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Commit 076938f1 authored by Fabian Pedregosa's avatar Fabian Pedregosa
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Flatten data in Iris dataset 

From: cdavid <cdavid@cb17146a-f446-4be1-a4f7-bd7c5bb65646>

git-svn-id: https://scikit-learn.svn.sourceforge.net/svnroot/scikit-learn/trunk@13 22fbfee3-77ab-4535-9bad-27d1bd3bc7d8
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scikits/learn/datasets/iris/data.py
+ 34
− 14
View file @ 076938f1
#! /usr/bin/env python #! /usr/bin/env python
# -*- coding: utf-8 -*- # -*- coding: utf-8 -*-
# Last Change: Sun Jul 01 08:00 PM 2007 J # Last Change: Tue Jul 17 04:00 PM 2007 J
# The code and descriptive text is copyrighted and offered under the terms of # The code and descriptive text is copyrighted and offered under the terms of
# the BSD License from the authors; see below. However, the actual dataset may # the BSD License from the authors; see below. However, the actual dataset may
...@@ -98,25 +98,45 @@ def load(): ...@@ -98,25 +98,45 @@ def load():
"""load the iris data and returns them. """load the iris data and returns them.
:returns: :returns:
data: recordarray d : dict
a record array of the data. contains the following values:
- '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.
Example
-------
Let's say you are interested in the samples 10, 25, and 50, and want to
know their class name.
>>>> d = load()
>>>> ind = [10, 25, 50]
>>>> lind = d['label'][ind] # returns the label index of each sample
>>>> d['class'][lind] # returns the class name of each sample
""" """
import numpy import numpy
from iris import SL, SW, PL, PW, CLI from iris import SL, SW, PL, PW, LABELS, LI2LN
PW = numpy.array(PW).astype(numpy.float) PW = numpy.array(PW).astype(numpy.float)
PL = numpy.array(PL).astype(numpy.float) PL = numpy.array(PL).astype(numpy.float)
SW = numpy.array(SW).astype(numpy.float) SW = numpy.array(SW).astype(numpy.float)
SL = numpy.array(SL).astype(numpy.float) SL = numpy.array(SL).astype(numpy.float)
data = {} data = {}
for i in CLI.items(): data['data'] = numpy.empty(len(PW),
name = i[0][5:] [('petal width', numpy.int),
data[name] = numpy.empty(len(i[1]), [('petal width', numpy.int),\
('petal length', numpy.int), ('petal length', numpy.int),
('sepal width', numpy.int), ('sepal width', numpy.int),
('sepal length', numpy.int)]) ('sepal length', numpy.int)])
data[name]['petal width'] = numpy.round(PW[i[1]] * 10)
data[name]['petal length'] = numpy.round(PL[i[1]] * 10) data['data']['petal width'] = numpy.round(PW * 10)
data[name]['sepal width'] = numpy.round(SW[i[1]] * 10) data['data']['petal length'] = numpy.round(PL * 10)
data[name]['sepal length'] = numpy.round(SL[i[1]] * 10) data['data']['sepal width'] = numpy.round(SW * 10)
