solver = CategoricalHyperparameter(name="solver", choices=['lbfgs', 'sgd', 'adam'], default_value='adam') cs.add_hyperparameters([ hidden_layer_depth, num_nodes_per_layer, activation, alpha, solver, ]) return cs # Add MLP classifier component to auto-sklearn. autosklearn.pipeline.components.classification.add_classifier(MLPClassifier) cs = MLPClassifier.get_hyperparameter_search_space() print(cs) ############################################################################ # Data Loading # ============ X, y = load_breast_cancer(return_X_y=True) X_train, X_test, y_train, y_test = train_test_split(X, y) ############################################################################ # Fit MLP classifier to the data # ============================== clf = autosklearn.classification.AutoSklearnClassifier( time_left_for_this_task=30,