转换一个一维数组在Python 2D基于类的矩阵

问题描述

我使用了多级分类，所以为了测试之后评价它，我需要从分类器predictions（ Y_ preD ）反对真正的类值进行比较（ y_test ）。

I'm using a multi-class classifier, so in order to evaluate it after testing, I need the predictions from the classifier (y_pred)to be compared against the true class values (y_test).

但我有他们两个一维数组，像这样：

But I have them both as 1D arrays, like so:

y_test = [1, 1, 1, 2, 1, 4, 5, 3, ... etc ]
y_pred = [1, 1, 1, 2, 3, 2, 5, 0, ... etc ]

在我总共有46类。

但为了建立ROC曲线（在这里：），我猜我需要的 y_test 和 Y_ preD 以与二进制值的二维矩阵，以下形状：
number_of_test_cases点¯xnumber_of_classes 。

But in order to build ROC curves (as in here: http://scikit-learn.org/stable/auto_examples/plot_roc.html), I'm guessing I need the y_test and y_pred to be in a 2D matrix with binary values, of the following shape:number_of_test_cases x number_of_classes.

每列重presents一个类，并重新1 presents的分类识别给定的测试样品行此类的事实。

Where each column represents one class, and 1 represents the fact that the classifier recognized this class on the given test sample row.

因此​​，考虑上述几个值我发现，我明白我需要y_test看起来是这样的：

So given the above few values I showed, I understand I need y_test to look something like this:

y_test = [ 1 0 ...
           1 0
           1 0
           0 1
           1 0
           0 1
           0 0
           0 0
           ...

这是我的理解...我希望我是对的！

This is what I understand... I hope I'm right!

是否有任何 numpy的函数来创建从一维数组这样的矩阵？

Is there any numpy function to create such a matrix from a 1D array?

推荐答案

有一个看的是在例子code引用您的链接功能。

Have a look at the label_binarize function that's referenced in the example code in your link.

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