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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_, model.domain.attributes
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_)]
class RandomForestLearner(SklLearner, _FeatureScorerMixin):
__wraps__ = skl_ensemble.RandomForestClassifier
__returns__ = RandomForestClassifier
supports_weights = True
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="sqrt",
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()
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