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pythonscikit-learnvisualizationpca

How to visualize high-dimension vectors as points in 2D plane?

For example, there are three vectors as below.

[ 0.0377,  0.1808,  0.0807, -0.0703,  0.2427, -0.1957, -0.0712, -0.2137,
     -0.0754, -0.1200,  0.1919,  0.0373,  0.0536,  0.0887, -0.1916, -0.1268,
     -0.1910, -0.1411, -0.1282,  0.0274, -0.0781,  0.0138, -0.0654,  0.0491,
      0.0398,  0.1696,  0.0365,  0.2266,  0.1241,  0.0176,  0.0881,  0.2993,
     -0.1425, -0.2535,  0.1801, -0.1188,  0.1251,  0.1840,  0.1112,  0.3172,
      0.0844, -0.1142,  0.0662,  0.0910,  0.0416,  0.2104,  0.0781, -0.0348,
     -0.1488,  0.0129],
 [-0.1302,  0.1581, -0.0897,  0.1024, -0.1133,  0.1076,  0.1595, -0.1047,
      0.0760,  0.1092,  0.0062, -0.1567, -0.1448, -0.0548, -0.1275, -0.0689,
     -0.1293,  0.1024,  0.1615,  0.0869,  0.2906, -0.2056,  0.0442, -0.0595,
     -0.1448,  0.0167, -0.1259, -0.0989,  0.0651, -0.0424,  0.0795, -0.1546,
      0.1330, -0.2284,  0.1672,  0.1847,  0.0841,  0.1771, -0.0101, -0.0681,
      0.1497,  0.1226,  0.1146, -0.2090,  0.3275,  0.0981, -0.3295,  0.0590,
      0.1130, -0.0650],
 [-0.1745, -0.1940, -0.1529, -0.0964,  0.2657, -0.0979,  0.1510, -0.1248,
     -0.1541,  0.1782, -0.1769, -0.2335,  0.2011,  0.1906, -0.1918,  0.1896,
     -0.2183, -0.1543,  0.1816,  0.1684, -0.1318,  0.2285,  0.1784,  0.2260,
     -0.2331,  0.0523,  0.1882,  0.1764, -0.1686,  0.2292]

How to plot them as three points in the same 2D plane like this picture below? Thanks!

In this picture, the three points represents three vectors for the three words

Solution

  • I use PCA from sklearn, maybe this code help you:

    import matplotlib.pyplot as plt
import numpy as np
from sklearn.decomposition import PCA

usa = [ 0.0377,  0.1808,  0.0807, -0.0703,  0.2427, -0.1957, -0.0712, -0.2137,
     -0.0754, -0.1200,  0.1919,  0.0373,  0.0536,  0.0887, -0.1916, -0.1268,
     -0.1910, -0.1411, -0.1282,  0.0274, -0.0781,  0.0138, -0.0654,  0.0491,
      0.0398,  0.1696,  0.0365,  0.2266,  0.1241,  0.0176,  0.0881,  0.2993,
     -0.1425, -0.2535,  0.1801, -0.1188,  0.1251,  0.1840,  0.1112,  0.3172,
      0.0844, -0.1142,  0.0662,  0.0910,  0.0416,  0.2104,  0.0781, -0.0348,
     -0.1488,  0.0129]
obama =  [-0.1302,  0.1581, -0.0897,  0.1024, -0.1133,  0.1076,  0.1595, -0.1047,
      0.0760,  0.1092,  0.0062, -0.1567, -0.1448, -0.0548, -0.1275, -0.0689,
     -0.1293,  0.1024,  0.1615,  0.0869,  0.2906, -0.2056,  0.0442, -0.0595,
     -0.1448,  0.0167, -0.1259, -0.0989,  0.0651, -0.0424,  0.0795, -0.1546,
      0.1330, -0.2284,  0.1672,  0.1847,  0.0841,  0.1771, -0.0101, -0.0681,
      0.1497,  0.1226,  0.1146, -0.2090,  0.3275,  0.0981, -0.3295,  0.0590,
      0.1130, -0.0650]
nationality =  [-0.1745, -0.1940, -0.1529, -0.0964,  0.2657, -0.0979,  0.1510, -0.1248,
     -0.1541,  0.1782, -0.1769, -0.2335,  0.2011,  0.1906, -0.1918,  0.1896,
     -0.2183, -0.1543,  0.1816,  0.1684, -0.1318,  0.2285,  0.1784,  0.2260,
     -0.2331,  0.0523,  0.1882,  0.1764, -0.1686,  0.2292]


pca = PCA(n_components=1)

X = np.array(usa).reshape(2,len(usa)//2)
X = pca.fit_transform(X)

Y = np.array(obama).reshape(2,len(obama)//2)
Y = pca.fit_transform(Y)

Z = np.array(nationality).reshape(2,len(nationality)//2)
Z = pca.fit_transform(Z)



x_coordinates = [X[0][0], Y[0][0], Z[0][0]]
y_coordinates = [X[1][0], Y[1][0], Z[1][0]]
colors = ['r','g','b']
annotations=["U.S.A","Obama","Nationality"]

plt.figure(figsize=(8,6))
plt.scatter(x_coordinates, y_coordinates, marker=",", color=colors,s=300)

for i, label in enumerate(annotations):
    plt.annotate(label, (x_coordinates[i], y_coordinates[i]))
    

plt.show()

    output:

    enter image description here