Clustering Comparison

Clustering comparison¶

Generate datasets¶

We choose the size big enough to see the scalability of the algorithms, but we don't want the example to take too long.

In [1]:

import numpy as np
from sklearn import datasets
from collections import OrderedDict

np.random.seed(0)
n_samples = 2500
ds = OrderedDict()

In [2]:

ds['noisy_circles'] = datasets.make_circles(
    n_samples=n_samples, factor=.5, noise=.05)

In [3]:

ds['noisy_moons'] = datasets.make_moons(
    n_samples=n_samples, noise=.05)

In [4]:

ds['blobs'] = np.concatenate([
    np.array([[0,-1]]) + 0.5 * np.random.randn(n_samples//3, 2),
    np.array([[5, 0]]) + 0.1 * np.random.randn(n_samples//3, 2),
    np.array([[0, 5]]) + 2.0 * np.random.randn(n_samples//3, 2),
]), None

In [5]:

ds['no_structure'] = np.random.rand(n_samples, 2), None

In [6]:

# Scale the example data
from sklearn.preprocessing import StandardScaler
ds = OrderedDict((name, StandardScaler().fit_transform(X))
                 for name, (X, y) in ds.items())

Enumerate clustering choices¶

In [7]:

from msmbuilder import cluster
algos = [
    (cluster.KCenters, 3),
    (cluster.RegularSpatial, 2),
    (cluster.KMeans, 3),
    (cluster.MiniBatchKMeans, 3),
    (cluster.KMedoids, 3),
    (cluster.MiniBatchKMedoids, 3),
]

/home/travis/miniconda3/envs/docenv/lib/python3.6/site-packages/sklearn/cross_validation.py:41: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.
  "This module will be removed in 0.20.", DeprecationWarning)
/home/travis/miniconda3/envs/docenv/lib/python3.6/site-packages/sklearn/grid_search.py:42: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. This module will be removed in 0.20.
  DeprecationWarning)

Perform clustering¶

In [8]:

import time

results = {}
for ds_name, X in ds.items():    
    for algo, param in algos:
        algorithm = algo(param)
        t0 = time.time()
        algorithm.fit([X])
        t1 = time.time()
        
        if hasattr(algorithm, 'labels_'):
            y_pred = algorithm.labels_[0].astype(np.int)
        else:
            y_pred = algorithm.transform([X])[0]
            
        if hasattr(algorithm, 'cluster_centers_'):
            centers = algorithm.cluster_centers_
        else:
            centers = []
            
        results[ds_name, algo.__name__] = (t1-t0, y_pred, centers) 

Plot¶

In [9]:

%matplotlib inline
import matplotlib.pyplot as plt

colors = np.array([x for x in 'bgrcmykbgrcmykbgrcmykbgrcmyk'])
colors = np.hstack([colors] * 20)
plt.figure(figsize=(14.5, 9.5))
plot_num = 1
titles = True
for ds_name, X in ds.items():
    for algo, param in algos:
        t, y_pred, centers = results[ds_name, algo.__name__]        
        plt.subplot(4, 6, plot_num)        
        if titles:
            plt.title(algo.__name__, size=18)
        plt.scatter(X[:, 0], X[:, 1], color=colors[y_pred].tolist(), s=6)
        center_colors = colors[:len(centers)]
        plt.scatter(centers[:, 0], centers[:, 1], s=100, c=center_colors)
        plt.xlim(-2, 2)
        plt.ylim(-2, 2)
        plt.xticks(())
        plt.yticks(())
        plt.text(.99, .01, '{:.2f}s'.format(t),
                 transform=plt.gca().transAxes, size=20,
                 horizontalalignment='right')
        plot_num += 1
    titles = False
plt.tight_layout()

(Clustering-Comparison.ipynb; Clustering-Comparison.eval.ipynb; Clustering-Comparison.py)