Source code for gemseo.mlearning.classification.random_forest

# -*- coding: utf-8 -*-
# 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: Francois Gallard, Matthias De Lozzo, Syver Doving Agdestein
#    OTHER AUTHORS   - MACROSCOPIC CHANGES
"""The random forest algorithm for classification.

The random forest classification model uses averaging methods on an ensemble
of decision trees.

Dependence
----------
The classifier relies on the RandomForestClassifier class
of the `scikit-learn library <https://scikit-learn.org/stable/modules/
generated/sklearn.ensemble.RandomForestClassifier.html>`_.
"""
from __future__ import division, unicode_literals

import logging
from typing import Iterable, Optional, Union

from numpy import ndarray, stack
from sklearn.ensemble import RandomForestClassifier as SKLRandForest

from gemseo.core.dataset import Dataset
from gemseo.mlearning.classification.classification import MLClassificationAlgo
from gemseo.mlearning.core.ml_algo import TransformerType

LOGGER = logging.getLogger(__name__)


[docs]class RandomForestClassifier(MLClassificationAlgo): """The random forest classification algorithm.""" LIBRARY = "scikit-learn" ABBR = "RandomForestClassifier" def __init__( self, data, # type: Dataset transformer=None, # type: Optional[TransformerType] input_names=None, # type: Optional[Iterable[str]] output_names=None, # type: Optional[Iterable[str]] n_estimators=100, # type: int **parameters # type: Optional[Union[int,float,bool,str]] ): # type: (...) -> None """ Args: n_estimators: The number of trees in the forest. """ super(RandomForestClassifier, self).__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, # type:ndarray output_data, # type:ndarray ): # type: (...) -> None if output_data.shape[1] == 1: output_data = output_data.ravel() self.algo.fit(input_data, output_data) def _predict( self, input_data, # type:ndarray ): # type: (...) -> ndarray output_data = self.algo.predict(input_data).astype(int) if len(output_data.shape) == 1: output_data = output_data[:, None] return output_data def _predict_proba_soft( self, input_data, # type: ndarray ): # type: (...)-> ndarray probas = self.algo.predict_proba(input_data) if len(probas[0].shape) == 1: probas = probas[..., None] else: probas = stack(probas, axis=-1) return probas