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

"""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_)