from https://github.com/decisionstats/pythonfordatascience/blob/master/2%2BClustering%2B-K%2BMeans.ipynb


import matplotlib.pyplot as plt
from sklearn import datasets
from sklearn.cluster import KMeans
import sklearn.metrics as sm
 
import pandas as pd
import numpy as np

In [2]:

wine=pd.read_csv("http://archive.ics.uci.edu/ml/machine-learning-databases/wine/wine.data",header=None)

In [3]:

wine.head()

Out[3]:

	0	1	2	3	4	5	6	7	8	9	10	11	12	13
0	1	14.23	1.71	2.43	15.6	127	2.80	3.06	0.28	2.29	5.64	1.04	3.92	1065
1	1	13.20	1.78	2.14	11.2	100	2.65	2.76	0.26	1.28	4.38	1.05	3.40	1050
2	1	13.16	2.36	2.67	18.6	101	2.80	3.24	0.30	2.81	5.68	1.03	3.17	1185
3	1	14.37	1.95	2.50	16.8	113	3.85	3.49	0.24	2.18	7.80	0.86	3.45	1480
4	1	13.24	2.59	2.87	21.0	118	2.80	2.69	0.39	1.82	4.32	1.04	2.93	735

In [4]:

wine.columns=['winetype','Alcohol','Malic acid','Ash','Alcalinity of ash','Magnesium','Total phenols','Flavanoids','Nonflavanoid phenols','Proanthocyanins','Color intensity','Hue','OD280/OD315 of diluted wines','Proline']

In [5]:

wine.head()

Out[5]:

	winetype	Alcohol	Malic acid	Ash	Alcalinity of ash	Magnesium	Total phenols	Flavanoids	Nonflavanoid phenols	Proanthocyanins	Color intensity	Hue	OD280/OD315 of diluted wines	Proline
0	1	14.23	1.71	2.43	15.6	127	2.80	3.06	0.28	2.29	5.64	1.04	3.92	1065
1	1	13.20	1.78	2.14	11.2	100	2.65	2.76	0.26	1.28	4.38	1.05	3.40	1050
2	1	13.16	2.36	2.67	18.6	101	2.80	3.24	0.30	2.81	5.68	1.03	3.17	1185
3	1	14.37	1.95	2.50	16.8	113	3.85	3.49	0.24	2.18	7.80	0.86	3.45	1480
4	1	13.24	2.59	2.87	21.0	118	2.80	2.69	0.39	1.82	4.32	1.04	2.93	735

In [6]:

wine.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 178 entries, 0 to 177
Data columns (total 14 columns):
winetype                        178 non-null int64
Alcohol                         178 non-null float64
Malic acid                      178 non-null float64
Ash                             178 non-null float64
Alcalinity of ash               178 non-null float64
Magnesium                       178 non-null int64
Total phenols                   178 non-null float64
Flavanoids                      178 non-null float64
Nonflavanoid phenols            178 non-null float64
Proanthocyanins                 178 non-null float64
Color intensity                 178 non-null float64
Hue                             178 non-null float64
OD280/OD315 of diluted wines    178 non-null float64
Proline                         178 non-null int64
dtypes: float64(11), int64(3)
memory usage: 19.5 KB

In [7]:

wine.describe()

Out[7]:

