Scikit-Learn 패키지의 샘플 데이터 - classification용

In [2]:

    

from sklearn.datasets import load_iris
iris = load_iris()
print(iris.DESCR)


    

    




Iris Plants Database

Notes
-----
Data Set Characteristics:
    :Number of Instances: 150 (50 in each of three classes)
    :Number of Attributes: 4 numeric, predictive attributes and the class
    :Attribute Information:
        - sepal length in cm
        - sepal width in cm
        - petal length in cm
        - petal width in cm
        - class:
                - Iris-Setosa
                - Iris-Versicolour
                - Iris-Virginica
    :Summary Statistics:

    ============== ==== ==== ======= ===== ====================
                    Min  Max   Mean    SD   Class Correlation
    ============== ==== ==== ======= ===== ====================
    sepal length:   4.3  7.9   5.84   0.83    0.7826
    sepal width:    2.0  4.4   3.05   0.43   -0.4194
    petal length:   1.0  6.9   3.76   1.76    0.9490  (high!)
    petal width:    0.1  2.5   1.20  0.76     0.9565  (high!)
    ============== ==== ==== ======= ===== ====================

    :Missing Attribute Values: None
    :Class Distribution: 33.3% for each of 3 classes.
    :Creator: R.A. Fisher
    :Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov)
    :Date: July, 1988

This is a copy of UCI ML iris datasets.
http://archive.ics.uci.edu/ml/datasets/Iris

The famous Iris database, first used by Sir R.A Fisher

This is perhaps the best known database to be found in the
pattern recognition literature.  Fisher's paper is a classic in the field and
is referenced frequently to this day.  (See Duda & Hart, for example.)  The
data set contains 3 classes of 50 instances each, where each class refers to a
type of iris plant.  One class is linearly separable from the other 2; the
latter are NOT linearly separable from each other.

References
----------
   - Fisher,R.A. "The use of multiple measurements in taxonomic problems"
     Annual Eugenics, 7, Part II, 179-188 (1936); also in "Contributions to
     Mathematical Statistics" (John Wiley, NY, 1950).
   - Duda,R.O., & Hart,P.E. (1973) Pattern Classification and Scene Analysis.
     (Q327.D83) John Wiley & Sons.  ISBN 0-471-22361-1.  See page 218.
   - Dasarathy, B.V. (1980) "Nosing Around the Neighborhood: A New System
     Structure and Classification Rule for Recognition in Partially Exposed
     Environments".  IEEE Transactions on Pattern Analysis and Machine
     Intelligence, Vol. PAMI-2, No. 1, 67-71.
   - Gates, G.W. (1972) "The Reduced Nearest Neighbor Rule".  IEEE Transactions
     on Information Theory, May 1972, 431-433.
   - See also: 1988 MLC Proceedings, 54-64.  Cheeseman et al"s AUTOCLASS II
     conceptual clustering system finds 3 classes in the data.
   - Many, many more ...

In [3]:

    

df = pd.DataFrame(iris.data, columns=iris.feature_names)
sy = pd.Series(iris.target, dtype="category")
sy = sy.cat.rename_categories(iris.target_names)
df['species'] = sy
df


    

    
Out[3]:






  

    

      

      
sepal length (cm)
      
sepal width (cm)
      
petal length (cm)
      
petal width (cm)
      
species
    
  
  

    

      
0
      
5.1
      
3.5
      
1.4
      
0.2
      
setosa
    
    

      
1
      
4.9
      
3.0
      
1.4
      
0.2
      
setosa
    
    

      
2
      
4.7
      
3.2
      
1.3
      
0.2
      
setosa
    
    

      
3
      
4.6
      
3.1
      
1.5
      
0.2
      
setosa
    
    

      
4
      
5.0
      
3.6
      
1.4
      
0.2
      
setosa
    
    

      
5
      
5.4
      
3.9
      
1.7
      
0.4
      
setosa
    
    

      
6
      
4.6
      
3.4
      
1.4
      
0.3
      
setosa
    
    

      
7
      
5.0
      
3.4
      
1.5
      
0.2
      
setosa
    
    

      
8
      
4.4
      
2.9
      
1.4
      
0.2
      
setosa
    
    

      
9
      
4.9
      
3.1
      
1.5
      
0.1
      
setosa
    
    

      
10
      
5.4
      
3.7
      
1.5
      
0.2
      
setosa
    
    

      
11
      
4.8
      
3.4
      
1.6
      
0.2
      
setosa
    
    

