import numpy as np
import pandas as pd
from sklearn.preprocessing import LabelEncoder
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
[docs]class CatBoostClassifier(Estimator):
"""
CatBoost Classifier, a classifier 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 Classifier"
hyperparameter_ranges = {
"n_estimators": Integer(10, 1000),
"eta": Real(0.000001, 1),
"max_depth": Integer(4, 10),
}
model_family = ModelFamily.CATBOOST
supported_problem_types = [ProblemTypes.BINARY, ProblemTypes.MULTICLASS]
SEED_MIN = 0
SEED_MAX = SEED_BOUNDS.max_bound
[docs] def __init__(self, n_estimators=1000, eta=0.03, max_depth=6, bootstrap_type=None, random_state=0):
random_seed = get_random_seed(random_state, self.SEED_MIN, self.SEED_MAX)
parameters = {"n_estimators": n_estimators,
"eta": eta,
"max_depth": max_depth}
if bootstrap_type is not None:
parameters['bootstrap_type'] = bootstrap_type
cb_error_msg = "catboost is not installed. Please install using `pip install catboost.`"
catboost = import_or_raise("catboost", error_msg=cb_error_msg)
self._label_encoder = None
cb_classifier = catboost.CatBoostClassifier(**parameters,
random_seed=random_seed,
silent=True,
allow_writing_files=False)
super().__init__(parameters=parameters,
component_obj=cb_classifier,
random_state=random_state)
[docs] def fit(self, X, y=None):
cat_cols = X.select_dtypes(['category', 'object'])
# For binary classification, catboost expects numeric values, so encoding before.
if y.nunique() <= 2:
self._label_encoder = LabelEncoder()
y = pd.Series(self._label_encoder.fit_transform(y))
model = self._component_obj.fit(X, y, silent=True, cat_features=cat_cols)
return model
[docs] def predict(self, X):
predictions = self._component_obj.predict(X)
if self._label_encoder:
return self._label_encoder.inverse_transform(predictions.astype(np.int64))
return predictions
@property
def feature_importances(self):
return self._component_obj.get_feature_importance()