"""CatBoost Classifier, a classifier that uses gradient-boosting on decision trees. CatBoost is an open-source library and natively supports categorical features."""
import copy
import warnings
import numpy as np
import pandas as pd
from skopt.space import Integer, Real
from evalml.model_family import ModelFamily
from evalml.pipelines.components.estimators import Estimator
from evalml.pipelines.components.transformers import LabelEncoder
from evalml.problem_types import ProblemTypes
from evalml.utils import import_or_raise, infer_feature_types
[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/
Args:
n_estimators (float): The maximum number of trees to build. Defaults to 10.
eta (float): The learning rate. Defaults to 0.03.
max_depth (int): The maximum tree depth for base learners. Defaults to 6.
bootstrap_type (string): Defines the method for sampling the weights of objects. Available methods are 'Bayesian', 'Bernoulli', 'MVS'. Defaults to None.
silent (boolean): Whether to use the "silent" logging mode. Defaults to True.
allow_writing_files (boolean): Whether to allow writing snapshot files while training. Defaults to False.
n_jobs (int or None): Number of jobs to run in parallel. -1 uses all processes. Defaults to -1.
random_seed (int): Seed for the random number generator. Defaults to 0.
"""
name = "CatBoost Classifier"
hyperparameter_ranges = {
"n_estimators": Integer(4, 100),
"eta": Real(0.000001, 1),
"max_depth": Integer(4, 10),
}
"""{
"n_estimators": Integer(4, 100),
"eta": Real(0.000001, 1),
"max_depth": Integer(4, 10),
}"""
model_family = ModelFamily.CATBOOST
"""ModelFamily.CATBOOST"""
supported_problem_types = [
ProblemTypes.BINARY,
ProblemTypes.MULTICLASS,
ProblemTypes.TIME_SERIES_BINARY,
ProblemTypes.TIME_SERIES_MULTICLASS,
]
"""[
ProblemTypes.BINARY,
ProblemTypes.MULTICLASS,
ProblemTypes.TIME_SERIES_BINARY,
ProblemTypes.TIME_SERIES_MULTICLASS,
]"""
def __init__(
self,
n_estimators=10,
eta=0.03,
max_depth=6,
bootstrap_type=None,
silent=True,
allow_writing_files=False,
random_seed=0,
n_jobs=-1,
**kwargs,
):
parameters = {
"n_estimators": n_estimators,
"eta": eta,
"max_depth": max_depth,
"bootstrap_type": bootstrap_type,
"silent": silent,
"allow_writing_files": allow_writing_files,
}
if kwargs.get("thread_count", None) is not None:
warnings.warn(
"Parameter 'thread_count' will be ignored. To use parallel threads, use the 'n_jobs' parameter instead.",
)
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_parameters["thread_count"] = n_jobs
cb_classifier = catboost.CatBoostClassifier(
**cb_parameters, random_seed=random_seed
)
parameters["n_jobs"] = n_jobs
super().__init__(
parameters=parameters,
component_obj=cb_classifier,
random_seed=random_seed,
)
[docs] def fit(self, X, y=None):
"""Fits CatBoost classifier component to data.
Args:
X (pd.DataFrame): The input training data of shape [n_samples, n_features].
y (pd.Series): The target training data of length [n_samples].
Returns:
self
"""
X = infer_feature_types(X)
cat_cols = list(X.ww.select("category", return_schema=True).columns)
self.input_feature_names = list(X.columns)
X, y = super()._manage_woodwork(X, y)
# For binary classification, catboost expects numeric values, so encoding before.
if y.nunique() <= 2:
self._label_encoder = LabelEncoder()
y = self._label_encoder.fit_transform(None, y)[1]
self._component_obj.fit(X, y, silent=True, cat_features=cat_cols)
return self
[docs] def predict(self, X):
"""Make predictions using the fitted CatBoost classifier.
Args:
X (pd.DataFrame): Data of shape [n_samples, n_features].
Returns:
pd.DataFrame: Predicted values.
"""
X = infer_feature_types(X)
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),
)
predictions = infer_feature_types(predictions)
predictions.index = X.index
return predictions
@property
def feature_importance(self):
"""Feature importance of fitted CatBoost classifier."""
return pd.Series(self._component_obj.get_feature_importance())