Source code for modeva.models.wrappers.builtin.catboost

from catboost import CatBoostRegressor, CatBoostClassifier

from .fanova_base import FANOVATreeInterpretBase
from ..sklearn import MoSKLearnRegressor, MoSKLearnClassifier


[docs] class MoCatBoostRegressor(MoSKLearnRegressor, FANOVATreeInterpretBase): """ A lightweight wrapper of `catboost.CatBoostRegressor <https://catboost.ai/docs/en/concepts/python-reference_catboostregressor>`_. Parameters ---------- name : str, default=None Identifier for the model instance. *args Variable length argument list passed to the underlying CatBoostRegressor model. **kwargs Arbitrary keyword arguments passed to the underlying CatBoostRegressor model. """ def __init__(self, name: str = None, *args, **kwargs): super().__init__(name=name, estimator=CatBoostRegressor(*args, **kwargs))
[docs] def get_params(self, deep=True): """ Get parameters for this estimator. Parameters ---------- deep : bool, default=True If True, will return the parameters for this estimator and contained subobjects that are estimators. Returns ------- params : dict Parameter names mapped to their values. """ return self.estimator.get_params(deep=deep)
[docs] class MoCatBoostClassifier(MoSKLearnClassifier, FANOVATreeInterpretBase): """ A lightweight wrapper of `catboost.CatBoostClassifier <https://catboost.ai/docs/en/concepts/python-reference_catboostclassifier>`_. Parameters ---------- name : str, default=None Identifier for the model instance. *args Variable length argument list passed to the underlying CatBoostClassifier model. **kwargs Arbitrary keyword arguments passed to the underlying CatBoostClassifier model. """ def __init__(self, name: str = None, *args, **kwargs): super().__init__(name=name, estimator=CatBoostClassifier(*args, **kwargs))
[docs] def get_params(self, deep=True): """ Get parameters for this estimator. Parameters ---------- deep : bool, default=True If True, will return the parameters for this estimator and contained subobjects that are estimators. Returns ------- params : dict Parameter names mapped to their values. """ return self.estimator.get_params(deep=deep)