基于分类数据的分类

发布于 2025-01-25 05:39:25 字数 199 浏览 2 评论 0原文

我有一个

Inp1    Inp2        Output
A,B,C   AI,UI,JI    Animals
L,M,N   LI,DO,LI    Noun
X,Y     AI,UI       Extras

用于这些值的数据集，我需要应用ML算法。哪种算法最适合在这些组之间找到关系以将输出类分配给它们？

原文

I have a dataset

Inp1    Inp2        Output
A,B,C   AI,UI,JI    Animals
L,M,N   LI,DO,LI    Noun
X,Y     AI,UI       Extras

For these values, I need to apply a ML algorithm. Which algorithm would be best suited to find relations in between these groups to assign an output class to them?

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冰雪梦之恋 2025-02-01 05:39:25

假设每个单元格是一个列表（因为您在每个单元中存储了多个字符串），并且您不在寻找特定的编码。以下应该有效。它也可以调整以适合不同的编码。

import pandas as pd
A = [["Inp1", "Inp2", "Inp3", "Output"],
[["A","B","C"], ["AI","UI","JI"],["Apple","Bat","Dog"],["Animals"]],
[["L","M","N"], ["LI","DO","LI"], ["Lawn", "Moon", "Noon"], ["Noun"]]]

dataframe = pd.DataFrame(A[1:], columns=A[0])

def my_encoding(row):
    encoded_row = []
    for ls in row:
        encoded_ls = []
        for s in ls:
            sbytes = s.encode('utf-8')
            sint = int.from_bytes(sbytes, 'little')
            encoded_ls.append(sint)
        encoded_row.append(encoded_ls)
    return encoded_row

print(dataframe.apply(my_encoding))

输出：

           Inp1  ...               Output
0  [65, 66, 67]  ...  [32488788024979009]
1  [76, 77, 78]  ...         [1853189966]

如果我的假设不正确，或者这不是您正在寻找的，请告诉我。

Assuming each cell is a list (as you have multiple strings stored in each), and that you are not looking for a specific encoding. The following should work. It can also be adjusted to suit different encodings.

import pandas as pd
A = [["Inp1", "Inp2", "Inp3", "Output"],
[["A","B","C"], ["AI","UI","JI"],["Apple","Bat","Dog"],["Animals"]],
[["L","M","N"], ["LI","DO","LI"], ["Lawn", "Moon", "Noon"], ["Noun"]]]

dataframe = pd.DataFrame(A[1:], columns=A[0])

def my_encoding(row):
    encoded_row = []
    for ls in row:
        encoded_ls = []
        for s in ls:
            sbytes = s.encode('utf-8')
            sint = int.from_bytes(sbytes, 'little')
            encoded_ls.append(sint)
        encoded_row.append(encoded_ls)
    return encoded_row

print(dataframe.apply(my_encoding))

output:

           Inp1  ...               Output
0  [65, 66, 67]  ...  [32488788024979009]
1  [76, 77, 78]  ...         [1853189966]

if my assumptions are incorrect or this is not what you're looking for let me know.

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恋你朝朝暮暮 2025-02-01 05:39:25

如您提到的，您将应用ML算法（例如分类），我认为一个热编码是您想要的。

请求格式：

Inp1     Inp2    Inp3      Output
7,44,87  4,65,2  47,36,20  45

此格式无法帮助您将模型训练为单个单元格中的多个标签。但是，您必须像 ohe 一样再次预处理。

建议格式：< / strong>

A  B  C  L  M  N  X  Y  AI  DO  JI  LI  UI  Apple  Bat  Dog  Lawn  Moon  Noon  Yemen  Zombie
1  1  1  0  0  0  0  0   1   0   1   0   1      1    1    1     0     0     0      0       0
0  0  0  1  1  1  0  0   0   1   0   1   0      0    0    0     1     1     1      0       0
0  0  0  0  0  0  1  1   1   0   0   0   1      0    0    0     0     0     0      1       1

以后您可以按照您的模型要求编码 / OHE输出字段。

愉快的学习！

As you mentioned, you are going to apply ML algorithm (say classification), I think One Hot Encoding is what you are looking for.

Requested format:

Inp1     Inp2    Inp3      Output
7,44,87  4,65,2  47,36,20  45

This format can't help you to train your model as multiple labels in a single cell. However you have to pre-process again like OHE.

Suggesting format:

A  B  C  L  M  N  X  Y  AI  DO  JI  LI  UI  Apple  Bat  Dog  Lawn  Moon  Noon  Yemen  Zombie
1  1  1  0  0  0  0  0   1   0   1   0   1      1    1    1     0     0     0      0       0
0  0  0  1  1  1  0  0   0   1   0   1   0      0    0    0     1     1     1      0       0
0  0  0  0  0  0  1  1   1   0   0   0   1      0    0    0     0     0     0      1       1

Hereafter you can label encode / ohe the output field as per your model requires.

Happy learning !

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