Source code for asreview.models.classifiers.rf

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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 RandomState, 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(RandomForestClassifier, self).__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)
[docs] def full_hyper_space(self): from hyperopt import hp hyper_choices = {} hyper_space = { "mdl_n_estimators": hp.quniform("mdl_n_estimators", 10, 100, 1), "mdl_max_features": hp.quniform("mdl_max_features", 6, 10, 1), "mdl_class_weight": hp.lognormal('mdl_class_weight', 0, 1), } return hyper_space, hyper_choices