demo25.py

import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from sklearn import datasets
from sklearn.decomposition import PCA

iris = datasets.load_iris()

X = iris.data
species = iris.target

fig = plt.figure(1, figsize=(8, 8))
ax = Axes3D(fig, elev=-150, azim=110)
X_reduced = PCA(n_components=3).fit_transform(iris.data)
ax.scatter(X_reduced[:, 0], X_reduced[:, 1], X_reduced[:, 2], c=species, cmap=plt.cm.Paired)

plt.show()
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from sklearn import datasets
from sklearn.decomposition import PCA

iris = datasets.load_iris()

X = iris.data
species = iris.target

fig = plt.figure(1, figsize=(8, 8))
ax = Axes3D(fig, elev=-150, azim=110)
print(plt.cm.get_cmap())
# print(d)

# magma, plasma, viridis
# https://matplotlib.org/3.1.0/tutorials/colors/colormaps.html
X_reduced = PCA(n_components=3).fit_transform(iris.data)
ax.scatter(X_reduced[:, 0], X_reduced[:, 1], X_reduced[:, 2], c=species, cmap=plt.get_cmap("viridis"))

ax.set_title("3 CPA direction")
ax.set_xlabel("1st eigen value")
ax.set_ylabel("2nd eigne value")
ax.set_zlabel("3rd eigen value")
ax.w_xaxis.set_ticklabels([])
ax.w_yaxis.set_ticklabels([])
ax.w_zaxis.set_ticklabels([])
plt.show()