Source code for evalml.pipelines.components.estimators.regressors.catboost_regressor

import copy

from skopt.space import Integer, Real

from evalml.model_family import ModelFamily
from evalml.pipelines.components.estimators import Estimator
from evalml.problem_types import ProblemTypes
from evalml.utils import SEED_BOUNDS, get_random_seed, import_or_raise
from evalml.utils.gen_utils import (
    _convert_to_woodwork_structure,
    _convert_woodwork_types_wrapper
)


[docs]class CatBoostRegressor(Estimator): """ CatBoost Regressor, a regressor that uses gradient-boosting on decision trees. CatBoost is an open-source library and natively supports categorical features. For more information, check out https://catboost.ai/ """ name = "CatBoost Regressor" hyperparameter_ranges = { "n_estimators": Integer(4, 100), "eta": Real(0.000001, 1), "max_depth": Integer(4, 10), } model_family = ModelFamily.CATBOOST supported_problem_types = [ProblemTypes.REGRESSION, ProblemTypes.TIME_SERIES_REGRESSION] SEED_MIN = 0 SEED_MAX = SEED_BOUNDS.max_bound
[docs] def __init__(self, n_estimators=10, eta=0.03, max_depth=6, bootstrap_type=None, silent=False, allow_writing_files=False, random_state=0, **kwargs): random_seed = get_random_seed(random_state, self.SEED_MIN, self.SEED_MAX) parameters = {"n_estimators": n_estimators, "eta": eta, "max_depth": max_depth, 'bootstrap_type': bootstrap_type, 'silent': silent, 'allow_writing_files': allow_writing_files} parameters.update(kwargs) cb_error_msg = "catboost is not installed. Please install using `pip install catboost.`" catboost = import_or_raise("catboost", error_msg=cb_error_msg) # catboost will choose an intelligent default for bootstrap_type, so only set if provided cb_parameters = copy.copy(parameters) if bootstrap_type is None: cb_parameters.pop('bootstrap_type') cb_regressor = catboost.CatBoostRegressor(**cb_parameters, random_seed=random_seed) super().__init__(parameters=parameters, component_obj=cb_regressor, random_state=random_state)
[docs] def fit(self, X, y=None): X = _convert_to_woodwork_structure(X) cat_cols = list(X.select('category').columns) X = _convert_woodwork_types_wrapper(X.to_dataframe()) y = _convert_to_woodwork_structure(y) y = _convert_woodwork_types_wrapper(y.to_series()) model = self._component_obj.fit(X, y, silent=True, cat_features=cat_cols) return model
@property def feature_importance(self): return self._component_obj.get_feature_importance()