Minimal Example

The following code optimizes a support vector machine on the iris dataset.

from ConfigSpace import Configuration, ConfigurationSpace

import numpy as np
from smac import HyperparameterOptimizationFacade, Scenario
from sklearn import datasets
from sklearn.svm import SVC
from sklearn.model_selection import cross_val_score

iris = datasets.load_iris()

def train(config: Configuration, seed: int = 0) -> float:
    classifier = SVC(C=config["C"], random_state=seed)
    scores = cross_val_score(classifier,,, cv=5)
    return 1 - np.mean(scores)

configspace = ConfigurationSpace({"C": (0.100, 1000.0)})

# Scenario object specifying the optimization environment
scenario = Scenario(configspace, deterministic=True, n_trials=200)

# Use SMAC to find the best configuration/hyperparameters
smac = HyperparameterOptimizationFacade(scenario, train)
incumbent = smac.optimize()