      
12
      
4.8
      
3.0
      
1.4
      
0.1
      
setosa
    
    

      
13
      
4.3
      
3.0
      
1.1
      
0.1
      
setosa
    
    

      
14
      
5.8
      
4.0
      
1.2
      
0.2
      
setosa
    
    

      
15
      
5.7
      
4.4
      
1.5
      
0.4
      
setosa
    
    

      
16
      
5.4
      
3.9
      
1.3
      
0.4
      
setosa
    
    

      
17
      
5.1
      
3.5
      
1.4
      
0.3
      
setosa
    
    

      
18
      
5.7
      
3.8
      
1.7
      
0.3
      
setosa
    
    

      
19
      
5.1
      
3.8
      
1.5
      
0.3
      
setosa
    
    

      
20
      
5.4
      
3.4
      
1.7
      
0.2
      
setosa
    
    

      
21
      
5.1
      
3.7
      
1.5
      
0.4
      
setosa
    
    

      
22
      
4.6
      
3.6
      
1.0
      
0.2
      
setosa
    
    

      
23
      
5.1
      
3.3
      
1.7
      
0.5
      
setosa
    
    

      
24
      
4.8
      
3.4
      
1.9
      
0.2
      
setosa
    
    

      
25
      
5.0
      
3.0
      
1.6
      
0.2
      
setosa
    
    

      
26
      
5.0
      
3.4
      
1.6
      
0.4
      
setosa
    
    

      
27
      
5.2
      
3.5
      
1.5
      
0.2
      
setosa
    
    

      
28
      
5.2
      
3.4
      
1.4
      
0.2
      
setosa
    
    

      
29
      
4.7
      
3.2
      
1.6
      
0.2
      
setosa
    
    

      
...
      
...
      
...
      
...
      
...
      
...
    
    

      
120
      
6.9
      
3.2
      
5.7
      
2.3
      
virginica
    
    

      
121
      
5.6
      
2.8
      
4.9
      
2.0
      
virginica
    
    

      
122
      
7.7
      
2.8
      
6.7
      
2.0
      
virginica
    
    

      
123
      
6.3
      
2.7
      
4.9
      
1.8
      
virginica
    
    

      
124
      
6.7
      
3.3
      
5.7
      
2.1
      
virginica
    
    

      
125
      
7.2
      
3.2
      
6.0
      
1.8
      
virginica
    
    

      
126
      
6.2
      
2.8
      
4.8
      
1.8
      
virginica
    
    

      
127
      
6.1
      
3.0
      
4.9
      
1.8
      
virginica
    
    

      
128
      
6.4
      
2.8
      
5.6
      
2.1
      
virginica
    
    

      
129
      
7.2
      
3.0
      
5.8
      
1.6
      
virginica
    
    

      
130
      
7.4
      
2.8
      
6.1
      
1.9
      
virginica
    
    

      
131
      
7.9
      
3.8
      
6.4
      
2.0
      
virginica
    
    

      
132
      
6.4
      
2.8
      
5.6
      
2.2
      
virginica
    
    

      
133
      
6.3
      
2.8
      
5.1
      
1.5
      
virginica
    
    

      
134
      
6.1
      
2.6
      
5.6
      
1.4
      
virginica
    
    

      
135
      
7.7
      
3.0
      
6.1
      
2.3
      
virginica
    
    

      
136
      
6.3
      
3.4
      
5.6
      
2.4
      
virginica
    
    

      
137
      
6.4
      
3.1
      
5.5
      
1.8
      
virginica
    
    

      
138
      
6.0
      
3.0
      
4.8
      
1.8
      
virginica
    
    

      
139
      
6.9
      
3.1
      
5.4
      
2.1
      
virginica
    
    

      
140
      
6.7
      
3.1
      
5.6
      
2.4
      
virginica
    
    

      
141
      
6.9
      
3.1
      
5.1
      
2.3
      
virginica
    
    

      
142
      
5.8
      
2.7
      
5.1
      
1.9
      
virginica
    
    

      
143
      
6.8
      
3.2
      
5.9
      
2.3
      
virginica
    
    

      
144
      
6.7
      
3.3
      
5.7
      
2.5
      
virginica
    
    

      
145
      
6.7
      
3.0
      
5.2
      
2.3
      
virginica
    
    

      
146
      
6.3
      
2.5
      
5.0
      
1.9
      
virginica
    
    

