1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
|
# pylint: disable=unused-argument,too-many-arguments
from typing import Tuple
import numpy as np
import sklearn.ensemble as skl_ensemble
from Orange.classification import SklLearner, SklModel
from Orange.data import Variable, DiscreteVariable, Table
from Orange.preprocess.score import LearnerScorer
__all__ = ["GBClassifier"]
class _FeatureScorerMixin(LearnerScorer):
feature_type = Variable
class_type = DiscreteVariable
def score(self, data: Table) -> Tuple[np.ndarray, Tuple[Variable]]:
model: GBClassifier = self(data)
return model.skl_model.feature_importances_, model.domain.attributes
class GBClassifier(SklLearner, _FeatureScorerMixin):
__wraps__ = skl_ensemble.GradientBoostingClassifier
__returns__ = SklModel
supports_weights = True
def __init__(self,
loss="log_loss",
learning_rate=0.1,
n_estimators=100,
subsample=1.0,
criterion="friedman_mse",
min_samples_split=2,
min_samples_leaf=1,
min_weight_fraction_leaf=0.,
max_depth=3,
min_impurity_decrease=0.,
min_impurity_split=None,
init=None,
random_state=None,
max_features=None,
verbose=0,
max_leaf_nodes=None,
warm_start=False,
presort="deprecated",
validation_fraction=0.1,
n_iter_no_change=None,
tol=1e-4,
ccp_alpha=0.0,
preprocessors=None):
super().__init__(preprocessors=preprocessors)
self.params = vars()
|
