Support vector machine classification (SVC)ΒΆ

You can find an executable version of this example in bin/examples/python/sklearn/ in your Optunity release.

In this example, we will train an SVC with RBF kernel using scikit-learn. In this case, we have to tune two hyperparameters: C and gamma. We will use twice iterated 10-fold cross-validation to test a pair of hyperparameters.

In this example, we will use optunity.maximize().

import optunity
import optunity.metrics
import sklearn.svm

# score function: twice iterated 10-fold cross-validated accuracy
@optunity.cross_validated(x=data, y=labels, num_folds=10, num_iter=2)
def svm_auc(x_train, y_train, x_test, y_test, logC, logGamma):
    model = sklearn.svm.SVC(C=10 ** logC, gamma=10 ** logGamma).fit(x_train, y_train)
    decision_values = model.decision_function(x_test)
    return optunity.metrics.roc_auc(y_test, decision_values)

# perform tuning
hps, _, _ = optunity.maximize(svm_auc, num_evals=200, logC=[-5, 2], logGamma=[-5, 1])

# train model on the full training set with tuned hyperparameters
optimal_model = sklearn.svm.SVC(C=10 ** hps['logC'], gamma=10 ** hps['logGamma']).fit(data, labels)