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 typing import Final
from sklearn.ensemble import RandomForestRegressor as SKLRandForest
from gemseo.mlearning.regression.regression import MLRegressionAlgo
from gemseo.utils.seeder import SEED
if TYPE_CHECKING:
from collections.abc import Iterable
from numpy import ndarray
from gemseo.datasets.io_dataset import IODataset
from gemseo.mlearning.core.ml_algo import TransformerType
[docs]
class RandomForestRegressor(MLRegressionAlgo):
"""Random forest regression."""
SHORT_ALGO_NAME: ClassVar[str] = "RF"
LIBRARY: Final[str] = "scikit-learn"
def __init__(
self,
data: IODataset,
transformer: TransformerType = MLRegressionAlgo.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: ndarray,
output_data: ndarray,
) -> None:
# SKLearn RandomForestReressor 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: ndarray,
) -> ndarray:
return self.algo.predict(input_data).reshape((len(input_data), -1))