Source code for Orange.classification.random_forest

import sklearn.ensemble as skl_ensemble

from Orange.base import RandomForestModel
from Orange.classification import SklLearner, SklModel
from Orange.classification.tree import SklTreeClassifier
from Orange.data import Variable, DiscreteVariable
from Orange.preprocess.score import LearnerScorer

__all__ = ["RandomForestLearner"]


class _FeatureScorerMixin(LearnerScorer):
    feature_type = Variable
    class_type = DiscreteVariable

    def score(self, data):
        model = self(data)
        return model.skl_model.feature_importances_


class RandomForestClassifier(SklModel, RandomForestModel):
    @property
    def trees(self):
        def wrap(tree, i):
            t = SklTreeClassifier(tree)
            t.domain = self.domain
            t.supports_multiclass = self.supports_multiclass
            t.name = "{} - tree {}".format(self.name, i)
            t.original_domain = self.original_domain
            if hasattr(self, 'instances'):
                t.instances = self.instances
            return t

        return [wrap(tree, i)
                for i, tree in enumerate(self.skl_model.estimators_)]


[docs]class RandomForestLearner(SklLearner, _FeatureScorerMixin): __wraps__ = skl_ensemble.RandomForestClassifier __returns__ = RandomForestClassifier def __init__(self, n_estimators=10, criterion="gini", max_depth=None, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0., max_features="auto", max_leaf_nodes=None, bootstrap=True, oob_score=False, n_jobs=1, random_state=None, verbose=0, class_weight=None, preprocessors=None): super().__init__(preprocessors=preprocessors) self.params = vars()