如何绘制多类数据的ROC曲线并从混淆矩阵中测量MAUC

Question

我已经在具有3个类别的数据集上使用了反向传播：L，B，R。在制作了神经网络之后，我还制作了一个混淆矩阵。

实际的类数组：

sample_test = array([0, 1, 0, 2, 0, 2, 1, 1, 0, 1, 1, 1], dtype=int64)

预测类数组：

yp = array([0, 1, 0, 2, 0, 2, 0, 1, 0, 1, 1, 1], dtype=int64)

混淆矩阵代码：

from sklearn import svm, datasets
from sklearn.model_selection import train_test_split
from sklearn.metrics import confusion_matrix
from sklearn.utils.multiclass import unique_labels

class_names = ['B','R','L']

def plot_confusion_matrix(y_true, y_pred, classes,
                          normalize=False,
                          title=None,
                          cmap=plt.cm.Blues):
    """
    This function prints and plots the confusion matrix.
    Normalization can be applied by setting `normalize=True`.
    """
    if not title:
        if normalize:
            title = 'Normalized confusion matrix'
        else:
            title = 'Confusion matrix, without normalization'

    # Compute confusion matrix
    cm = confusion_matrix(y_true, y_pred)
    # Only use the labels that appear in the data
    classes = [0, 1, 2]
    if normalize:
        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
        print("Normalized confusion matrix")
    else:
        print('Confusion matrix, without normalization')

    print(cm)

    fig, ax = plt.subplots()
    im = ax.imshow(cm, interpolation='nearest', cmap=cmap)
    ax.figure.colorbar(im, ax=ax)
    # We want to show all ticks...
    ax.set(xticks=np.arange(cm.shape[1]),
           yticks=np.arange(cm.shape[0]),
           # ... and label them with the respective list entries
           xticklabels=classes, yticklabels=classes,
           title=title,
           ylabel='True label',
           xlabel='Predicted label')

    # Rotate the tick labels and set their alignment.
    plt.setp(ax.get_xticklabels(), rotation=45, ha="right",
             rotation_mode="anchor")

    # Loop over data dimensions and create text annotations.
    fmt = '.2f' if normalize else 'd'
    thresh = cm.max() / 2.
    for i in range(cm.shape[0]):
        for j in range(cm.shape[1]):
            ax.text(j, i, format(cm[i, j], fmt),
                    ha="center", va="center",
                    color="white" if cm[i, j] > thresh else "black")
    fig.tight_layout()
    return ax

np.set_printoptions(precision=2)

# Plot non-normalized confusion matrix
plot_confusion_matrix(sample_test, yp, classes=class_names, 
                      title='Confusion matrix, without normalization')

# Plot normalized confusion matrix
plot_confusion_matrix(sample_test, yp, classes=class_names , normalize=True,
                      title='Normalized confusion matrix')

plt.show()

输出：

现在，我要为此绘制ROC曲线并计算MAUC。我看到了documentation，但无法正确理解该怎么做。

如果有人可以提出一些建议来帮助我，我将非常感激。预先感谢。

Answer 1

ROC是按类别计算的-将每个类别视为“正”类别，将其他类别视为“负”类别。像这样的东西：

from sklearn.metrics import roc_auc_score
print (roc_auc_score((your_true_classes_vector== 'one of the classes').astype(float), your_predict_proba_df['the predicted probability of the class']))