Dataset loader moved to datasets.base, but not being installed

MANIFEST.in

@@ -4,4 +4,5 @@ include scikits/__init__.py
 recursive-include doc *
 recursive-include examples *
 recursive-include scikits *.c *.h *.pyx
-recursive-include scikits/learn/datasets *.csv *.csv.gz *.TXT *.rst
+recursive-include scikits/learn/datasets *.csv *.csv.gz *.TXT *.rst 
+recursive-include scikits/learn/datasets/images *.jpg *.txt

examples/cluster/vq_china.py

@@ -10,15 +10,26 @@
 print __doc__
 
 import numpy as np
-from PIL import Image
+import pylab as pl
 from scikits.learn.cluster import KMeans
-from pylab import figure, axes, imshow, show
+from scikits.learn.datasets import load_sample_images
+# Try to import Image and imresize from PIL. We do this here to prevent
+# this module from depending on PIL.
+try:
+    try:
+        from scipy.misc import Image
+    except ImportError:
+        from scipy.misc.pilutil import Image
+except ImportError:
+    raise ImportError("The Python Imaging Library (PIL)"
+                      "is required to load data from jpeg files")
 
-# Load Image
-filename = "/home/bob/Code/scikit-learn/scikits/learn/datasets/images/china.jpg"
+# Get all sample images and obtain just china.jpg
+sample_images = load_sample_images()
+index = sample_images.filenames.index("china.jpg")
+image_data = sample_images.images[index]
 
-# Transform to numpy array
-image_data = np.asarray(Image.open(filename))
+# Load Image and transform to a 2D numpy array.
 w, h, d = original_shape = tuple(image_data.shape)
 image_array = np.reshape(image_data, (w * h, 3))
 
@@ -28,7 +39,7 @@
 sample_indices = sample_indices[:int(len(image_array) * 0.5)]
 sample_data = image_array[sample_indices]
 
-# Perform Vector Quantisation with 256 clusters
+# Perform Vector Quantisation with 256 clusters.
 k = 256
 kmeans = KMeans(k=k)
 kmeans.fit(image_array)
@@ -50,11 +61,11 @@ def recreate_image(centroids, labels, w, h):
     return image
 
 # Display all results, alongside original image
-figure()
-ax = axes([0,0,1,1], frameon=False)
+pl.figure()
+ax = pl.axes([0,0,1,1], frameon=False)
 ax.set_axis_off()
 centroids, labels = reduced_image
-im = imshow(recreate_image(centroids, labels, w, h))
+im = pl.imshow(recreate_image(centroids, labels, w, h))
 
 show()
 

scikits/learn/datasets/__init__.py

@@ -6,6 +6,7 @@
 from .base import load_boston
 from .base import get_data_home
 from .base import clear_data_home
+from .base import load_sample_images
 from .mlcomp import load_mlcomp
 from .lfw import load_lfw_pairs
 from .lfw import load_lfw_people

scikits/learn/datasets/base.py

@@ -347,3 +347,39 @@ def load_boston():
                  feature_names=feature_names,
                  DESCR=fdescr.read())
 
+
+def load_sample_images():
+    """ Load sample images for image manipulation.
+
+    Return
+    ------
+    data : Bunch
+        Dictionary-like object, the interesting attributes are:
+        'data', the data to learn, `images`, the images corresponding
+        to each sample, 'target', the classification labels for each
+        sample, 'target_names', the meaning of the labels, and 'DESCR',
+        the full description of the dataset.
+
+    Examples
+    --------
+    To load the data and visualize the images::
+
+    >>> from scikits.learn.datasets import load_sample_images
+    >>> images = load_sample_images()
+
+    >>> # import pylab as pl
+    >>> # pl.gray()
+    >>> # pl.matshow(images.images[0]) # Visualize the first image
+    >>> # pl.show()
+    """
+    module_path = join(dirname(__file__), "images")
+    descr = open(join(module_path, 'README.txt')).read()
+    filenames = [filename for filename in os.listdir(module_path)
+                 if filename.endswith(".jpg")]
+    # Load image data for each image in the source folder.
+    images = [np.asarray(Image.open(filename))
+              for filename in filenames]
+    return Bunch(images=images,
+                 filenames=filenames,
+                 DESCR=descr)
+