12.5 classification_metrics.py 文件源码

def test_roc_auc_score():

    iris=load_iris()
    X=iris.data
    y=iris.target

    y = label_binarize(y, classes=[0, 1, 2])
    n_classes = y.shape[1]

    np.random.seed(0)
    n_samples, n_features = X.shape
    X = np.c_[X, np.random.randn(n_samples, 200 * n_features)]

    X_train,X_test,y_train,y_test=train_test_split(X,y,
            test_size=0.5,random_state=0)

    clf=OneVsRestClassifier(SVC(kernel='linear', probability=True,random_state=0))
    clf.fit(X_train,y_train)
    y_score = clf.fit(X_train, y_train).decision_function(X_test)

    fig=plt.figure()
    ax=fig.add_subplot(1,1,1)
    fpr = dict()
    tpr = dict()
    roc_auc=dict()
    for i in range(n_classes):
        fpr[i], tpr[i], _ = roc_curve(y_test[:, i],y_score[:, i])
        roc_auc[i] = roc_auc_score(fpr[i], tpr[i])
        ax.plot(fpr[i],tpr[i],label="target=%s,auc=%s"%(i,roc_auc[i]))
    ax.plot([0, 1], [0, 1], 'k--')
    ax.set_xlabel("FPR")
    ax.set_ylabel("TPR")
    ax.set_title("ROC")
    ax.legend(loc="best")
    ax.set_xlim(0,1.1)
    ax.set_ylim(0,1.1)
    ax.grid()
    plt.show()