      
147
      
6.5
      
3.0
      
5.2
      
2.0
      
virginica
    
    

      
148
      
6.2
      
3.4
      
5.4
      
2.3
      
virginica
    
    

      
149
      
5.9
      
3.0
      
5.1
      
1.8
      
virginica
    
  

150 rows × 5 columns

In [4]:

    

sns.pairplot(df, hue='species')
plt.show()


    

In [5]:

    

from sklearn.datasets import fetch_20newsgroups
newsgroups = fetch_20newsgroups(subset="all")
print(newsgroups.description)
print(newsgroups.keys())


    

    




the 20 newsgroups by date dataset
dict_keys(['description', 'data', 'target', 'target_names', 'filenames', 'DESCR'])

In [6]:

    

from pprint import pprint
pprint(list(newsgroups.target_names))


    

    




['alt.atheism',
 'comp.graphics',
 'comp.os.ms-windows.misc',
 'comp.sys.ibm.pc.hardware',
 'comp.sys.mac.hardware',
 'comp.windows.x',
 'misc.forsale',
 'rec.autos',
 'rec.motorcycles',
 'rec.sport.baseball',
 'rec.sport.hockey',
 'sci.crypt',
 'sci.electronics',
 'sci.med',
 'sci.space',
 'soc.religion.christian',
 'talk.politics.guns',
 'talk.politics.mideast',
 'talk.politics.misc',
 'talk.religion.misc']

In [7]:

    

print(newsgroups.data[1])
print("="*80)
print(newsgroups.target_names[newsgroups.target[1]])


    

    




From: mblawson@midway.ecn.uoknor.edu (Matthew B Lawson)
Subject: Which high-performance VLB video card?
Summary: Seek recommendations for VLB video card
Nntp-Posting-Host: midway.ecn.uoknor.edu
Organization: Engineering Computer Network, University of Oklahoma, Norman, OK, USA
Keywords: orchid, stealth, vlb
Lines: 21

  My brother is in the market for a high-performance video card that supports
VESA local bus with 1-2MB RAM.  Does anyone have suggestions/ideas on:

  - Diamond Stealth Pro Local Bus

  - Orchid Farenheit 1280

  - ATI Graphics Ultra Pro

  - Any other high-performance VLB card


Please post or email.  Thank you!

  - Matt

-- 
    |  Matthew B. Lawson <------------> (mblawson@essex.ecn.uoknor.edu)  |   
  --+-- "Now I, Nebuchadnezzar, praise and exalt and glorify the King  --+-- 
    |   of heaven, because everything he does is right and all his ways  |   
    |   are just." - Nebuchadnezzar, king of Babylon, 562 B.C.           |   

================================================================================
comp.sys.ibm.pc.hardware

In [8]:

    

from sklearn.datasets import fetch_olivetti_faces
olivetti = fetch_olivetti_faces()
print(olivetti.DESCR)
print(olivetti.keys())


    

    




Modified Olivetti faces dataset.

The original database was available from (now defunct)

    http://www.uk.research.att.com/facedatabase.html

The version retrieved here comes in MATLAB format from the personal
web page of Sam Roweis:

    http://www.cs.nyu.edu/~roweis/

There are ten different images of each of 40 distinct subjects. For some
subjects, the images were taken at different times, varying the lighting,
facial expressions (open / closed eyes, smiling / not smiling) and facial
details (glasses / no glasses). All the images were taken against a dark
homogeneous background with the subjects in an upright, frontal position (with
tolerance for some side movement).

The original dataset consisted of 92 x 112, while the Roweis version
consists of 64x64 images.

dict_keys(['target', 'images', 'DESCR', 'data'])

In [10]:

    

N=2; M=5;
fig = plt.figure(figsize=(8,5))
plt.subplots_adjust(top=1, bottom=0, hspace=0, wspace=0.05)
klist = np.random.choice(range(len(olivetti.data)), N * M)
for i in range(N):
    for j in range(M):
        k = klist[i*M+j]
        ax = fig.add_subplot(N, M, i*M+j+1)
        ax.imshow(olivetti.images[k], cmap=plt.cm.bone);
        ax.grid(False)
        ax.xaxis.set_ticks([])
        ax.yaxis.set_ticks([])
        plt.title(olivetti.target[k])
plt.tight_layout()
plt.show()


    

In [11]:

    

from sklearn.datasets import fetch_lfw_people
lfw_people = fetch_lfw_people(min_faces_per_person=70, resize=0.4)
print(lfw_people.DESCR)
print(lfw_people.keys())


