diff --git a/examples/lda.py b/examples/lda.py
index 149cc61421a5452912976f066d79bdb0027835a1..8560026554d0f6c05d3dc255cc3b3306898afc0a 100644
--- a/examples/lda.py
+++ b/examples/lda.py
@@ -16,7 +16,7 @@ import pylab as pl
# The IRIS dataset
from scikits.learn import datasets, svm
-iris = datasets.load('iris')
+iris = datasets.load_iris()
# Some noisy data not correlated
E = np.random.normal(size=(len(iris.data), 35))
diff --git a/examples/naive_bayes.py b/examples/naive_bayes.py
index d4a39baa202a817191f6700dfbd1d118ad459c71..86b3a4cf3f47c0e5498dea0ff9b1473d986815bd 100644
--- a/examples/naive_bayes.py
+++ b/examples/naive_bayes.py
@@ -16,7 +16,7 @@ import pylab as pl
# The IRIS dataset
from scikits.learn import datasets, svm
-iris = datasets.load('iris')
+iris = datasets.load_iris()
X = iris.data
@@ -29,4 +29,4 @@ gnb = GNB()
y_pred = gnb.fit(X, y).predict(X)
-print "Number of mislabeled points : %d"%(y != y_pred).sum()
+print "Number of mislabeled points : %d" % (y != y_pred).sum()
diff --git a/examples/plot_feature_selection.py b/examples/plot_feature_selection.py
index 5d5bca900a9984068de40d92622273a4b07e47d1..063b67ffc202219164692f0f3515f3dd47b5705b 100644
--- a/examples/plot_feature_selection.py
+++ b/examples/plot_feature_selection.py
@@ -28,7 +28,7 @@ import pylab as pl
# The IRIS dataset
from scikits.learn import datasets, svm
-iris = datasets.load('iris')
+iris = datasets.load_iris()
# Some noisy data not correlated
E = np.random.normal(size=(len(iris.data), 35))
diff --git a/examples/plot_neighbors.py b/examples/plot_neighbors.py
index c1c932cd4deae6ae5c972e35d5f166b296e86b81..3c22fe3e389bacb87511a37582e5b0d0b3aeebec 100644
--- a/examples/plot_neighbors.py
+++ b/examples/plot_neighbors.py
@@ -11,7 +11,7 @@ import pylab as pl
from scikits.learn import neighbors, datasets
# import some data to play with
-iris = datasets.load('iris')
+iris = datasets.load_iris()
X = iris.data[:, :2] # we only take the first two features. We could
# avoid this ugly slicing by using a two-dim dataset
Y = iris.target
diff --git a/examples/plot_svm.py b/examples/plot_svm.py
index 6d4a56f1730b85c77b611221ccc24b06aed20bab..3a1aadade32a17371531d70db004bdce4fc9a4e0 100644
--- a/examples/plot_svm.py
+++ b/examples/plot_svm.py
@@ -11,7 +11,7 @@ import pylab as pl
from scikits.learn import svm, datasets
# import some data to play with
-iris = datasets.load('iris')
+iris = datasets.load_iris()
X = iris.data[:, :2] # we only take the first two features. We could
# avoid this ugly slicing by using a two-dim dataset
Y = iris.target
diff --git a/scikits/learn/datasets/__init__.py b/scikits/learn/datasets/__init__.py
index d89713f7dfcbdee04d867a88c89cf1eab9e8c2b2..15bbd36d9f12ce1ca344c9b64d93b547b360790f 100644
--- a/scikits/learn/datasets/__init__.py
+++ b/scikits/learn/datasets/__init__.py
@@ -1 +1 @@
-from base import load
+from base import load_iris, load_digits
diff --git a/scikits/learn/datasets/base.py b/scikits/learn/datasets/base.py
index 76e0e3f88be37112aa213c9c9d7871af9840bd18..f4de58c45e6ed407fa65020753dda0b436dc4b8d 100644
--- a/scikits/learn/datasets/base.py
+++ b/scikits/learn/datasets/base.py
@@ -1,67 +1,101 @@
"""
-Base object for all datasets
+Base IO code for all datasets
"""
# Copyright (c) 2007 David Cournapeau <cournape@gmail.com>
# 2010 Fabian Pedregosa <fabian.pedregosa@inria.fr>
#
+import csv
+import os
import numpy as np
-class Bunch(dict):
- """
- Container for dataset.
