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from typing import Tuple
import numpy as np
import catboost
from Orange.base import CatGBBaseLearner, CatGBModel
from Orange.classification import Learner, Model
from Orange.data import Variable, DiscreteVariable, Table
from Orange.preprocess.score import LearnerScorer
__all__ = ["CatGBClassifier"]
class _FeatureScorerMixin(LearnerScorer):
feature_type = Variable
class_type = DiscreteVariable
def score(self, data: Table) -> Tuple[np.ndarray, Tuple[Variable]]:
model: CatGBClassifier = self(data)
return model.cat_model.feature_importances_, model.domain.attributes
class CatGBClsModel(CatGBModel, Model):
pass
class CatGBClassifier(CatGBBaseLearner, Learner, _FeatureScorerMixin):
__wraps__ = catboost.CatBoostClassifier
__returns__ = CatGBClsModel
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