了解scikit-learn中的数据格式 [英] Understanding format of data in scikit-learn

问题描述

我正在尝试使用Python 3.x中的scikit-learn处理多标签文本分类.我有使用load_svmlight_file模块加载的libsvm格式的数据.数据格式是这样的.

I am trying to work with multi-label text classification using scikit-learn in Python 3.x. I have data in libsvm format which I am loading using load_svmlight_file module. The data format is like this.

• 314523,165538,76255 1:1 2:1 3:1 4:1 5:1 6:1 7:1 8:1 9:1 10:1 11:1 12:2 13:1
• 410523,230296,368303,75145 8:1 19:2 22:1 24:1 29:1 63:1 68:1 69:3 76:1 82:1 83:1 84:1

• 314523,165538,76255 1:1 2:1 3:1 4:1 5:1 6:1 7:1 8:1 9:1 10:1 11:1 12:2 13:1
• 410523,230296,368303,75145 8:1 19:2 22:1 24:1 29:1 63:1 68:1 69:3 76:1 82:1 83:1 84:1

每行对应一个文档.前三个数字是标签，接下来的条目是具有其值的要素编号.每个功能都对应一个单词.

Each of these lines corresponds to one document. The first three numbers are the labels, and the next entries are feature numbers with their values. Each feature corresponds to a word.

我正在使用此脚本加载数据.

I am loading the data using this script.

from sklearn.datasets import load_svmlight_file

X,Y = load_svmlight_file("train.csv", multilabel = True, zero_based = True)

我的问题是，当我通过执行print (X[0])来查看数据格式时，会得到此输出.

My question is, that when I see the format of data by doing for example, print (X[0]), I get this output.

(0，1)1.0

(0, 1) 1.0

(0，2)1.0

(0，3)1.0

(0，4)1.0

(0，5)1.0

(0，6)1.0

(0，7)1.0

(0，8)1.0

(0，9)1.0

(0，10)1.0

(0，11)1.0

(0，12)2.0

(0，13)1.0

我不了解此格式的含义.格式不应该是这样的.

I don't understand the meaning of this format. Shouldn't the format be something like this.

> 1  2  3  4  5  6  7  8  9  10  11  12  13

> 1  1  1  1  1  1  1  1  1   1   1   2   1  

我是scikit的新手.在这方面，我将提供一些帮助.

I am new to scikit. I would appreciate some help in this regard.

推荐答案

这与多标签分类本身无关.从load_svmlight_file获得的特征矩阵X是

This has nothing to do with multilabel classification per se. The feature matrix X that you get from load_svmlight_file is a SciPy CSR matrix, as explained in the docs, and those print in a rather unfortunate format:

>>> from scipy.sparse import csr_matrix
>>> X = csr_matrix([[0, 0, 1], [2, 3, 0]])
>>> X
<2x3 sparse matrix of type '<type 'numpy.int64'>'
    with 3 stored elements in Compressed Sparse Row format>
>>> X.toarray()
array([[0, 0, 1],
       [2, 3, 0]])
>>> print(X)
  (0, 2)    1
  (1, 0)    2
  (1, 1)    3

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