Source code for evalml.pipelines.components.estimators.classifiers.catboost_classifier

import copy

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
from evalml.utils.gen_utils import (
    _convert_to_woodwork_structure,
    _convert_woodwork_types_wrapper
)


[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(4, 100), "eta": Real(0.000001, 1), "max_depth": Integer(4, 10), } model_family = ModelFamily.CATBOOST supported_problem_types = [ProblemTypes.BINARY, ProblemTypes.MULTICLASS, ProblemTypes.TIME_SERIES_BINARY, ProblemTypes.TIME_SERIES_MULTICLASS] 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=True, 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) self._label_encoder = None # 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_classifier = catboost.CatBoostClassifier(**cb_parameters, random_seed=random_seed) super().__init__(parameters=parameters, component_obj=cb_classifier, 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()) # 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)) self._component_obj.fit(X, y, silent=True, cat_features=cat_cols) return self
[docs] def predict(self, X): X = _convert_to_woodwork_structure(X) X = _convert_woodwork_types_wrapper(X.to_dataframe()) predictions = self._component_obj.predict(X) if predictions.ndim == 2 and predictions.shape[1] == 1: predictions = predictions.flatten() if self._label_encoder: predictions = self._label_encoder.inverse_transform(predictions.astype(np.int64)) if not isinstance(predictions, pd.Series): predictions = pd.Series(predictions) return predictions
@property def feature_importance(self): return self._component_obj.get_feature_importance()