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,
