3.6.10.2. Demo PCA in 2D

Load the iris data

from sklearn import datasets
iris = datasets.load_iris()
X = iris.data
y = iris.target

Fit a PCA

from sklearn.decomposition import PCA
pca = PCA(n_components=2, whiten=True)
pca.fit(X)

Project the data in 2D

X_pca = pca.transform(X)

Visualize the data

target_ids = range(len(iris.target_names))

from matplotlib import pyplot as plt
plt.figure(figsize=(6, 5))
for i, c, label in zip(target_ids, 'rgbcmykw', iris.target_names):
    plt.scatter(X_pca[y == i, 0], X_pca[y == i, 1],
               c=c, label=label)
plt.legend()
plt.show()

Total running time of the script: ( 0 minutes 0.020 seconds)