Error executing Optunity

Question

After getting the code from http://optunity.readthedocs.io/en/latest/notebooks/notebooks/sklearn-svc.html#tune-svc-without-deciding-the-kernel-in-advance, i encountered an error :

"ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()"

What seems to be the problem. Can anyone help me with this?

Below is the code.

import optunity
import optunity.metrics
import numpy as np

# k nearest neighbours
from sklearn.neighbors import KNeighborsClassifier
# support vector machine classifier
from sklearn.svm import SVC
# Naive Bayes
from sklearn.naive_bayes import GaussianNB
# Random Forest
from sklearn.ensemble import RandomForestClassifier
from sklearn.externals import joblib
import sklearn
im_features,image_classes,training_names,stdSlr,kmeans,k = joblib.load("others.pkl")

n = len(image_classes)


data = im_features
labels = np.array(image_classes)


cv_decorator = optunity.cross_validated(x=data, y=labels, num_folds=5)

space = {'kernel': {'linear': {'C': [0, 2]},
                    'rbf': {'logGamma': [-5, 0], 'C': [0, 10]},
                    'poly': {'degree': [2, 5], 'C': [0, 5], 'coef0': [0, 2]}
                    }
         }


def train_model(x_train, y_train, kernel, C, logGamma, degree, coef0):
    """A generic SVM training function, with arguments based on the chosen kernel."""
    if kernel == 'linear':
        model = sklearn.svm.SVC(kernel=kernel, C=C)
    elif kernel == 'poly':
        model = sklearn.svm.SVC(kernel=kernel, C=C, degree=degree, coef0=coef0)
    elif kernel == 'rbf':
        model = sklearn.svm.SVC(kernel=kernel, C=C, gamma=10 ** logGamma)
    else:
        raise ArgumentError("Unknown kernel function: %s" % kernel)
    model.fit(x_train, y_train)
    return model


def svm_tuned_auroc(x_train, y_train, x_test, y_test, kernel='linear', C=0, logGamma=0, degree=0, coef0=0):
    model = train_model(x_train, y_train, kernel, C, logGamma, degree, coef0)
    decision_values = model.decision_function(x_test)
    return optunity.metrics.roc_auc(y_test, decision_values)

svm_tuned_auroc = cv_decorator(svm_tuned_auroc)

optimal_svm_pars, info, _ = optunity.maximize_structured(svm_tuned_auroc, space, num_evals=150)
print("Optimal parameters" + str(optimal_svm_pars))
print("AUROC of tuned SVM: %1.3f" % info.optimum)