data['data']['sepal length'] = numpy.round(SL * 10)
data['label'] = numpy.array(LABELS).astype(numpy.int)
data['class'] = numpy.empty(len(LI2LN),
'S%d' % numpy.max([len(i) for i in LI2LN.values()]))
for i,c in LI2LN.items():
data['class'][i] = c
return data return data
scikits/learn/datasets/iris/iris.py
+ 65
− 6
View file @ 076938f1
# Autogenerated by convert.py at Sun, 01 Jul 2007 10:36:56 +0000 # Autogenerated by convert.py at Tue, 17 Jul 2007 06:47:17 +0000
SL = ['5.1', '4.9', '4.7', '4.6', '5.0', '5.4', '4.6', '5.0', '4.4', '4.9', '5.4', '4.8', '4.8', '4.3', '5.8', '5.7', '5.4', '5.1', '5.7', '5.1', '5.4', '5.1', '4.6', '5.1', '4.8', '5.0', '5.0', '5.2', '5.2', '4.7', '4.8', '5.4', '5.2', '5.5', '4.9', '5.0', '5.5', '4.9', '4.4', '5.1', '5.0', '4.5', '4.4', '5.0', '5.1', '4.8', '5.1', '4.6', '5.3', '5.0', '7.0', '6.4', '6.9', '5.5', '6.5', '5.7', '6.3', '4.9', '6.6', '5.2', '5.0', '5.9', '6.0', '6.1', '5.6', '6.7', '5.6', '5.8', '6.2', '5.6', '5.9', '6.1', '6.3', '6.1', '6.4', '6.6', '6.8', '6.7', '6.0', '5.7', '5.5', '5.5', '5.8', '6.0', '5.4', '6.0', '6.7', '6.3', '5.6', '5.5', '5.5', '6.1', '5.8', '5.0', '5.6', '5.7', '5.7', '6.2', '5.1', '5.7', '6.3', '5.8', '7.1', '6.3', '6.5', '7.6', '4.9', '7.3', '6.7', '7.2', '6.5', '6.4', '6.8', '5.7', '5.8', '6.4', '6.5', '7.7', '7.7', '6.0', '6.9', '5.6', '7.7', '6.3', '6.7', '7.2', '6.2', '6.1', '6.4', '7.2', '7.4', '7.9', '6.4', '6.3', '6.1', '7.7', '6.3', '6.4', '6.0', '6.9', '6.7', '6.9', '5.8', '6.8', '6.7', '6.7', '6.3', '6.5', '6.2', '5.9'] SL = ['5.1', '4.9', '4.7', '4.6', '5.0', '5.4', '4.6', '5.0', '4.4', '4.9',
'5.4', '4.8', '4.8', '4.3', '5.8', '5.7', '5.4', '5.1', '5.7', '5.1', '5.4',
'5.1', '4.6', '5.1', '4.8', '5.0', '5.0', '5.2', '5.2', '4.7', '4.8', '5.4',
'5.2', '5.5', '4.9', '5.0', '5.5', '4.9', '4.4', '5.1', '5.0', '4.5', '4.4',
'5.0', '5.1', '4.8', '5.1', '4.6', '5.3', '5.0', '7.0', '6.4', '6.9', '5.5',
'6.5', '5.7', '6.3', '4.9', '6.6', '5.2', '5.0', '5.9', '6.0', '6.1', '5.6',
'6.7', '5.6', '5.8', '6.2', '5.6', '5.9', '6.1', '6.3', '6.1', '6.4', '6.6',
'6.8', '6.7', '6.0', '5.7', '5.5', '5.5', '5.8', '6.0', '5.4', '6.0', '6.7',
'6.3', '5.6', '5.5', '5.5', '6.1', '5.8', '5.0', '5.6', '5.7', '5.7', '6.2',
'5.1', '5.7', '6.3', '5.8', '7.1', '6.3', '6.5', '7.6', '4.9', '7.3', '6.7',
'7.2', '6.5', '6.4', '6.8', '5.7', '5.8', '6.4', '6.5', '7.7', '7.7', '6.0',
'6.9', '5.6', '7.7', '6.3', '6.7', '7.2', '6.2', '6.1', '6.4', '7.2', '7.4',
'7.9', '6.4', '6.3', '6.1', '7.7', '6.3', '6.4', '6.0', '6.9', '6.7', '6.9',
'5.8', '6.8', '6.7', '6.7', '6.3', '6.5', '6.2', '5.9']
