Linear Discriminant Analysis (Error: index 1 is out of bounds)

Question

我有一个数据集。前 10 个数字是我的特征（一、二、...、十），最后一列是我的目标（只有 2 个目标，包括 MID 和 HIGH）。数据以 txt 格式 (data.txt) 保存，如：

200000,400000,5000000,100000,5000000,50000,50000,300000,3333,1333,MID
200000,100000,500000,100000,5000000,5000,50000,300000,2000,1333,MID
100000,400000,5000000,100000,5000000,5000,50000,300000,2000,3333,MID
400000,200000,50000000,100000,5000000,5000,50000,300000,3333,3333,MID
200000,200000,5000000,100000,5000000,5000,50000,300000,3333,1333,HIGH
200000,100000,500000,10000000,5000000,50000,50000,300000,3333,3333,HIGH
100000,200000,500000,100000,5000000,50000,50000,300000,3333,666,HIGH
200000,100000,500000,1000000,5000000,50000,50000,300000,3333,666,HIGH
200000,100000,5000000,1000000,5000000,50000,5000,300000,3333,1333,HIGH

我尝试根据可用的教程实现 LDA 分析。我还使用 StandardScaler 进行归一化，因为 nine 和 ten 列的单位与第一个不同8 列。这是我尝试过的：

import pandas as pd
from matplotlib import pyplot as plt
import numpy as np
import math
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from sklearn.preprocessing import StandardScaler

df = pd.read_csv('data.txt', header=None)
df.columns=['one','two','three','four','five','six','seven','eight','nine','ten','class']
X = df.ix[:,0:10].values
y = df.ix[:,10].values
X_std = StandardScaler().fit_transform(X)

lda = LinearDiscriminantAnalysis(n_components=2)
X_r2 = lda.fit_transform(X_std,y)

with plt.style.context('seaborn-whitegrid'):
    plt.figure(figsize=(8, 6))
    for lab, col in zip(('MID', 'HIGH'),
                        ('blue', 'red')):
        plt.scatter(X_r2[y==lab, 0],
                    X_r2[y==lab, 1],
                    label=lab,s=100,
                    c=col)
    plt.xlabel('LDA 1')
    plt.ylabel('LDA 2')
    plt.legend(loc='lower right')
    plt.tight_layout()
    plt.savefig('Results.png', format='png', dpi=1200)
    plt.show()

我收到这个错误：

line 32, in <module>X_r2[y==lab, 1],
IndexError: index 1 is out of bounds for axis 1 with size 1

有人知道我该如何解决这个问题吗？预先感谢您的帮助。

Answer 1

当您的目标变量只有两个唯一值时，即使您将其指定为 2，LDA 生成的 n_components 也只会是 1。

来自文档：

n_components : int, optional
Number of components (< n_classes - 1) for dimensionality reduction.

因此，如果您在数据集中为以下内容添加一行，

200000,400000,5000000,100000,5000000,50000,50000,300000,3333,1333,LOW

为 y 中的另一个类别更新了代码：

import pandas as pd
from sklearn.preprocessing import StandardScaler
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from matplotlib import pyplot as plt

df.columns=['one','two','three','four','five','six','seven','eight','nine','ten','class']
X = df.ix[:,0:10].values
y = df.ix[:,10].values
X_std = StandardScaler().fit_transform(X)

lda = LinearDiscriminantAnalysis(n_components=2)
X_r2 = lda.fit_transform(X_std,y)

with plt.style.context('seaborn-whitegrid'):
    plt.figure(figsize=(8, 6))
    for lab, col in zip(('MID', 'HIGH','LOW'),
                        ('blue', 'red','green')):
        plt.scatter(X_r2[y==lab, 0],
                    X_r2[y==lab, 1],
                    label=lab,s=100,
                    c=col)
    plt.xlabel('LDA 1')
    plt.ylabel('LDA 2')
    plt.legend(loc='lower right')
    plt.tight_layout()
    plt.savefig('Results.png', format='png', dpi=1200)
    plt.show()

会生成如下剧情！

Linear Discriminant Analysis (Error: index 1 is out of bounds)

Linear Discriminant Analysis (Error: index 1 is out of bounds)

python-3.x

dimensionality-reduction

scikit-learn

linear-discriminant