Source code for gemseo.mlearning.regression.random_forest

# 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
"""Random forest regression model.

Use an ensemble of decision trees.

Dependence
----------
The regression model relies on the ``RandomForestRegressor`` class
of the `scikit-learn library <https://scikit-learn.org/stable/modules/
generated/sklearn.ensemble.RandomForestRegressor.html>`_.
"""

from __future__ import annotations

from typing import TYPE_CHECKING
from typing import ClassVar

from sklearn.ensemble import RandomForestRegressor as SKLRandForest

from gemseo.mlearning.regression.regression import BaseMLRegressionAlgo
from gemseo.utils.seeder import SEED

if TYPE_CHECKING:
    from collections.abc import Iterable

    from gemseo.datasets.io_dataset import IODataset
    from gemseo.mlearning.core.ml_algo import TransformerType
    from gemseo.typing import RealArray


[docs] class RandomForestRegressor(BaseMLRegressionAlgo): """Random forest regression.""" SHORT_ALGO_NAME: ClassVar[str] = "RF" LIBRARY: ClassVar[str] = "scikit-learn" def __init__( self, data: IODataset, transformer: TransformerType = BaseMLRegressionAlgo.IDENTITY, input_names: Iterable[str] | None = None, output_names: Iterable[str] | None = None, n_estimators: int = 100, random_state: int | None = SEED, **parameters, ) -> None: """ Args: n_estimators: The number of trees in the forest. random_state: The random state passed to the random number generator. Use an integer for reproducible results. """ # noqa: D205 D212 super().__init__( data, transformer=transformer, input_names=input_names, output_names=output_names, n_estimators=n_estimators, random_state=random_state, **parameters, ) self.algo = SKLRandForest( n_estimators=n_estimators, random_state=random_state, **parameters ) def _fit( self, input_data: RealArray, output_data: RealArray, ) -> None: # SKLearn RandomForestRegressor does not like output # shape (n_samples, 1), prefers (n_samples,). # The shape (n_samples, n_outputs) with n_outputs >= 2 is fine. if output_data.shape[1] == 1: output_data = output_data[:, 0] self.algo.fit(input_data, output_data) def _predict( self, input_data: RealArray, ) -> RealArray: return self.algo.predict(input_data).reshape((len(input_data), -1))