SW = ['3.5', '3.0', '3.2', '3.1', '3.6', '3.9', '3.4', '3.4', '2.9', '3.1', '3.7', '3.4', '3.0', '3.0', '4.0', '4.4', '3.9', '3.5', '3.8', '3.8', '3.4', '3.7', '3.6', '3.3', '3.4', '3.0', '3.4', '3.5', '3.4', '3.2', '3.1', '3.4', '4.1', '4.2', '3.1', '3.2', '3.5', '3.1', '3.0', '3.4', '3.5', '2.3', '3.2', '3.5', '3.8', '3.0', '3.8', '3.2', '3.7', '3.3', '3.2', '3.2', '3.1', '2.3', '2.8', '2.8', '3.3', '2.4', '2.9', '2.7', '2.0', '3.0', '2.2', '2.9', '2.9', '3.1', '3.0', '2.7', '2.2', '2.5', '3.2', '2.8', '2.5', '2.8', '2.9', '3.0', '2.8', '3.0', '2.9', '2.6', '2.4', '2.4', '2.7', '2.7', '3.0', '3.4', '3.1', '2.3', '3.0', '2.5', '2.6', '3.0', '2.6', '2.3', '2.7', '3.0', '2.9', '2.9', '2.5', '2.8', '3.3', '2.7', '3.0', '2.9', '3.0', '3.0', '2.5', '2.9', '2.5', '3.6', '3.2', '2.7', '3.0', '2.5', '2.8', '3.2', '3.0', '3.8', '2.6', '2.2', '3.2', '2.8', '2.8', '2.7', '3.3', '3.2', '2.8', '3.0', '2.8', '3.0', '2.8', '3.8', '2.8', '2.8', '2.6', '3.0', '3.4', '3.1', '3.0', '3.1', '3.1', '3.1', '2.7', '3.2', '3.3', '3.0', '2.5', '3.0', '3.4', '3.0'] SW = ['3.5', '3.0', '3.2', '3.1', '3.6', '3.9', '3.4', '3.4', '2.9', '3.1',
'3.7', '3.4', '3.0', '3.0', '4.0', '4.4', '3.9', '3.5', '3.8', '3.8', '3.4',
'3.7', '3.6', '3.3', '3.4', '3.0', '3.4', '3.5', '3.4', '3.2', '3.1', '3.4',
'4.1', '4.2', '3.1', '3.2', '3.5', '3.1', '3.0', '3.4', '3.5', '2.3', '3.2',
'3.5', '3.8', '3.0', '3.8', '3.2', '3.7', '3.3', '3.2', '3.2', '3.1', '2.3',
'2.8', '2.8', '3.3', '2.4', '2.9', '2.7', '2.0', '3.0', '2.2', '2.9', '2.9',
'3.1', '3.0', '2.7', '2.2', '2.5', '3.2', '2.8', '2.5', '2.8', '2.9', '3.0',
'2.8', '3.0', '2.9', '2.6', '2.4', '2.4', '2.7', '2.7', '3.0', '3.4', '3.1',
'2.3', '3.0', '2.5', '2.6', '3.0', '2.6', '2.3', '2.7', '3.0', '2.9', '2.9',
'2.5', '2.8', '3.3', '2.7', '3.0', '2.9', '3.0', '3.0', '2.5', '2.9', '2.5',
'3.6', '3.2', '2.7', '3.0', '2.5', '2.8', '3.2', '3.0', '3.8', '2.6', '2.2',
'3.2', '2.8', '2.8', '2.7', '3.3', '3.2', '2.8', '3.0', '2.8', '3.0', '2.8',
'3.8', '2.8', '2.8', '2.6', '3.0', '3.4', '3.1', '3.0', '3.1', '3.1', '3.1',
'2.7', '3.2', '3.3', '3.0', '2.5', '3.0', '3.4', '3.0']
PL = ['1.4', '1.4', '1.3', '1.5', '1.4', '1.7', '1.4', '1.5', '1.4', '1.5', '1.5', '1.6', '1.4', '1.1', '1.2', '1.5', '1.3', '1.4', '1.7', '1.5', '1.7', '1.5', '1.0', '1.7', '1.9', '1.6', '1.6', '1.5', '1.4', '1.6', '1.6', '1.5', '1.5', '1.4', '1.5', '1.2', '1.3', '1.5', '1.3', '1.5', '1.3', '1.3', '1.3', '1.6', '1.9', '1.4', '1.6', '1.4', '1.5', '1.4', '4.7', '4.5', '4.9', '4.0', '4.6', '4.5', '4.7', '3.3', '4.6', '3.9', '3.5', '4.2', '4.0', '4.7', '3.6', '4.4', '4.5', '4.1', '4.5', '3.9', '4.8', '4.0', '4.9', '4.7', '4.3', '4.4', '4.8', '5.0', '4.5', '3.5', '3.8', '3.7', '3.9', '5.1', '4.5', '4.5', '4.7', '4.4', '4.1', '4.0', '4.4', '4.6', '4.0', '3.3', '4.2', '4.2', '4.2', '4.3', '3.0', '4.1', '6.0', '5.1', '5.9', '5.6', '5.8', '6.6', '4.5', '6.3', '5.8', '6.1', '5.1', '5.3', '5.5', '5.0', '5.1', '5.3', '5.5', '6.7', '6.9', '5.0', '5.7', '4.9', '6.7', '4.9', '5.7', '6.0', '4.8', '4.9', '5.6', '5.8', '6.1', '6.4', '5.6', '5.1', '5.6', '6.1', '5.6', '5.5', '4.8', '5.4', '5.6', '5.1', '5.1', '5.9', '5.7', '5.2', '5.0', '5.2', '5.4', '5.1'] PL = ['1.4', '1.4', '1.3', '1.5', '1.4', '1.7', '1.4', '1.5', '1.4', '1.5',
