优化 svc 中的 optUnity 示例错误，而不决定内核

Question

我正在寻找如何调整 svm 参数，但在一篇文章中说，sklearn 中固有的网格搜索不是最有效的调优方法，而是使用 optunity，所以如果发现这个 http://optunity.readthedocs.io/en/latest/notebooks/notebooks/sklearn-svc.html#tune-svc-without-deciding-the-kernel-in-advance 我适应可能会编码到这个，但我遇到了一个错误：

"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)

我的代码有什么问题。我只替换了示例代码中的data和labels。谁能帮我解决这个问题。我非常需要调试这个。提前谢谢

完整的错误在这里：

回溯错误

Answer 1

您需要使用 labelencoder 对标签进行编码