegressionAlgo): """Gradient boosting regression.""" LIBRARY: Final[str] = "scikit-learn" SHORT_ALGO_NAME: ClassVar[str] = "GradientBoostingRegressor" def __init__( self, data: Dataset, transformer: Mapping[str, TransformerType] | None = None, input_names: Iterable[str] = None, output_names: Iterable[str] = None, n_estimators: int = 100, **parameters, ) -> None: """# noqa: D205 D212 D415 Args: n_estimators: The number of boosting stages to perform. """ super().__init__( data, transformer=transformer, input_names=input_names, output_names=output_names, n_estimators=n_estimators, **parameters, ) self.__algo = {"n_estimators": n_estimators, "parameters": parameters} self.algo = [] def _fit( self, input_data: ndarray, output_data: ndarray, ) -> None: for _output_data in output_data.T: self.algo.append( SKLGradientBoosting( n_estimators=self.__algo["n_estimators"], **self.__algo["parameters"], ) ) self.algo[-1].fit(input_data, _output_data) def _predict( self, input_data: ndarray, ) -> ndarray: return array([algo.predict(input_data) for algo in self.algo]).T