'1.5', '1.6', '1.4', '1.1', '1.2', '1.5', '1.3', '1.4', '1.7', '1.5', '1.7',
'1.5', '1.0', '1.7', '1.9', '1.6', '1.6', '1.5', '1.4', '1.6', '1.6', '1.5',
'1.5', '1.4', '1.5', '1.2', '1.3', '1.5', '1.3', '1.5', '1.3', '1.3', '1.3',
'1.6', '1.9', '1.4', '1.6', '1.4', '1.5', '1.4', '4.7', '4.5', '4.9', '4.0',
'4.6', '4.5', '4.7', '3.3', '4.6', '3.9', '3.5', '4.2', '4.0', '4.7', '3.6',
'4.4', '4.5', '4.1', '4.5', '3.9', '4.8', '4.0', '4.9', '4.7', '4.3', '4.4',
'4.8', '5.0', '4.5', '3.5', '3.8', '3.7', '3.9', '5.1', '4.5', '4.5', '4.7',
'4.4', '4.1', '4.0', '4.4', '4.6', '4.0', '3.3', '4.2', '4.2', '4.2', '4.3',
'3.0', '4.1', '6.0', '5.1', '5.9', '5.6', '5.8', '6.6', '4.5', '6.3', '5.8',
'6.1', '5.1', '5.3', '5.5', '5.0', '5.1', '5.3', '5.5', '6.7', '6.9', '5.0',
'5.7', '4.9', '6.7', '4.9', '5.7', '6.0', '4.8', '4.9', '5.6', '5.8', '6.1',
'6.4', '5.6', '5.1', '5.6', '6.1', '5.6', '5.5', '4.8', '5.4', '5.6', '5.1',
'5.1', '5.9', '5.7', '5.2', '5.0', '5.2', '5.4', '5.1']
PW = ['0.2', '0.2', '0.2', '0.2', '0.2', '0.4', '0.3', '0.2', '0.2', '0.1', '0.2', '0.2', '0.1', '0.1', '0.2', '0.4', '0.4', '0.3', '0.3', '0.3', '0.2', '0.4', '0.2', '0.5', '0.2', '0.2', '0.4', '0.2', '0.2', '0.2', '0.2', '0.4', '0.1', '0.2', '0.1', '0.2', '0.2', '0.1', '0.2', '0.2', '0.3', '0.3', '0.2', '0.6', '0.4', '0.3', '0.2', '0.2', '0.2', '0.2', '1.4', '1.5', '1.5', '1.3', '1.5', '1.3', '1.6', '1.0', '1.3', '1.4', '1.0', '1.5', '1.0', '1.4', '1.3', '1.4', '1.5', '1.0', '1.5', '1.1', '1.8', '1.3', '1.5', '1.2', '1.3', '1.4', '1.4', '1.7', '1.5', '1.0', '1.1', '1.0', '1.2', '1.6', '1.5', '1.6', '1.5', '1.3', '1.3', '1.3', '1.2', '1.4', '1.2', '1.0', '1.3', '1.2', '1.3', '1.3', '1.1', '1.3', '2.5', '1.9', '2.1', '1.8', '2.2', '2.1', '1.7', '1.8', '1.8', '2.5', '2.0', '1.9', '2.1', '2.0', '2.4', '2.3', '1.8', '2.2', '2.3', '1.5', '2.3', '2.0', '2.0', '1.8', '2.1', '1.8', '1.8', '1.8', '2.1', '1.6', '1.9', '2.0', '2.2', '1.5', '1.4', '2.3', '2.4', '1.8', '1.8', '2.1', '2.4', '2.3', '1.9', '2.3', '2.5', '2.3', '1.9', '2.0', '2.3', '1.8'] PW = ['0.2', '0.2', '0.2', '0.2', '0.2', '0.4', '0.3', '0.2', '0.2', '0.1',
'0.2', '0.2', '0.1', '0.1', '0.2', '0.4', '0.4', '0.3', '0.3', '0.3', '0.2',
'0.4', '0.2', '0.5', '0.2', '0.2', '0.4', '0.2', '0.2', '0.2', '0.2', '0.4',
'0.1', '0.2', '0.1', '0.2', '0.2', '0.1', '0.2', '0.2', '0.3', '0.3', '0.2',
'0.6', '0.4', '0.3', '0.2', '0.2', '0.2', '0.2', '1.4', '1.5', '1.5', '1.3',
'1.5', '1.3', '1.6', '1.0', '1.3', '1.4', '1.0', '1.5', '1.0', '1.4', '1.3',
