An example system

In this section, you will write a wrapper function to optimize the XGBoost algorithm hyperparameters to improve performance on the Breast Cancer Wisconsin dataset:

# Importing necessary libraries
import numpy as np
from xgboost import XGBClassifier
from sklearn import datasets
from sklearn.model_selection import cross_val_score

# Importing ConfigSpace and different types of parameters
from smac.configspace import ConfigurationSpace
from ConfigSpace.hyperparameters import CategoricalHyperparameter, \
    UniformFloatHyperparameter, UniformIntegerHyperparameter
from ConfigSpace.conditions import InCondition

# Import SMAC-utilities
from smac.tae.execute_func import ExecuteTAFuncDict
from smac.scenario.scenario import Scenario
from smac.facade.smac_facade import SMAC

# Creating configuration space.
# Configuration space will hold all of your hyperparameters
cs = ConfigurationSpace()

# Defining hyperparameters and range of values that they can take
learning_rate = UniformFloatHyperparameter...