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python-3.x - 如何绘制多类数据的 ROC 曲线并从混淆矩阵测量 MAUC

问题描述

我在具有 3 个类的数据集上使用了反向传播：L、B、R。在制作神经网络后，我还制作了一个混淆矩阵。

实际类数组：

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：

在此处输入图像描述 在此处输入图像描述

现在我想为此绘制 ROC 曲线并计算 MAUC。我看到了文档，但无法正确理解该怎么做。

如果有人可以通过提供一些建议来帮助我，我将非常感激。提前致谢。

标签： python-3.xmachine-learningrocconfusion-matrix

解决方案

ROC 是按类计算的——将每个类视为“正”类，将其他类视为“负”类。注意 - 首先你必须使用 predict_proba() - 来获得每个类别的预测概率。像这样的东西：

import seaborn as sns
from sklearn.tree import DecisionTreeClassifier
from sklearn.model_selection import train_test_split
from sklearn import preprocessing
from sklearn.metrics import roc_auc_score

iris = sns.load_dataset('iris')
X = iris.drop('species',axis=1)
y = iris['species']
X_train, X_test, y_train, y_test = train_test_split(X,y)

le = preprocessing.LabelEncoder()
le.fit(y_train)
le.transform(y_train)

model = DecisionTreeClassifier(max_depth=1)
model.fit(X_train,le.transform(y_train))

predictions =pd.DataFrame(model.predict_proba(X_test),columns=list(le.inverse_transform(model.classes_)))

print(roc_auc_score((y_test == 'versicolor').astype(float), predictions['versicolor']))

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