我正在尝试使用scikit-learn的监督学习方法之一将文本片段分类为一个或多个类别。我尝试过的所有算法的预测函数只返回一个匹配项。
例如，我有一段文字：
"Theaters in New York compared to those in London"
我已经训练了算法，为我输入的每个文本片段选择一个位置。
在上面的例子中，我希望它返回New York和London，但它只返回New York。
是否可以使用 scikit-learn 返回多个结果？甚至以下一个最高概率返回标签？
感谢您的帮助。
---更新
我尝试使用OneVsRestClassifier但我仍然只得到每段文本的一个选项。以下是我正在使用的示例代码
y_train = ('New York','London')
train_set = ("new york nyc big apple", "london uk great britain")
vocab = {'new york' :0,'nyc':1,'big apple':2,'london' : 3, 'uk': 4, 'great britain' : 5}
count = CountVectorizer(analyzer=WordNGramAnalyzer(min_n=1, max_n=2),vocabulary=vocab)
test_set = ('nice day in nyc','london town','hello welcome to the big apple. enjoy it here and london too')
X_vectorized = count.transform(train_set).todense()
smatrix2 = count.transform(test_set).todense()
base_clf = MultinomialNB(alpha=1)
clf = OneVsRestClassifier(base_clf).fit(X_vectorized, y_train)
Y_pred = clf.predict(smatrix2)
print Y_pred
结果：["纽约"伦敦"伦敦"]
你想要
的称为多标签分类。Scikits-learn可以做到这一点。看这里：http://scikit-learn.org/dev/modules/multiclass.html。
我不确定你的例子出了什么问题，我的sklearn版本显然没有WordNGramAnalyzer。也许这是一个使用更多训练示例或尝试不同分类器的问题？但请注意，多标签分类器期望目标是元组列表/标签列表。
以下内容对我有用：
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
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 = [[0],[0],[0],[0],[0],[0],[1],[1],[1],[1],[1],[1],[0,1],[0,1]]
X_test = np.array(['nice day in nyc',
'welcome to london',
'hello welcome to new york. enjoy it here and london too'])
target_names = ['New York', 'London']
classifier = Pipeline([
('vectorizer', CountVectorizer(min_n=1,max_n=2)),
('tfidf', TfidfTransformer()),
('clf', OneVsRestClassifier(LinearSVC()))])
classifier.fit(X_train, y_train)
predicted = classifier.predict(X_test)
for item, labels in zip(X_test, predicted):
print '%s => %s' % (item, ', '.join(target_names[x] for x in labels))
对我来说，这会产生输出：
nice day in nyc => New York
welcome to london => London
hello welcome to new york. enjoy it here and london too => New York, London
编辑：针对Python 3进行了更新，scikit-learn 0.18.1使用MultiLabelBinarizer建议。
我也一直在研究这个问题，并对 mwv 的出色答案进行了轻微的增强，这可能是有用的。它将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
我也遇到了这个问题，对我来说的问题是我的y_Train是字符串序列，而不是字符串序列序列。显然，OneVsRestClassifier将根据输入标签格式决定是否使用多类还是多标签。所以改变：
y_train = ('New York','London')
自
y_train = (['New York'],['London'])
显然这将在未来消失，因为它打破了所有标签都是相同的：https://github.com/scikit-learn/scikit-learn/pull/1987
更改此行以使其在新版本的 python 中工作
# lb = preprocessing.LabelBinarizer()
lb = preprocessing.MultiLabelBinarizer()
一些多分类示例如下：-
示例 1：-
import numpy as np
from sklearn.preprocessing import LabelBinarizer
encoder = LabelBinarizer()
arr2d = np.array([1, 2, 3,4,5,6,7,8,9,10,11,12,13,14,1])
transfomed_label = encoder.fit_transform(arr2d)
print(transfomed_label)
输出为
[[1 0 0 0 0 0 0 0 0 0 0 0 0 0]
[0 1 0 0 0 0 0 0 0 0 0 0 0 0]
[0 0 1 0 0 0 0 0 0 0 0 0 0 0]
[0 0 0 1 0 0 0 0 0 0 0 0 0 0]
[0 0 0 0 1 0 0 0 0 0 0 0 0 0]
[0 0 0 0 0 1 0 0 0 0 0 0 0 0]
[0 0 0 0 0 0 1 0 0 0 0 0 0 0]
[0 0 0 0 0 0 0 1 0 0 0 0 0 0]
[0 0 0 0 0 0 0 0 1 0 0 0 0 0]
[0 0 0 0 0 0 0 0 0 1 0 0 0 0]
[0 0 0 0 0 0 0 0 0 0 1 0 0 0]
[0 0 0 0 0 0 0 0 0 0 0 1 0 0]
[0 0 0 0 0 0 0 0 0 0 0 0 1 0]
[0 0 0 0 0 0 0 0 0 0 0 0 0 1]
[1 0 0 0 0 0 0 0 0 0 0 0 0 0]]
示例 2：-
import numpy as np
from sklearn.preprocessing import LabelBinarizer
encoder = LabelBinarizer()
arr2d = np.array(['Leopard','Lion','Tiger', 'Lion'])
transfomed_label = encoder.fit_transform(arr2d)
print(transfomed_label)
输出为
[[1 0 0]
[0 1 0]
[0 0 1]
[0 1 0]]