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 import_or_raise, infer_feature_types
[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]
[docs] def __init__(self, n_estimators=10, eta=0.03, max_depth=6, bootstrap_type=None, silent=False,
allow_writing_files=False, random_seed=0, **kwargs):
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_seed=random_seed)
[docs] def fit(self, X, y=None):
X = infer_feature_types(X)
cat_cols = list(X.select('category').columns)
self.input_feature_names = list(X.columns)
X, y = super()._manage_woodwork(X, y)
self._component_obj.fit(X, y, silent=True, cat_features=cat_cols)
return self
@property
def feature_importance(self):
return self._component_obj.get_feature_importance()