'1.4', '1.5', '1.0', '1.5', '1.1', '1.8', '1.3', '1.5', '1.2', '1.3', '1.4',
'1.4', '1.7', '1.5', '1.0', '1.1', '1.0', '1.2', '1.6', '1.5', '1.6', '1.5',
'1.3', '1.3', '1.3', '1.2', '1.4', '1.2', '1.0', '1.3', '1.2', '1.3', '1.3',
'1.1', '1.3', '2.5', '1.9', '2.1', '1.8', '2.2', '2.1', '1.7', '1.8', '1.8',
'2.5', '2.0', '1.9', '2.1', '2.0', '2.4', '2.3', '1.8', '2.2', '2.3', '1.5',
'2.3', '2.0', '2.0', '1.8', '2.1', '1.8', '1.8', '1.8', '2.1', '1.6', '1.9',
'2.0', '2.2', '1.5', '1.4', '2.3', '2.4', '1.8', '1.8', '2.1', '2.4', '2.3',
'1.9', '2.3', '2.5', '2.3', '1.9', '2.0', '2.3', '1.8']
CLI = {'Iris-virginica': [100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149], 'Iris-setosa': [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49], 'Iris-versicolor': [50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99]} LABELS = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2]
LI2LN = {0: 'Iris-setosa', 1: 'Iris-versicolor', 2: 'Iris-virginica'}
scikits/learn/datasets/iris/src/convert.py
+ 19
− 15
View file @ 076938f1
#! /usr/bin/env python #! /usr/bin/env python
# Last Change: Sun Jul 01 07:00 PM 2007 J # Last Change: Tue Jul 17 03:00 PM 2007 J
# This script generates a python file from the txt data # This script generates a python file from the txt data
import time import time
import csv import csv
from scikits.learn.datasets.misc import dumpvar
# array for equivalence label index <> label name
ln2li = {'Iris-setosa' : 0, 'Iris-versicolor': 1, 'Iris-virginica' :2}
li2ln = {}
for c,i in ln2li.items():
li2ln[i] = c
# Load the data
dataname = 'iris.data' dataname = 'iris.data'
f = open(dataname, 'r') f = open(dataname, 'r')
a = csv.reader(f) a = csv.reader(f)
...@@ -19,22 +28,17 @@ pl = [i[2] for i in el] ...@@ -19,22 +28,17 @@ pl = [i[2] for i in el]
pw = [i[3] for i in el] pw = [i[3] for i in el]
cl = [i[4] for i in el] cl = [i[4] for i in el]
dcl = dict([(i, []) for i in cl]) # dcl[i] = label index of data[i]
for i in range(len(cl)): dcl = [ln2li[i] for i in cl]
dcl[cl[i]].append(i)
# Write the data in oldfaitful.py # Write the data in oldfaitful.py
a = open("iris.py", "w") a = open("../iris.py", "w")
a.write('# Autogenerated by convert.py at %s\n\n' % a.write('# Autogenerated by convert.py at %s\n\n' %
time.strftime("%a, %d %b %Y %H:%M:%S +0000", time.gmtime())) time.strftime("%a, %d %b %Y %H:%M:%S +0000", time.gmtime()))
def dump_var(var, varname): a.writelines(dumpvar(sl, 'SL'))
a.write(varname + " = ") a.writelines(dumpvar(sw, 'SW'))
a.write(str(var)) a.writelines(dumpvar(pl, 'PL'))
a.write("\n\n") a.writelines(dumpvar(pw, 'PW'))
a.writelines(dumpvar(dcl, 'LABELS'))
dump_var(sl, 'SL') a.writelines(dumpvar(li2ln, 'LI2LN'))
dump_var(sw, 'SW')
dump_var(pl, 'PL')
dump_var(pw, 'PW')
dump_var(dcl, 'CLI')
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