# Copyright 2019-2022 The ASReview Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
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__all__ = ["RandomForestClassifier"]
from sklearn.ensemble import RandomForestClassifier as SKRandomForestClassifier
from asreview.models.classifiers.base import BaseTrainClassifier
from asreview.models.classifiers.utils import _set_class_weight
[docs]
class RandomForestClassifier(BaseTrainClassifier):
"""
Random forest classifier (``rf``).
The Random Forest classifier is an implementation based
on the sklearn Random Forest classifier.
Arguments
---------
n_estimators : int, default=100
The number of trees in the forest.
max_features: int, default=10
Number of features in the model.
class_weight: float, default=1.0
Class weight of the inclusions.
random_state : int or asreview.utils.SeededRandomState, default=None
Controls both the randomness of the bootstrapping of the samples used
when building trees and the sampling of the features to consider when
looking for the best split at each node.
"""
name = "rf"
label = "Random forest"
def __init__(
self, n_estimators=100, max_features=10, class_weight=1.0, random_state=None
):
super().__init__()
self.n_estimators = int(n_estimators)
self.max_features = int(max_features)
self.class_weight = class_weight
self._random_state = random_state
self._model = SKRandomForestClassifier(
n_estimators=self.n_estimators,
max_features=self.max_features,
class_weight=_set_class_weight(class_weight),
random_state=random_state,
)