optunity example error in Tuning svc without deciding kernel

Question

I am finding how to tune svm parameters but it is said in one article that gridsearch which is innate in sklearn is not the most efficient way to do tuning but with optunity so if found this http://optunity.readthedocs.io/en/latest/notebooks/notebooks/sklearn-svc.html#tune-svc-without-deciding-the-kernel-in-advance I adapt may code into this one but i encountered an error :

"ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()" I do not know what is wrong.


#here is the source 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)

what is wrong with my code.I only replaced dataand labels in the example code. could anyone help me with this. I badly need to debugged this one. Advance thank you

complete error is here:

Traceback error

Answer 1

You need to encode the labels using labelencoder