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"""
============================
Faces dataset decompositions
============================
This example applies to :ref:`olivetti_faces` different unsupervised
matrix decomposition (dimension reduction) methods from the module
:py:mod:`sklearn.decomposition` (see the documentation chapter
:ref:`decompositions`) .
"""
print __doc__
# Authors: Vlad Niculae, Alexandre Gramfort
# License: BSD
import logging
from time import time
from numpy.random import RandomState
import pylab as pl
from sklearn.datasets import fetch_olivetti_faces
from sklearn.cluster import MiniBatchKMeans
from sklearn import decomposition
# Display progress logs on stdout
logging.basicConfig(level=logging.INFO,
format='%(asctime)s %(levelname)s %(message)s')
n_row, n_col = 2, 3
n_components = n_row * n_col
image_shape = (64, 64)
rng = RandomState(0)
###############################################################################
# Load faces data
dataset = fetch_olivetti_faces(shuffle=True, random_state=rng)
faces = dataset.data
n_samples, n_features = faces.shape
# global centering
faces_centered = faces - faces.mean(axis=0)
# local centering
faces_centered -= faces_centered.mean(axis=1).reshape(n_samples, -1)
print "Dataset consists of %d faces" % n_samples
###############################################################################
def plot_gallery(title, images):
pl.figure(figsize=(2. * n_col, 2.26 * n_row))
pl.suptitle(title, size=16)
for i, comp in enumerate(images):
pl.subplot(n_row, n_col, i + 1)
vmax = max(comp.max(), -comp.min())
pl.imshow(comp.reshape(image_shape), cmap=pl.cm.gray,
interpolation='nearest',
vmin=-vmax, vmax=vmax)
pl.xticks(())
pl.yticks(())
pl.subplots_adjust(0.01, 0.05, 0.99, 0.93, 0.04, 0.)
###############################################################################
# List of the different estimators, whether to center and transpose the
# problem, and whether the transformer uses the clustering API.
estimators = [
('Eigenfaces - RandomizedPCA',
decomposition.RandomizedPCA(n_components=n_components, whiten=True),
True, False),
('Non-negative components - NMF',
decomposition.NMF(n_components=n_components, init='nndsvda', beta=5.0,
tol=5e-3, sparseness='components'),
False, False),
('Independent components - FastICA',
decomposition.FastICA(n_components=n_components, whiten=True,
max_iter=10),
True, True),
('Sparse comp. - MiniBatchSparsePCA',
decomposition.MiniBatchSparsePCA(n_components=n_components, alpha=0.8,
n_iter=100, chunk_size=3,
random_state=rng),
True, False),
('MiniBatchDictionaryLearning',
decomposition.MiniBatchDictionaryLearning(n_atoms=15, alpha=0.1,
n_iter=50, chunk_size=3,
random_state=rng),
True, False),
('Cluster centers - MiniBatchKMeans',
MiniBatchKMeans(k=n_components, tol=1e-3, batch_size=20, max_iter=50,
random_state=rng),
True, False)
]
###############################################################################
# Plot a sample of the input data
plot_gallery("First centered Olivetti faces", faces_centered[:n_components])
###############################################################################
# Do the estimation and plot it
for name, estimator, center, transpose in estimators:
print "Extracting the top %d %s..." % (n_components, name)
t0 = time()
data = faces
if center:
data = faces_centered
if transpose:
data = data.T
estimator.fit(data)
train_time = (time() - t0)
print "done in %0.3fs" % train_time
if hasattr(estimator, 'cluster_centers_'):
components_ = estimator.cluster_centers_
else:
components_ = estimator.components_
if transpose:
components_ = components_.T
plot_gallery('%s - Train time %.1fs' % (name, train_time),
components_[:n_components])
pl.show()
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