更改每个类的集群颜色

Question

我有一个散点图，我正在绘制 14 个集群，但每 2 个集群属于同一类，它们以 14 种不同的颜色绘制，但我只需要 2 种颜色。因此，对于 14 个集群，我将只有 2 个不同的类，因为它们将成对分组。我的集群在我的数据框中从 A(1) 枚举到 N(14)。我想要做的是通过它们的颜色来识别每个集群，例如每个奇数得到蓝色，每个偶数得到红色。或者如果你按字母顺序：A 变蓝，B 变红，C 变蓝，D 变红，E 变蓝等等。

Link for the Data Frame

import pandas as pd
import numpy as np
from matplotlib import pyplot as plt

y = [0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0, 0,  0,  0,  0,  0,  0,
  0,  0,  0,  0, 0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,
  0,  0,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,
  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,
  1,  1,  1,  1,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,
  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,
  2,  2,  2,  2,  2,  2,  3,  3,  3,  3,  3,  3,  3,  3,  3,  3,  3,  3,  3,  3,  3,  3,  3,  3,
  3,  3,  3,  3,  3,  3,  3,  3,  3,  3,  3,  3,  3,  3,  3,  3,  3,  3,  3,  3,  3,  3,  3,  3,
  3,  3,  3,  3,  3,  3,  3,  3,  4,  4,  4,  4,  4,  4,  4,  4,  4,  4,  4,  4,  4,  4,  4,  4,
  4,  4,  4,  4,  4,  4,  4,  4,  4,  4,  4,  4,  4,  4,  4,  4,  4,  4,  4,  4,  4,  4,  4,  4,
  4,  4,  4,  4,  4,  4,  4,  4,  4,  4,  5,  5,  5,  5,  5,  5,  5,  5,  5,  5,  5,  5,  5,  5,
  5,  5,  5,  5,  5,  5,  5,  5,  5,  5,  5,  5,  5,  5,  5,  5,  5,  5,  5,  5,  5,  5,  5,  5,
  5,  5,  5,  5,  5,  5,  5,  5,  5,  5,  5,  5,  6,  6,  6,  6,  6,  6,  6,  6,  6,  6,  6,  6,
  6,  6,  6,  6,  6,  6,  6,  6,  6,  6,  6,  6,  6,  6,  6,  6,  6,  6,  6,  6,  6,  6,  6,  6,
  6,  6,  6,  6,  6,  6,  6,  6,  6,  6,  6,  6,  6,  6,  7,  7,  7,  7,  7,  7,  7,  7,  7,  7,
  7,  7,  7,  7,  7,  7,  7,  7,  7,  7,  7,  7,  7,  7,  7,  7,  7,  7,  7,  7,  7,  7,  7,  7,
  7,  7,  7,  7,  7,  7,  7,  7,  7,  7,  7,  7,  7,  7,  7,  7,  8,  8,  8,  8,  8,  8,  8,  8,
  8,  8,  8,  8,  8,  8,  8,  8,  8,  8,  8,  8,  8,  8,  8,  8,  8,  8,  8,  8,  8,  8,  8,  8,
  8,  8,  8,  8,  8,  8,  8,  8,  8,  8,  8,  8,  8,  8,  8,  8,  8,  8,  9,  9,  9,  9,  9,  9,
  9,  9,  9,  9,  9,  9,  9,  9,  9,  9,  9,  9,  9,  9,  9,  9,  9,  9,  9,  9,  9,  9,  9,  9,
  9,  9,  9,  9,  9,  9,  9,  9,  9,  9,  9,  9,  9,  9,  9,  9,  9,  9,  9,  9, 10, 10, 10, 10,
 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10,
 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 11, 11,
 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11,
 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11,
 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12,
 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12,
 12, 12, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13,
 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13,
 13, 13, 13, 13]
X_lda = pd.read_pickle('lda_values')
X_lda = np.asarray(X_lda)


plt.xlabel('1-eigenvector')
plt.ylabel('2-eigenvector')
plt.scatter(
    X_lda[:,0],
    X_lda[:,1],
    c=y,
    cmap='rainbow',
    alpha=0.7,
    edgecolors='w'
)

这是我可以从包含大量数据的大型代码中获得的最小可重现示例。

我正在做的是我为 y 中的不同类设置不同的颜色，X_lda 中的数据，其中前 50 行是蓝色，从 50 到 100 是红色，101-150 蓝色，151-200 红色。等等。

这是实际的情节：

这或多或少是我想要达到的目标。

Answer 1

您可以使用下面的示例来绘制数据。假设有五个簇分别为 0,1,2,3,4，并且您想对 簇 0,2,4 和 红色中的样本使用 蓝色 集群 1,3 中的样本的颜色。

import numpy as np
from matplotlib import pyplot as plt

X_lda=np.array([[1,2],[1,1],[3,3],[4,4],[2,4],[3,5],[3,4],[3,2]]) # suppose you want to plot X

y=[0,1,1,1,2,3,4,4] # the cluster of each sample in X_lda 

color=['b','r'] # You want to use blue for samples in clusters 0,2,4 and red color for samples in clusters 1,3 
X_lda_colors=  [ color[i] for i in list(np.array(y)%2) ] # get the color for each sample in X_lda

plt.xlabel('1-eigenvector')
plt.ylabel('2-eigenvector')
plt.scatter(
    X_lda[:,0],
    X_lda[:,1],
    c=X_lda_colors,
    cmap='rainbow',
    alpha=0.7,
    edgecolors='w'
)
plt.show()