Question

鉴于这个简单的多标签分类示例（取自这个问题，use scikit-learn to classify into multiple categories）

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 import preprocessing
from sklearn.metrics import accuracy_score

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'])

y_test_text = [["new york"],["london"],["london"],["london"],["new york", "london"],["new york", "london"],["new york", "london"]]


lb = preprocessing.MultiLabelBinarizer()
Y = lb.fit_transform(y_train_text)
Y_test = lb.fit_transform(y_test_text)

classifier = Pipeline([
('vectorizer', CountVectorizer()),
('tfidf', TfidfTransformer()),
('clf', OneVsRestClassifier(LinearSVC()))])

classifier.fit(X_train, Y)
predicted = classifier.predict(X_test)


print "Accuracy Score: ",accuracy_score(Y_test, predicted)

代码运行正常，并打印准确度分数，但是如果我将y_test_text更改为

y_test_text = [["new york"],["london"],["england"],["london"],["new york", "london"],["new york", "london"],["new york", "london"]]

我得到了

Traceback (most recent call last):
  File "/Users/scottstewart/Documents/scikittest/example.py", line 52, in <module>
     print "Accuracy Score: ",accuracy_score(Y_test, predicted)
  File "/Library/Python/2.7/site-packages/sklearn/metrics/classification.py", line 181, in accuracy_score
differing_labels = count_nonzero(y_true - y_pred, axis=1)
File "/System/Library/Frameworks/Python.framework/Versions/2.7/Extras/lib/python/scipy/sparse/compressed.py", line 393, in __sub__
raise ValueError("inconsistent shapes")
ValueError: inconsistent shapes

注意引入英国＆＃39;标签不在训练集中。如何使用多标签分类，以便在测试时使用＃34;标签介绍，我仍然可以运行一些指标？或者甚至可能吗？

编辑：谢谢你的回答，我想我的问题更多的是关于scikit二进制文件如何工作或应该工作。鉴于我的简短示例代码，如果我将y_test_text更改为

，我也会期待

y_test_text = [["new york"],["new york"],["new york"],["new york"],["new york"],["new york"],["new york"]]

它会起作用 - 我的意思是我们已经适合该标签，但在这种情况下我得到了

ValueError: Can't handle mix of binary and multilabel-indicator

Answer 1

你可以，如果你＆＃34;介绍＆＃34;训练中的新标签也是如此：

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 import preprocessing
from sklearn.metrics import accuracy_score

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","England"],["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'])

y_test_text = [["new york"],["new york"],["new york"],["new york"],["new york"],["new york"],["new york"]]


lb = preprocessing.MultiLabelBinarizer(classes=("new york","london","England"))
Y = lb.fit_transform(y_train_text)
Y_test = lb.fit_transform(y_test_text)

print Y_test

classifier = Pipeline([
('vectorizer', CountVectorizer()),
('tfidf', TfidfTransformer()),
('clf', OneVsRestClassifier(LinearSVC()))])

classifier.fit(X_train, Y)
predicted = classifier.predict(X_test)
print predicted

print "Accuracy Score: ",accuracy_score(Y_test, predicted)

<强>输出：

Accuracy Score:  0.571428571429

关键部分是：

y_train_text = [["new york"],["new york"],["new york"],
                ["new york"],["new york"],["new york"],
                ["london"],["london"],["london"],["london"],
                ["london"],["london"],["new york","England"],
                ["new york","london"]]

我们插入＆＃34;英格兰＆＃34;太。这是有道理的，因为其他方式如果他之前没有看到它，如何预测分类器的某些标签？所以我们用这种方式创建了一个三标签分类问题。

<强>编辑：

lb = preprocessing.MultiLabelBinarizer(classes=("new york","london","England"))

您必须将这些类作为arg传递给MultiLabelBinarizer()，它将适用于任何y_test_text。

Answer 2

简而言之 - 这是一个不适合的问题。分类假定所有标签都事先已知，二进制化器也是如此。将其安装在所有标签上，然后训练您想要的任何子集。

Answer 3

正如另一条评论所述，我个人希望二进制文件在“转换”时忽略未见过的类。如果测试样本提供的特征与训练中使用的特征不同，那么消耗二值化器结果的分类器可能反应不佳。

我解决了这个问题，只是从示例中删除了未见过的类。我认为比动态更改拟合二进制化器更安全，或者（另一种选择）扩展它以允许忽略。

list(map(lambda names: np.intersect1d(lb.classes_, names), y_test_text))

没有和你一起运行实际代码

对测试数据使用MultilabelBinarizer，标签不在训练集中

3 个答案: