gemseo.mlearning.regression.random_forest — GEMSEO 4.0.1 documentation

# 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

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

from numpy import ndarray
from sklearn.ensemble import RandomForestRegressor as SKLRandForest

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

LOGGER = logging.getLogger(__name__)


[docs]class RandomForestRegressor(MLRegressionAlgo):
    """Random forest regression."""

    SHORT_ALGO_NAME: ClassVar[str] = "RF"
    LIBRARY: Final[str] = "scikit-learn"

    def __init__(
        self,
        data: Dataset,
        transformer: Mapping[str, TransformerType] | None = None,
        input_names: Iterable[str] | None = None,
        output_names: Iterable[str] | None = None,
        n_estimators: int = 100,
        **parameters,
    ) -> None:
        """
        Args:
            n_estimators: The number of trees in the forest.
        """
        super().__init__(
            data,
            transformer=transformer,
            input_names=input_names,
            output_names=output_names,
            n_estimators=n_estimators,
            **parameters,
        )
        self.algo = SKLRandForest(n_estimators=n_estimators, **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:
        output_data = self.algo.predict(input_data)

        # n_outputs=1 => output_shape=(n_samples,). Convert to (n_samples, 1).
        if len(output_data.shape) == 1:
            output_data = output_data[:, None]

        return output_data
Source code for gemseo.mlearning.regression.random_forest
