In [4]:

    

import pandas
import numpy as np
import pandas as pd

#from __future__ import print_function

from sklearn.datasets import make_blobs
from sklearn.cluster import KMeans, DBSCAN
from sklearn.metrics import silhouette_samples, silhouette_score

import matplotlib.pyplot as plt
import matplotlib.cm as cm

%matplotlib inline


    

In [40]:

    

X = np.random.random( (1500, 2))

plt.scatter(X[:, 0], X[:, 1],  c = "white", edgecolor='k')

plt.savefig('random-1500-%d.png'%(n_clusters), bbox_inches='tight')


    

In [41]:

    

from sklearn import metrics
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
X_db = sc.fit_transform(X)
#X_db = X
n_clusters = 2

clusterer = KMeans(n_clusters = n_clusters).fit(X_db)
labels = clusterer.predict(X_db)



print('Number of clusters: %d' % n_clusters)

print("Silhouette Coefficient: %0.3f"
      % metrics.silhouette_score(X_db, labels))

unique_labels = set(labels)
colors = plt.cm.Spectral(np.linspace(0, 1, len(unique_labels)))
for k, col in zip(unique_labels, colors):
    if k == -1:
        # Black used for noise.
        col = 'k'

    class_member_mask = (labels == k)

    xy = X[class_member_mask]
    plt.scatter(xy[:, 0], xy[:, 1],  c = col, edgecolor='k')

    xy = X[class_member_mask]
    plt.scatter(xy[:, 0], xy[:, 1],  c = col, edgecolor='k')

plt.savefig('kmeans-random-1500-%d.png'%(n_clusters), bbox_inches='tight')


    

    




Number of clusters: 2
Silhouette Coefficient: 0.358






    

In [30]:

    

np.set_printoptions(suppress=True)
sc.inverse_transform(clusterer.cluster_centers_)


    

    
Out[30]:





array([[ 0.58056375,  0.22146937],
       [ 0.4041706 ,  0.73634012]])

In [42]:

    

from sklearn import metrics
from sklearn.preprocessing import StandardScaler, MinMaxScaler
X_db = StandardScaler().fit_transform(X)
db = DBSCAN().fit(X_db)
core_samples_mask = np.zeros_like(db.labels_, dtype=bool)
core_samples_mask[db.core_sample_indices_] = True
labels = db.labels_

# Number of clusters in labels, ignoring noise if present.
n_clusters_ = len(set(labels)) - (1 if -1 in labels else 0)

print('Estimated number of clusters: %d' % n_clusters_)

print("Silhouette Coefficient: %0.3f"
      % metrics.silhouette_score(X_db, labels))


    

    




Estimated number of clusters: 1






    




---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-42-f665aefd9f56> in <module>()
     13 
     14 print("Silhouette Coefficient: %0.3f"
---> 15       % metrics.silhouette_score(X_db, labels))

/usr/local/lib/python2.7/dist-packages/sklearn/metrics/cluster/unsupervised.pyc in silhouette_score(X, labels, metric, sample_size, random_state, **kwds)
     98         else:
     99             X, labels = X[indices], labels[indices]
--> 100     return np.mean(silhouette_samples(X, labels, metric=metric, **kwds))
    101 
    102 

/usr/local/lib/python2.7/dist-packages/sklearn/metrics/cluster/unsupervised.pyc in silhouette_samples(X, labels, metric, **kwds)
    164     le = LabelEncoder()
    165     labels = le.fit_transform(labels)
--> 166     check_number_of_labels(len(le.classes_), X.shape[0])
    167 
    168     distances = pairwise_distances(X, metric=metric, **kwds)

/usr/local/lib/python2.7/dist-packages/sklearn/metrics/cluster/unsupervised.pyc in check_number_of_labels(n_labels, n_samples)
     18     if not 1 < n_labels < n_samples:
     19         raise ValueError("Number of labels is %d. Valid values are 2 "
---> 20                          "to n_samples - 1 (inclusive)" % n_labels)
     21 
     22 

ValueError: Number of labels is 1. Valid values are 2 to n_samples - 1 (inclusive)

In [27]:

    

import matplotlib.pyplot as plt

# Black removed and is used for noise instead.
unique_labels = set(labels)
colors = plt.cm.Spectral(np.linspace(0, 1, len(unique_labels)))
n_clusters_ = len(set(labels)) - (1 if -1 in labels else 0)

for k, col in zip(unique_labels, colors):
    if k == -1:
        # Black used for noise.
        col = 'k'
        #continue

    class_member_mask = (labels == k)

    xy = X[class_member_mask & core_samples_mask]
    plt.scatter(xy[:, 0], xy[:, 1],  c = col, edgecolor='k')

    xy = X[class_member_mask & ~core_samples_mask]
    plt.scatter(xy[:, 0], xy[:, 1],  c = col, edgecolor='k')

#plt.title('Estimated number of clusters: %d' % n_clusters_)
plt.savefig('dbscan-random.png', bbox_inches='tight')


    

In [ ]:

    




    

In [ ]: