"""XGBoost Classifier."""
import numpy as np
import pandas as pd
from pandas.api.types import is_integer_dtype
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 _rename_column_names_to_numeric, import_or_raise
[docs]class XGBoostClassifier(Estimator):
"""XGBoost Classifier.
Args:
eta (float): Boosting learning rate. Defaults to 0.1.
max_depth (int): Maximum tree depth for base learners. Defaults to 6.
min_child_weight (float): Minimum sum of instance weight (hessian) needed in a child. Defaults to 1.0
n_estimators (int): Number of gradient boosted trees. Equivalent to number of boosting rounds. Defaults to 100.
random_seed (int): Seed for the random number generator. Defaults to 0.
n_jobs (int): Number of parallel threads used to run xgboost. Note that creating thread contention will significantly slow down the algorithm. Defaults to 12.
"""
name = "XGBoost Classifier"
hyperparameter_ranges = {
"eta": Real(0.000001, 1),
"max_depth": Integer(1, 10),
"min_child_weight": Real(1, 10),
"n_estimators": Integer(1, 1000),
}
"""{
"eta": Real(0.000001, 1),
"max_depth": Integer(1, 10),
"min_child_weight": Real(1, 10),
"n_estimators": Integer(1, 1000),
}"""
model_family = ModelFamily.XGBOOST
"""ModelFamily.XGBOOST"""
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,
]"""
# xgboost supports seeds from -2**31 to 2**31 - 1 inclusive. these limits ensure the random seed generated below
# is within that range.
SEED_MIN = -(2**31)
SEED_MAX = 2**31 - 1
def __init__(
self,
eta=0.1,
max_depth=6,
min_child_weight=1,
n_estimators=100,
random_seed=0,
eval_metric="logloss",
n_jobs=12,
**kwargs,
):
parameters = {
"eta": eta,
"max_depth": max_depth,
"min_child_weight": min_child_weight,
"n_estimators": n_estimators,
"n_jobs": n_jobs,
"eval_metric": eval_metric,
}
parameters.update(kwargs)
if "use_label_encoder" in parameters:
parameters.pop("use_label_encoder")
xgb_error_msg = (
"XGBoost is not installed. Please install using `pip install xgboost.`"
)
xgb = import_or_raise("xgboost", error_msg=xgb_error_msg)
xgb_classifier = xgb.XGBClassifier(
use_label_encoder=False, random_state=random_seed, **parameters
)
self._label_encoder = None
super().__init__(
parameters=parameters,
component_obj=xgb_classifier,
random_seed=random_seed,
)
def _label_encode(self, y):
if not is_integer_dtype(y):
self._label_encoder = LabelEncoder()
y = pd.Series(self._label_encoder.fit_transform(None, y)[1], dtype="int64")
return y
[docs] def fit(self, X, y=None):
"""Fits XGBoost 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, y = super()._manage_woodwork(X, y)
self.input_feature_names = list(X.columns)
X = _rename_column_names_to_numeric(X)
y = self._label_encode(y)
self._component_obj.fit(X, y)
return self
[docs] def predict(self, X):
"""Make predictions using the fitted XGBoost classifier.
Args:
X (pd.DataFrame): Data of shape [n_samples, n_features].
Returns:
pd.DataFrame: Predicted values.
"""
X, _ = super()._manage_woodwork(X)
X = _rename_column_names_to_numeric(X)
predictions = super().predict(X)
if not self._label_encoder:
return predictions
predictions = self._label_encoder.inverse_transform(
predictions.astype(np.int64),
)
return predictions
[docs] def predict_proba(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, _ = super()._manage_woodwork(X)
X = _rename_column_names_to_numeric(X)
return super().predict_proba(X)
@property
def feature_importance(self):
"""Feature importance of fitted XGBoost classifier."""
return pd.Series(self._component_obj.feature_importances_)