Question

我正在做不同的文字分类实验。现在我需要计算每项任务的AUC-ROC。对于二进制分类，我已经使用了这段代码：

scaler = StandardScaler(with_mean=False)

enc = LabelEncoder()
y = enc.fit_transform(labels)

feat_sel = SelectKBest(mutual_info_classif, k=200)

clf = linear_model.LogisticRegression()

pipe = Pipeline([('vectorizer', DictVectorizer()),
                 ('scaler', StandardScaler(with_mean=False)),
                 ('mutual_info', feat_sel),
                 ('logistregress', clf)])
y_pred = model_selection.cross_val_predict(pipe, instances, y, cv=10)
# instances is a list of dictionaries

#visualisation ROC-AUC

fpr, tpr, thresholds = roc_curve(y, y_pred)
auc = auc(fpr, tpr)
print('auc =', auc)

plt.figure()
plt.title('Receiver Operating Characteristic')
plt.plot(fpr, tpr, 'b',
label='AUC = %0.2f'% auc)
plt.legend(loc='lower right')
plt.plot([0,1],[0,1],'r--')
plt.xlim([-0.1,1.2])
plt.ylim([-0.1,1.2])
plt.ylabel('True Positive Rate')
plt.xlabel('False Positive Rate')
plt.show()

但现在我需要为多类分类任务执行此操作。我在某处读到了我需要对标签进行二值化的内容，但我真的不知道如何计算多类分类的ROC。提示？

Answer 1

正如人们在评论中提到的那样，您必须使用OneVsAll方法将问题转换为二进制，因此您将拥有n_class个ROC曲线。

一个简单的例子：

from sklearn.metrics import roc_curve, auc
from sklearn import datasets
from sklearn.multiclass import OneVsRestClassifier
from sklearn.svm import LinearSVC
from sklearn.preprocessing import label_binarize
from sklearn.cross_validation import train_test_split
import matplotlib.pyplot as plt

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

y = label_binarize(y, classes=[0,1,2])
n_classes = 3

# shuffle and split training and test sets
X_train, X_test, y_train, y_test =\
    train_test_split(X, y, test_size=0.33, random_state=0)

# classifier
clf = OneVsRestClassifier(LinearSVC(random_state=0))
y_score = clf.fit(X_train, y_train).decision_function(X_test)

# Compute ROC curve and ROC area for each class
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] = auc(fpr[i], tpr[i])

# Plot of a ROC curve for a specific class
for i in range(n_classes):
    plt.figure()
    plt.plot(fpr[i], tpr[i], label='ROC curve (area = %0.2f)' % roc_auc[i])
    plt.plot([0, 1], [0, 1], 'k--')
    plt.xlim([0.0, 1.0])
    plt.ylim([0.0, 1.05])
    plt.xlabel('False Positive Rate')
    plt.ylabel('True Positive Rate')
    plt.title('Receiver operating characteristic example')
    plt.legend(loc="lower right")
    plt.show()

Answer 2

这对我有用，如果您希望它们在同一地块上，那很好。它类似于 @omdv的答案，但也许更简洁。

def plot_multiclass_roc(clf, X_test, y_test, n_classes, figsize=(17, 6)):
    y_score = clf.decision_function(X_test)

    # structures
    fpr = dict()
    tpr = dict()
    roc_auc = dict()

    # calculate dummies once
    y_test_dummies = pd.get_dummies(y_test, drop_first=False).values
    for i in range(n_classes):
        fpr[i], tpr[i], _ = roc_curve(y_test_dummies[:, i], y_score[:, i])
        roc_auc[i] = auc(fpr[i], tpr[i])

    # roc for each class
    fig, ax = plt.subplots(figsize=figsize)
    ax.plot([0, 1], [0, 1], 'k--')
    ax.set_xlim([0.0, 1.0])
    ax.set_ylim([0.0, 1.05])
    ax.set_xlabel('False Positive Rate')
    ax.set_ylabel('True Positive Rate')
    ax.set_title('Receiver operating characteristic example')
    for i in range(n_classes):
        ax.plot(fpr[i], tpr[i], label='ROC curve (area = %0.2f) for label %i' % (roc_auc[i], i))
    ax.legend(loc="best")
    ax.grid(alpha=.4)
    sns.despine()
    plt.show()

plot_multiclass_roc(full_pipeline, X_test, y_test, n_classes=16, figsize=(16, 10))

Sklearn：用于多类分类的ROC

2 个答案: