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PCA example with Iris Data-set¶
Principal Component Analysis applied to the Iris dataset.
See here for more information on this dataset.
# Code source: Gaël Varoquaux
# License: BSD 3 clause
import numpy as np
import matplotlib.pyplot as plt
from sklearn import decomposition
from sklearn import datasets
np.random.seed(5)
iris = datasets.load_iris()
X = iris.data
y = iris.target
fig = plt.figure(1, figsize=(4, 3))
plt.clf()
ax = fig.add_subplot(111, projection="3d", elev=48, azim=134)
ax.set_position([0, 0, 0.95, 1])
plt.cla()
pca = decomposition.PCA(n_components=3)
pca.fit(X)
X = pca.transform(X)
for name, label in [("Setosa", 0), ("Versicolour", 1), ("Virginica", 2)]:
ax.text3D(
X[y == label, 0].mean(),
X[y == label, 1].mean() + 1.5,
X[y == label, 2].mean(),
name,
horizontalalignment="center",
bbox=dict(alpha=0.5, edgecolor="w", facecolor="w"),
)
# Reorder the labels to have colors matching the cluster results
y = np.choose(y, [1, 2, 0]).astype(float)
ax.scatter(X[:, 0], X[:, 1], X[:, 2], c=y, cmap=plt.cm.nipy_spectral, edgecolor="k")
ax.w_xaxis.set_ticklabels([])
ax.w_yaxis.set_ticklabels([])
ax.w_zaxis.set_ticklabels([])
plt.show()
Total running time of the script: ( 0 minutes 0.074 seconds)