At what part does the iris data receive a labeled cluster?

Question

I'm going to be using sklearn to cluster data for a project I have with my company. For the beginning part I have to demonstrate that I am able to cluster data. In R this would be no problem for me, but R isn't so easy to use with HBase. I don't want to tarry but the problem is that I don't know at what point the data points receive labels. Also, this is a 3D plot, so why does X (iris.data) have 4 numbers ([ 5.4 3.9 1.3 0.4]) per datapoint?

What I truly need out of this is to know which data point corresponds to which cluster. I don't need the visual.

Here's the code (pulled from here)

import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D


from sklearn.cluster import KMeans
from sklearn import datasets

np.random.seed(5)

centers = [[1, 1], [-1, -1], [1, -1]]
iris = datasets.load_iris()
X = iris.data
y = iris.target

estimators = {'k_means_iris_3': KMeans(n_clusters=3),
              'k_means_iris_8': KMeans(n_clusters=8),
              'k_means_iris_bad_init': KMeans(n_clusters=3, n_init=1,
                                              init='random')}


fignum = 1
for name, est in estimators.items():
    fig = plt.figure(fignum, figsize=(4, 3))
    plt.clf()
    ax = Axes3D(fig, rect=[0, 0, .95, 1], elev=48, azim=134)

    plt.cla()
    est.fit(X)
    labels = est.labels_

    ax.scatter(X[:, 3], X[:, 0], X[:, 2], c=labels.astype(np.float))

    ax.w_xaxis.set_ticklabels([])
    ax.w_yaxis.set_ticklabels([])
    ax.w_zaxis.set_ticklabels([])
    ax.set_xlabel('Petal width')
    ax.set_ylabel('Sepal length')
    ax.set_zlabel('Petal length')
    fignum = fignum + 1

# Plot the ground truth
fig = plt.figure(fignum, figsize=(4, 3))
plt.clf()
ax = Axes3D(fig, rect=[0, 0, .95, 1], elev=48, azim=134)

plt.cla()

for name, label in [('Setosa', 0),
                    ('Versicolour', 1),
                    ('Virginica', 2)]:
    ax.text3D(X[y == label, 3].mean(),
              X[y == label, 0].mean() + 1.5,
              X[y == label, 2].mean(), name,
              horizontalalignment='center',
              bbox=dict(alpha=.5, edgecolor='w', facecolor='w'))
# Reorder the labels to have colors matching the cluster results
y = np.choose(y, [1, 2, 0]).astype(np.float)
ax.scatter(X[:, 3], X[:, 0], X[:, 2], c=y)

ax.w_xaxis.set_ticklabels([])
ax.w_yaxis.set_ticklabels([])
ax.w_zaxis.set_ticklabels([])
ax.set_xlabel('Petal width')
ax.set_ylabel('Sepal length')
ax.set_zlabel('Petal length')
plt.show()

Answer 1

Labels

Here is the result of adding two print statements to your code, which will show you when the labels are being generated.

for name, est in estimators.items():
    print est
    fig = plt.figure(fignum, figsize=(4, 3))
    plt.clf()
    ax = Axes3D(fig, rect=[0, 0, .95, 1], elev=48, azim=134)

    plt.cla()
    est.fit(X)
    labels = est.labels_
    print labels

est shows the parameters for the estimator that was used. As you can see the first one has 8 clusters, reflected by 0-7 cluster assignments in the labels.

KMeans(copy_x=True, init='k-means++', max_iter=300, n_clusters=8, n_init=10,
    n_jobs=1, precompute_distances=True, random_state=None, tol=0.0001,
    verbose=0)
[1 5 5 5 1 1 5 1 5 5 1 5 5 5 1 1 1 1 1 1 1 1 5 1 5 5 1 1 1 5 5 1 1 1 5 5 1
 5 5 1 1 5 5 1 1 5 1 5 1 5 2 2 2 7 2 7 2 6 2 7 6 7 7 2 7 2 7 7 2 7 4 7 4 2
 2 2 2 2 2 7 7 7 7 4 7 2 2 2 7 7 7 2 7 6 7 7 7 2 6 7 0 4 3 0 0 3 7 3 0 3 0
 4 0 4 4 0 0 3 3 4 0 4 3 4 0 3 4 4 0 3 3 3 0 4 4 3 0 0 4 0 0 0 4 0 0 0 4 0
 0 4]
KMeans(copy_x=True, init='k-means++', max_iter=300, n_clusters=3, n_init=10,
    n_jobs=1, precompute_distances=True, random_state=None, tol=0.0001,
    verbose=0)
[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
 0 0 0 0 0 0 0 0 0 0 0 0 0 2 2 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
 2 2 2 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 2 1 1 1 1 2 1 1 1 1
 1 1 2 2 1 1 1 1 2 1 2 1 2 1 1 2 2 1 1 1 1 1 2 1 1 1 1 2 1 1 1 2 1 1 1 2 1
 1 2]
KMeans(copy_x=True, init='random', max_iter=300, n_clusters=3, n_init=1,
    n_jobs=1, precompute_distances=True, random_state=None, tol=0.0001,
    verbose=0)
[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
 0 0 0 0 0 0 0 0 0 0 0 0 0 2 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
 1 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 2 2 2 2 1 2 2 2 2
 2 2 1 1 2 2 2 2 1 2 1 2 1 2 2 1 1 2 2 2 2 2 1 2 2 2 2 1 2 2 2 1 2 2 2 1 2
 2 1]

Dimensions

The iris dataset has 4 dimensions (attributes), if you look here, you'll see there are 4 dimensions. The one dimension you aren't plotting in this example is Sepal Width. You can see what each data point corresponds to by putting print iris in after iris = datasets.load_iris(). It prints out a lot of information, but the important information is at the bottom (not so pretty by the way). It looks like this-

:Attribute Information:\n        - sepal length in cm\n        - sepal width in cm\n        - petal length in cm\n        - petal width in cm

The attributes correspond to X[flower][0], X[flower][1], X[flower][2], X[flower][3].

Assignment

To see cluster assignments for each data point add this right below labels = est.labels_:

for flower in range(len(labels)):
        print (X[flower],labels[flower])

will get you the results below, just showing one way to access the data points cluster assignments, you probably don't care to print them, rather store them somewhere meaningful.

(array([ 5.1,  3.5,  1.4,  0.2]), 1)
(array([ 4.9,  3. ,  1.4,  0.2]), 5)
(array([ 4.7,  3.2,  1.3,  0.2]), 5)