- Members
- -------
- - data : a record array with the actual data
- - label : label[i] = label index of data[i]
- - class : class[i] is the string corresponding to label index i.
- - COPYRIGHT, TITLE, SOURCE, DESCRSHORT, DESCRLONG,
- NOTE. Information about the dataset.
+class Bunch(dict):
+ """ Container object for datasets: dictionnary-like object that
+ exposes its keys as attributes.
"""
def __init__(self, **kwargs):
dict.__init__(self, kwargs)
self.__dict__ = self
-def load(dataset):
- """load the data and returns them.
+
+################################################################################
+
+def load_iris():
+ """load the iris dataset and returns it.
Returns
-------
data : Bunch
- See docstring of bunch for a complete description of its members.
-
- Available datasets
- - iris
+ Dictionnary-like object, the interesting attributes are:
+ 'data', the data to learn, 'target', the classification labels,
+ 'target_names', the meaning of the labels, and 'DESCR', the
+ full description of the dataset.
Example
-------
Let's say you are interested in the samples 10, 25, and 50, and want to
know their class name.
- >>> data = load()
- >>> print data.label #doctest: +ELLIPSIS
- [ 0. 0. ...]
+ >>> data = load_iris()
+ >>> print data.target[[10, 25, 50]]
+ [0 0 1]
+ >>> print data.target_names[data.target[[10, 25, 50]]]
+ ['setosa' 'setosaosa' 'versicolor']
"""
- import csv
- import os
- firis = csv.reader(open(os.path.dirname(__file__)
- + '/data/%s.csv' % dataset))
+ data_file = csv.reader(open(os.path.dirname(__file__)
+ + '/data/iris.csv'))
fdescr = open(os.path.dirname(__file__)
- + '/descr/%s.rst' % dataset)
- temp = firis.next()
- nsamples = int(temp[0])
- nfeat = int(temp[1])
- targetnames = temp[2:]
- data = np.empty((nsamples, nfeat))
- target = np.empty((nsamples,))
- for i, ir in enumerate(firis):
+ + '/descr/iris.rst')
+ temp = data_file.next()
+ n_samples = int(temp[0])
+ n_features = int(temp[1])
+ target_names = np.array(temp[2:])
+ data = np.empty((n_samples, n_features))
+ target = np.empty((n_samples,), dtype=np.int)
+ for i, ir in enumerate(data_file):
data[i] = np.asanyarray(ir[:-1], dtype=np.float)
- target[i] = np.asanyarray(ir[-1], dtype=np.float)
- return Bunch(data=data, target=target, targetnames=targetnames,
+ target[i] = np.asanyarray(ir[-1], dtype=np.int)
+ return Bunch(data=data, target=target, target_names=target_names,
+ DESCR=fdescr.read())
+
+def load_digits():
+ """load the digits dataset and returns it.
+
+ Returns
+ -------
+ data : Bunch
+ Dictionnary-like object, the interesting attributes are:
+ 'data', the data to learn, `raw_data`, 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.
