Merge pull request #2 from agramfort/sparsepca

Sparse PCA

examples/decomposition/plot_sparse_pca.py

@@ -7,33 +7,37 @@
 the digits dataset containing only the digit 3.
 
 """
-
-
 print __doc__
 
+# Authors: Vlad Niculae, Alexandre Gramfort
+# License: BSD
+
 import numpy as np
-import matplotlib.pyplot as plt
+import pylab as pl
 
 from scikits.learn.decomposition import SparsePCA
 from scikits.learn.datasets import load_digits
 
+###############################################################################
+# Load data and fit the model
 rows, cols = 4, 3
 digits = load_digits()
 threes = digits.data[digits.target == 3]
-threes -= threes.mean(axis=0)  # todo: use preprocessors
-# low tolerance for high speed
-model = SparsePCA(n_components=rows * cols, tol=1e-3)
+threes -= threes.mean(axis=0)  # XXX: use preprocessors
+model = SparsePCA(n_components=rows * cols, alpha=5)
 model.fit(threes)
 span = np.max(np.abs(model.components_))
 
-fig = plt.figure(figsize=(1.2 * cols, 1.4 * rows))
+###############################################################################
+# Plot sparse components
+fig = pl.figure(figsize=(1.2 * cols, 1.4 * rows))
 for i, comp in enumerate(model.components_):
-    plt.subplot(rows, cols, i + 1)
-    plt.imshow(np.reshape(comp, (8, 8)), interpolation='nearest',
-               vmin=-span, vmax=span, cmap=plt.cm.PuOr)
-    plt.xticks(())
-    plt.yticks(())
-plt.subplots_adjust(0.01, 0.15, 0.99, 0.99, 0.04, 0.)
+    pl.subplot(rows, cols, i + 1)
+    pl.imshow(np.reshape(comp, (8, 8)), interpolation='nearest',
+               vmin=-span, vmax=span, cmap=pl.cm.PuOr)
+    pl.xticks(())
+    pl.yticks(())
+pl.subplots_adjust(0.01, 0.15, 0.99, 0.99, 0.04, 0.)
 cax = fig.add_axes([0.1, 0.06, 0.8, 0.04])
-plt.colorbar(cax=cax, orientation='horizontal')
-plt.show()
+pl.colorbar(cax=cax, orientation='horizontal')
+pl.show()

scikits/learn/decomposition/sparse_pca.py

@@ -1,3 +1,8 @@
+""" Matrix factorization with Sparse PCA
+"""
+# Author: Vlad Niculae, Gael Varoquaux, Alexandre Gramfort
+# License: BSD
+
 import time
 import sys
 from math import sqrt

scikits/learn/decomposition/tests/test_sparse_pca.py

@@ -1,3 +1,6 @@
+# Author: Vlad Niculae
+# License: BSD
+
 import numpy as np
 from .. import SparsePCA
 from numpy.testing import assert_array_almost_equal, assert_equal