编辑：建议使用MultiLabelBinarizer为Python 3，scikit-learn 0.18.1更新。
我也一直在努力，并且对mwv的出色回答（可能有用）进行了一些改进。它使用文本标签作为输入而不是二进制标签，并使用MultiLabelBinarizer对其进行编码。
import numpy as np
from sklearn.pipeline import Pipeline
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.svm import LinearSVC
from sklearn.feature_extraction.text import TfidfTransformer
from sklearn.multiclass import OneVsRestClassifier
from sklearn.preprocessing import MultiLabelBinarizer
X_train = np.array(["new york is a hell of a town",
"new york was originally dutch",
"the big apple is great",
"new york is also called the big apple",
"nyc is nice",
"people abbreviate new york city as nyc",
"the capital of great britain is london",
"london is in the uk",
"london is in england",
"london is in great britain",
"it rains a lot in london",
"london hosts the british museum",
"new york is great and so is london",
"i like london better than new york"])
y_train_text = [["new york"],["new york"],["new york"],["new york"],["new york"],
["new york"],["london"],["london"],["london"],["london"],
["london"],["london"],["new york","london"],["new york","london"]]
X_test = np.array(['nice day in nyc',
'welcome to london',
'london is rainy',
'it is raining in britian',
'it is raining in britian and the big apple',
'it is raining in britian and nyc',
'hello welcome to new york. enjoy it here and london too'])
target_names = ['New York', 'London']
mlb = MultiLabelBinarizer()
Y = mlb.fit_transform(y_train_text)
classifier = Pipeline([
('vectorizer', CountVectorizer()),
('tfidf', TfidfTransformer()),
('clf', OneVsRestClassifier(LinearSVC()))])
classifier.fit(X_train, Y)
predicted = classifier.predict(X_test)
all_labels = mlb.inverse_transform(predicted)
for item, labels in zip(X_test, all_labels):
print('{0} => {1}'.format(item, ', '.join(labels)))
这给了我以下输出：
nice day in nyc => new york
welcome to london => london
london is rainy => london
it is raining in britian => london
it is raining in britian and the big apple => new york
it is raining in britian and nyc => london, new york
hello welcome to new york. enjoy it here and london too => london, new york