+
+ Example
+ -------
+ To load the data and visualize the images::
+
+ import pylab as pl
+ digits = datasets.load_digits()
+ pl.gray()
+ # Visualize the first image:
+ pl.matshow(digits.raw_data[0])
+
+"""
+
+ data = np.loadtxt(os.path.join(os.path.dirname(__file__)
+ + '/data/digits.csv.gz'), delimiter=',')
+ fdescr = open(os.path.join(os.path.dirname(__file__)
+ + '/descr/iris.rst'))
+ target = data[:, -1]
+ flat_data = data[:, :-1]
+ images = flat_data.view()
+ images.shape = (-1, 8, 8)
+ return Bunch(data=data, target=target, target_names=np.arange(10),
+ raw_data=images,
DESCR=fdescr.read())
diff --git a/scikits/learn/datasets/data/digits.csv.gz b/scikits/learn/datasets/data/digits.csv.gz
new file mode 100644
index 0000000000000000000000000000000000000000..e191a13019b607d4477d1031503ab46218e21040
Binary files /dev/null and b/scikits/learn/datasets/data/digits.csv.gz differ
diff --git a/scikits/learn/datasets/descr/digits.rst b/scikits/learn/datasets/descr/digits.rst
new file mode 100644
index 0000000000000000000000000000000000000000..c8d92ecb3072d84593f46fd5ad069df4bbb48e00
--- /dev/null
+++ b/scikits/learn/datasets/descr/digits.rst
@@ -0,0 +1,55 @@
+ Optical Recognition of Handwritten Digits Data Set
+
+Data Set Characteristics:
+
+Source
+-------
+
+Creator: E. Alpaydin (alpaydin '@' boun.edu.tr)
+Date: July; 1998
+
+This is a copy of the test set of the UCI ML hand-written digits datasets
+
+The data set contains images of hand-written digits: 10 classes where
+each class refers to a digit.
+
+Preprocessing programs made available by NIST were used to extract
+normalized bitmaps of handwritten digits from a preprinted form. From a
+total of 43 people, 30 contributed to the training set and different 13
+to the test set. 32x32 bitmaps are divided into nonoverlapping blocks of
+4x4 and the number of on pixels are counted in each block. This generates
+an input matrix of 8x8 where each element is an integer in the range
+0..16. This reduces dimensionality and gives invariance to small
+distortions.
+
+For info on NIST preprocessing routines, see M. D. Garris, J. L. Blue, G.
+T. Candela, D. L. Dimmick, J. Geist, P. J. Grother, S. A. Janet, and C.
+L. Wilson, NIST Form-Based Handprint Recognition System, NISTIR 5469,
+1994.
+
+
+References
+-----------
+
+ - C. Kaynak (1995) Methods of Combining Multiple Classifiers and Their
+ Applications to Handwritten Digit Recognition, MSc Thesis, Institute of
+ Graduate Studies in Science and Engineering, Bogazici University.
+ - E. Alpaydin, C. Kaynak (1998) Cascading Classifiers, Kybernetika.
+ - Ken Tang and Ponnuthurai N. Suganthan and Xi Yao and A. Kai Qin.
+ Linear dimensionalityreduction using relevance weighted LDA. School of
+ Electrical and Electronic Engineering Nanyang Technological University.
+ 2005.
+ - Claudio Gentile. A New Approximate Maximal Margin Classification
+ Algorithm. NIPS. 2000.
+ - ...
+
+
+Number of Instances: 5620
+
+Number of Attributes: 64
+
+Attribute Information: 8x8 image of integer pixels in the range 0..16.
+
+Missing Attribute Values: None
+
+
diff --git a/scikits/learn/datasets/descr/iris.rst b/scikits/learn/datasets/descr/iris.rst
index 62a2d8e48404beb4da452a71bd95a173d69dd6ee..c5f4e1fbfb7ad264ac9d12f7ef81a5d5b1625463 100644
--- a/scikits/learn/datasets/descr/iris.rst
+++ b/scikits/learn/datasets/descr/iris.rst
@@ -62,8 +62,3 @@ Missing Attribute Values: None
Class Distribution: 33.3% for each of 3 classes.
-Example
--------
- >>> data = load()
- >>> print data.label #doctest: +ELLIPSIS
- [ 0. 0. ...][ 0. 0. ...]