This example uses a large dataset of faces to learn a set of 20 x 20 images patches that constitute faces. From the programming standpoint, it is interesting because it shows how to use the online API of the scikit-learn to process a very large dataset by chunks. The way we proceed is that we load an image at a time and extract randomly 50 patches from this image. Once we have accumulated 500 of these patches (using 10 images), we run the partial_fit method of the online KMeans object, MiniBatchKMeans.
The verbose setting on the MiniBatchKMeans enables us to see that some clusters are reassigned during the successive calls to partial-fit. This is because the number of patches that they represent has become too low, and it is better to choose a random new cluster.
import sklearn sklearn.__version__
print(__doc__) import plotly.plotly as py import plotly.graph_objs as go from plotly import tools import time import matplotlib.pyplot as plt import numpy as np from sklearn import datasets from sklearn.cluster import MiniBatchKMeans from sklearn.feature_extraction.image import extract_patches_2d faces = datasets.fetch_olivetti_faces()
Automatically created module for IPython interactive environment
Learn the dictionary of images.
print('Learning the dictionary... ') rng = np.random.RandomState(0) kmeans = MiniBatchKMeans(n_clusters=81, random_state=rng, verbose=True) patch_size = (20, 20) buffer =  index = 1 t0 = time.time() # The online learning part: cycle over the whole dataset 6 times index = 0 for _ in range(6): for img in faces.images: data = extract_patches_2d(img, patch_size, max_patches=50, random_state=rng) data = np.reshape(data, (len(data), -1)) buffer.append(data) index += 1 if index % 10 == 0: data = np.concatenate(buffer, axis=0) data -= np.mean(data, axis=0) data /= np.std(data, axis=0) kmeans.partial_fit(data) buffer =  if index % 100 == 0: print('Partial fit of %4i out of %i' % (index, 6 * len(faces.images))) dt = time.time() - t0 print('done in %.2fs.' % dt)
Learning the dictionary... Partial fit of 100 out of 2400 Partial fit of 200 out of 2400 [MiniBatchKMeans] Reassigning 16 cluster centers. Partial fit of 300 out of 2400 Partial fit of 400 out of 2400 Partial fit of 500 out of 2400 Partial fit of 600 out of 2400 Partial fit of 700 out of 2400 Partial fit of 800 out of 2400 Partial fit of 900 out of 2400 Partial fit of 1000 out of 2400 Partial fit of 1100 out of 2400 Partial fit of 1200 out of 2400 Partial fit of 1300 out of 2400 Partial fit of 1400 out of 2400 Partial fit of 1500 out of 2400 Partial fit of 1600 out of 2400 Partial fit of 1700 out of 2400 Partial fit of 1800 out of 2400 Partial fit of 1900 out of 2400 Partial fit of 2000 out of 2400 Partial fit of 2100 out of 2400 Partial fit of 2200 out of 2400 Partial fit of 2300 out of 2400 Partial fit of 2400 out of 2400 done in 3.78s.
def matplotlib_to_plotly(cmap, pl_entries): h = 1.0/(pl_entries-1) pl_colorscale =  for k in range(pl_entries): C = map(np.uint8, np.array(cmap(k*h)[:3])*255) pl_colorscale.append([k*h, 'rgb'+str((C, C, C))]) return pl_colorscale fig = tools.make_subplots(rows=9, cols=9, print_grid=False) j = 1 for i, patch in enumerate(kmeans.cluster_centers_): trace = go.Heatmap(z=patch.reshape(patch_size), showscale=False, colorscale=matplotlib_to_plotly(plt.cm.gray, len(patch.reshape(patch_size)) )) k = i/9+1 j = j%9 if(j==0): j = 9 fig.append_trace(trace, k, j) j=j+1 fig['layout'].update(title='Patches of faces<br>Train time %.1fs on %d patches' % (dt, 8 * len(faces.images)), height = 1200) for i in map(str,range(1,82)): y = 'yaxis'+ i x = 'xaxis'+i fig['layout'][y].update(autorange='reversed', showticklabels=False, ticks='') fig['layout'][x].update(showticklabels=False, ticks='') py.iplot(fig)