Python Sklearn：用于多类分类的 ROC
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Sklearn: ROC for multiclass classification
提问by Bambi
I'm doing different text classification experiments. Now I need to calculate the AUC-ROC for each task. For the binary classifications, I already made it work with this code:
我正在做不同的文本分类实验。现在我需要计算每个任务的 AUC-ROC。对于二进制分类，我已经使用以下代码使其工作：
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()
But now I need to do it for the multiclass classification task. I read somewhere that I need to binarize the labels, but I really don't get how to calculate ROC for multiclass classification. Tips?
但是现在我需要为多类分类任务做这件事。我在某处读到我需要对标签进行二值化，但我真的不知道如何计算多类分类的 ROC。提示？
回答by omdv
As people mentioned in comments you have to convert your problem into binary by using OneVsAllapproach, so you'll have n_classnumber of ROC curves.
正如人们在评论中提到的，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许多 ROC 曲线。
A simple example:
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()
回答by pableau
This works for me and is nice if you want them on the same plot. It is similar to @omdv's answer but maybe a little more succinct.
这对我有用，如果你想让它们在同一个情节上，那就太好了。它类似于@omdv 的答案，但可能更简洁一些。
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))
