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