93 lines
2.7 KiB
Python
93 lines
2.7 KiB
Python
# -*- coding: utf-8 -*-
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"""
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=========================================================
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K-means Clustering
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=========================================================
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The plots display firstly what a K-means algorithm would yield
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using three clusters. It is then shown what the effect of a bad
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initialization is on the classification process:
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By setting n_init to only 1 (default is 10), the amount of
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times that the algorithm will be run with different centroid
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seeds is reduced.
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The next plot displays what using eight clusters would deliver
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and finally the ground truth.
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"""
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# Code source: Gaël Varoquaux
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# Modified for documentation by Jaques Grobler
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# License: BSD 3 clause
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import numpy as np
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import matplotlib.pyplot as plt
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# Though the following import is not directly being used, it is required
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# for 3D projection to work with matplotlib < 3.2
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import mpl_toolkits.mplot3d # noqa: F401
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from sklearn.cluster import KMeans
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from sklearn import datasets
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np.random.seed(5)
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iris = datasets.load_iris()
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X = iris.data
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y = iris.target
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estimators = [
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("k_means_iris_8", KMeans(n_clusters=8)),
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("k_means_iris_3", KMeans(n_clusters=3)),
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("k_means_iris_bad_init", KMeans(n_clusters=3, n_init=1, init="random")),
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]
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fignum = 1
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titles = ["8 clusters", "3 clusters", "3 clusters, bad initialization"]
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for name, est in estimators:
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fig = plt.figure(fignum, figsize=(4, 3))
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ax = fig.add_subplot(111, projection="3d", elev=48, azim=134)
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ax.set_position([0, 0, 0.95, 1])
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est.fit(X)
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labels = est.labels_
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ax.scatter(X[:, 3], X[:, 0], X[:, 2], c=labels.astype(float), edgecolor="k")
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ax.w_xaxis.set_ticklabels([])
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ax.w_yaxis.set_ticklabels([])
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ax.w_zaxis.set_ticklabels([])
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ax.set_xlabel("Petal width")
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ax.set_ylabel("Sepal length")
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ax.set_zlabel("Petal length")
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ax.set_title(titles[fignum - 1])
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ax.dist = 12
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fignum = fignum + 1
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# Plot the ground truth
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fig = plt.figure(fignum, figsize=(4, 3))
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ax = fig.add_subplot(111, projection="3d", elev=48, azim=134)
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ax.set_position([0, 0, 0.95, 1])
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for name, label in [("Setosa", 0), ("Versicolour", 1), ("Virginica", 2)]:
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ax.text3D(
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X[y == label, 3].mean(),
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X[y == label, 0].mean(),
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X[y == label, 2].mean() + 2,
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name,
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horizontalalignment="center",
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bbox=dict(alpha=0.2, edgecolor="w", facecolor="w"),
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)
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# Reorder the labels to have colors matching the cluster results
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y = np.choose(y, [1, 2, 0]).astype(float)
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ax.scatter(X[:, 3], X[:, 0], X[:, 2], c=y, edgecolor="k")
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ax.w_xaxis.set_ticklabels([])
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ax.w_yaxis.set_ticklabels([])
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ax.w_zaxis.set_ticklabels([])
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ax.set_xlabel("Petal width")
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ax.set_ylabel("Sepal length")
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ax.set_zlabel("Petal length")
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ax.set_title("Ground Truth")
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ax.dist = 12
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fig.show()
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