如何对一个变长特征进行热编码? | 如何对一个变长特征进行热编码

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问题描述

给出可变长度特征的列表:

Given a list of variant length features:

features = [
    ['f1', 'f2', 'f3'],
    ['f2', 'f4', 'f5', 'f6'],
    ['f1', 'f2']
]

每个样本具有不同数量的特征，特征dtype是str，并且已经很热.

where each sample has variant number of features and the feature dtype is str and already one hot.

为了使用sklearn的特征选择实用程序，我必须将features转换为二维数组，如下所示:

In order to use feature selection utilities of sklearn, I have to convert the features to a 2D-array which looks like:

    f1  f2  f3  f4  f5  f6
s1   1   1   1   0   0   0
s2   0   1   0   1   1   1
s3   1   1   0   0   0   0

如何通过sklearn或numpy实现它?

How could I achieve it via sklearn or numpy?

推荐答案

您可以使用 MultiLabelBinarizer 存在于scikit中，专门用于执行此操作.

You can use MultiLabelBinarizer present in scikit which is specifically used for doing this.

您的示例代码:

features = [
            ['f1', 'f2', 'f3'],
            ['f2', 'f4', 'f5', 'f6'],
            ['f1', 'f2']
           ]
from sklearn.preprocessing import MultiLabelBinarizer
mlb = MultiLabelBinarizer()
new_features = mlb.fit_transform(features)

输出:

array([[1, 1, 1, 0, 0, 0],
       [0, 1, 0, 1, 1, 1],
       [1, 1, 0, 0, 0, 0]])

它还可以与其他feature_selection实用程序一起在管道中使用.

This can also be used in a pipeline, along with other feature_selection utilities.

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