    

    




LFW faces dataset
dict_keys(['target', 'target_names', 'images', 'DESCR', 'data'])

In [12]:

    

N=2; M=5;
fig = plt.figure(figsize=(8,5))
plt.subplots_adjust(top=1, bottom=0, hspace=0.1, wspace=0.05)
klist = np.random.choice(range(len(lfw_people.data)), N * M)
for i in range(N):
    for j in range(M):
        k = klist[i*M+j]
        ax = fig.add_subplot(N, M, i*M+j+1)
        ax.imshow(lfw_people.images[k], cmap=plt.cm.bone);
        ax.grid(False)
        ax.xaxis.set_ticks([])
        ax.yaxis.set_ticks([])
        plt.title(lfw_people.target_names[lfw_people.target[k]])
plt.tight_layout()
plt.show()


    

In [13]:

    

from sklearn.datasets import fetch_lfw_pairs
lfw_pairs = fetch_lfw_pairs(resize=0.4)
print(lfw_pairs.DESCR)
print(lfw_pairs.keys())


    

    




'train' segment of the LFW pairs dataset
dict_keys(['target', 'target_names', 'data', 'pairs', 'DESCR'])

In [14]:

    

N=2; M=5;
fig = plt.figure(figsize=(8,5))
plt.subplots_adjust(top=1, bottom=0, hspace=0.01, wspace=0.05)
klist = np.random.choice(range(len(lfw_pairs.data)), M)
for j in range(M):
    k = klist[j]
    ax1 = fig.add_subplot(N, M, j+1)
    ax1.imshow(lfw_pairs.pairs [k][0], cmap=plt.cm.bone);
    ax1.grid(False)
    ax1.xaxis.set_ticks([])
    ax1.yaxis.set_ticks([])
    plt.title(lfw_pairs.target_names[lfw_pairs.target[k]])
    ax2 = fig.add_subplot(N, M, j+1 + M)
    ax2.imshow(lfw_pairs.pairs [k][1], cmap=plt.cm.bone);
    ax2.grid(False)
    ax2.xaxis.set_ticks([])
    ax2.yaxis.set_ticks([])
plt.tight_layout()
plt.show()


    

In [15]:

    

from sklearn.datasets import load_digits
digits = load_digits()
print(digits.DESCR)
print(digits.keys())


    

    




Optical Recognition of Handwritten Digits Data Set
===================================================

Notes
-----
Data Set Characteristics:
    :Number of Instances: 5620
    :Number of Attributes: 64
    :Attribute Information: 8x8 image of integer pixels in the range 0..16.
    :Missing Attribute Values: None
    :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
http://archive.ics.uci.edu/ml/datasets/Optical+Recognition+of+Handwritten+Digits

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.

dict_keys(['target', 'target_names', 'images', 'DESCR', 'data'])

In [16]:

    

N=2; M=5;
fig = plt.figure(figsize=(10,5))
plt.subplots_adjust(top=1, bottom=0, hspace=0, wspace=0.05)
for i in range(N):
    for j in range(M):
        k = i*M+j
        ax = fig.add_subplot(N, M, k+1)
        ax.imshow(digits.images[k], cmap=plt.cm.bone, interpolation="none");
        ax.grid(False)
        ax.xaxis.set_ticks([])
        ax.yaxis.set_ticks([])
        plt.title(digits.target_names[k])
plt.tight_layout()
plt.show()


    

In [18]:

    

from sklearn.datasets.mldata import fetch_mldata
mnist = fetch_mldata('MNIST original')
mnist.keys()


    

    
Out[18]:





dict_keys(['target', 'data', 'DESCR', 'COL_NAMES'])

In [19]:

    

N=2; M=5;
fig = plt.figure(figsize=(8,5))
plt.subplots_adjust(top=1, bottom=0, hspace=0, wspace=0.05)
klist = np.random.choice(range(len(mnist.data)), N * M)
for i in range(N):
    for j in range(M):
        k = klist[i*M+j]
        ax = fig.add_subplot(N, M, i*M+j+1)
        ax.imshow(mnist.data[k].reshape(28, 28), cmap=plt.cm.bone, interpolation="nearest");
        ax.grid(False)
        ax.xaxis.set_ticks([])
        ax.yaxis.set_ticks([])
        plt.title(mnist.target[k])
plt.tight_layout()
plt.show()