iris_kmeans.ipynb

Kernel: Unknown Kernel

# GMM clustering on iris data
# Based on https://github.com/ageron/handson-ml2/blob/master/09_unsupervised_learning.ipynb


import numpy as np
import matplotlib.pyplot as plt
import os

figdir = "figures"


def savefig(fname):
    plt.savefig(os.path.join(figdir, fname))


import seaborn as sns

try:
    from sklearn.datasets import load_iris
except ModuleNotFoundError:
    %pip install -qq scikit-learn
    from sklearn.datasets import load_iris
# from sklearn.cluster import KMeans
from sklearn.mixture import GaussianMixture

iris = load_iris()
X = iris.data

fig, ax = plt.subplots()
idx1 = 2
idx2 = 3
ax.scatter(X[:, idx1], X[:, idx2], c="k", marker=".")
ax.set(xlabel=iris.feature_names[idx1])
ax.set(ylabel=iris.feature_names[idx2])
savefig("iris-2d-unlabeled")
plt.show()


K = 3
y_pred = GaussianMixture(n_components=K, random_state=42).fit(X).predict(X)
mapping = np.array([2, 0, 1])
y_pred = np.array([mapping[cluster_id] for cluster_id in y_pred])

colors = sns.color_palette()[0:K]
markers = ("s", "x", "o", "^", "v")
fig, ax = plt.subplots()
for k in range(0, K):
    ax.plot(
        X[y_pred == k, idx1],
        X[y_pred == k, idx2],
        color=colors[k],
        marker=markers[k],
        linestyle="None",
        label="Cluster {}".format(k),
    )
ax.set(xlabel=iris.feature_names[idx1])
ax.set(ylabel=iris.feature_names[idx2])
plt.legend(loc="upper left", fontsize=12)
savefig("iris-2d-gmm")
plt.show()