blocks|key|4351926|text|正如人们在评论中提到的那样，你必须使用OneVsAll方法将问题转化为二进制，这样你就有了n_class个ROC曲线。|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|4351927|一个简单的例子：|4351928|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.model_selection+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+=+%250.2f)'+%25+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()|code-block|syntax|javascript|4351929|​|4351930|📷|atomic|4351931|4351932|4351933|4351934|entityMap|0|IMAGE|mutability|IMMUTABLE|imageUrl|https://ask.qcloudimg.com/http-save/yehe-900000/e3c911b3a47b3497b6a69084adcdf742.png|imageAlt|1|https://ask.qcloudimg.com/http-save/yehe-900000/0e9c99f64670d6a92d9bb238c2e77248.png|2|https://ask.qcloudimg.com/http-save/yehe-900000/af65940efa9278e76f8dea73ed13fff7.png^0|J|8|19|7|0|0|0|0|0|1|0|0|0|1|1|0|0|1|2|0|0^^$0|@$1|2|3|4|5|6|7|17|8|@$9|18|A|19|B|C]|$9|1A|A|1B|B|C]]|D|@]|E|$]]|$1|F|3|G|5|6|7|1C|8|@]|D|@]|E|$]]|$1|H|3|I|5|J|7|1D|8|@]|D|@]|E|$K|L]]|$1|M|3|N|5|6|7|1E|8|@]|D|@]|E|$]]|$1|O|3|P|5|Q|7|1F|8|@]|D|@$9|1G|A|1H|1|1I]]|E|$]]|$1|R|3|P|5|Q|7|1J|8|@]|D|@$9|1K|A|1L|1|1M]]|E|$]]|$1|S|3|P|5|Q|7|1N|8|@]|D|@$9|1O|A|1P|1|1Q]]|E|$]]|$1|T|3|N|5|6|7|1R|8|@]|D|@]|E|$]]|$1|U|3|-4|5|6|7|1S|8|@]|D|@]|E|$]]]|V|$W|$5|X|Y|Z|E|$10|11|12|-4]]|13|$5|X|Y|Z|E|$10|14|12|-4]]|15|$5|X|Y|Z|E|$10|16|12|-4]]]]

As people mentioned in comments you have to convert your problem into binary by using <code>OneVsAll</code> approach, so you'll have <code>n_class</code> number of ROC curves.
A simple example:
<pre><code>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.model_selection 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=&quot;lower right&quot;)
 plt.show()
</code></pre>
<a href="https://i.stack.imgur.com/ByrqW.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/ByrqW.png" alt="enter image description here" /></a><a href="https://i.stack.imgur.com/pqC8U.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/pqC8U.png" alt="enter image description here" /></a><a href="https://i.stack.imgur.com/ydVNu.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/ydVNu.png" alt="enter image description here" /></a>

blocks|key|4351958|text|这对我来说很有效，如果你想把它们放在同一张图上，这是很好的。它类似于@omdv的答案，但可能更简洁一些。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|4351959|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+=+%250.2f)+for+label+%25i'+%25+(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))|code-block|syntax|javascript|4351960|entityMap^0|0|0^^$0|@$1|2|3|4|5|6|7|I|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|J|8|@]|9|@]|A|$E|F]]|$1|G|3|-4|5|6|7|K|8|@]|9|@]|A|$]]]|H|$]]

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.

<pre><code>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))
</code></pre>

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:

<pre><code>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()
</code></pre>

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?

ROC for multiclass classification

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，我已经让它与以下代码一起工作：scaler = StandardScaler(with_mean=False)enc = LabelEncoder()y = enc.fit_transform(labels)feat_sel = Sel...

问用于多类分类的ROC
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