	winetype	Alcohol	Malic acid	Ash	Alcalinity of ash	Magnesium	Total phenols	Flavanoids	Nonflavanoid phenols	Proanthocyanins	Color intensity	Hue	OD280/OD315 of diluted wines	Proline
count	178.000000	178.000000	178.000000	178.000000	178.000000	178.000000	178.000000	178.000000	178.000000	178.000000	178.000000	178.000000	178.000000	178.000000
mean	1.938202	13.000618	2.336348	2.366517	19.494944	99.741573	2.295112	2.029270	0.361854	1.590899	5.058090	0.957449	2.611685	746.893258
std	0.775035	0.811827	1.117146	0.274344	3.339564	14.282484	0.625851	0.998859	0.124453	0.572359	2.318286	0.228572	0.709990	314.907474
min	1.000000	11.030000	0.740000	1.360000	10.600000	70.000000	0.980000	0.340000	0.130000	0.410000	1.280000	0.480000	1.270000	278.000000
25%	1.000000	12.362500	1.602500	2.210000	17.200000	88.000000	1.742500	1.205000	0.270000	1.250000	3.220000	0.782500	1.937500	500.500000
50%	2.000000	13.050000	1.865000	2.360000	19.500000	98.000000	2.355000	2.135000	0.340000	1.555000	4.690000	0.965000	2.780000	673.500000
75%	3.000000	13.677500	3.082500	2.557500	21.500000	107.000000	2.800000	2.875000	0.437500	1.950000	6.200000	1.120000	3.170000	985.000000
max	3.000000	14.830000	5.800000	3.230000	30.000000	162.000000	3.880000	5.080000	0.660000	3.580000	13.000000	1.710000	4.000000	1680.000000

In [8]:

pd.value_counts(wine['winetype'])

Out[8]:

2    71
1    59
3    48
Name: winetype, dtype: int64

In [9]:

x=wine.ix[:,1:14]
y=wine.ix[:,:1]

In [10]:

x.columns

Out[10]:

Index(['Alcohol', 'Malic acid', 'Ash', 'Alcalinity of ash', 'Magnesium',
       'Total phenols', 'Flavanoids', 'Nonflavanoid phenols',
       'Proanthocyanins', 'Color intensity', 'Hue',
       'OD280/OD315 of diluted wines', 'Proline'],
      dtype='object')

In [11]:

y.columns

Out[11]:

Index(['winetype'], dtype='object')

In [12]:

x.head()

Out[12]:

	Alcohol	Malic acid	Ash	Alcalinity of ash	Magnesium	Total phenols	Flavanoids	Nonflavanoid phenols	Proanthocyanins	Color intensity	Hue	OD280/OD315 of diluted wines	Proline
0	14.23	1.71	2.43	15.6	127	2.80	3.06	0.28	2.29	5.64	1.04	3.92	1065
1	13.20	1.78	2.14	11.2	100	2.65	2.76	0.26	1.28	4.38	1.05	3.40	1050
2	13.16	2.36	2.67	18.6	101	2.80	3.24	0.30	2.81	5.68	1.03	3.17	1185
3	14.37	1.95	2.50	16.8	113	3.85	3.49	0.24	2.18	7.80	0.86	3.45	1480
4	13.24	2.59	2.87	21.0	118	2.80	2.69	0.39	1.82	4.32	1.04	2.93	735

In [13]:

y.head()

Out[13]:

	winetype
0	1
1	1
2	1
3	1
4	1

In [14]:

# K Means Cluster
model = KMeans(n_clusters=3)
model.fit(x)

Out[14]:

KMeans(copy_x=True, init='k-means++', max_iter=300, n_clusters=3, n_init=10,
    n_jobs=1, precompute_distances='auto', random_state=None, tol=0.0001,
    verbose=0)

In [15]:

model.labels_

Out[15]:

array([1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 1,
       1, 2, 2, 1, 1, 2, 1, 1, 1, 1, 1, 1, 2, 2, 1, 1, 2, 2, 1, 1, 2, 2, 1,
       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 2, 0, 2, 0, 0, 2, 0, 0, 2,
       2, 2, 0, 0, 1, 2, 0, 0, 0, 2, 0, 0, 2, 2, 0, 0, 0, 0, 0, 2, 2, 0, 0,
       0, 0, 0, 2, 2, 0, 2, 0, 2, 0, 0, 0, 2, 0, 0, 0, 0, 2, 0, 0, 2, 0, 0,
       0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 2, 2, 2, 2, 0,
       0, 0, 2, 2, 0, 0, 2, 2, 0, 2, 2, 0, 0, 0, 0, 2, 2, 2, 0, 2, 2, 2, 0,
       2, 0, 2, 2, 0, 2, 2, 2, 2, 0, 0, 2, 2, 2, 2, 2, 0])

In [16]:

pd.value_counts(model.labels_)

Out[16]:

0    69
2    62
1    47
dtype: int64

In [17]:

pd.value_counts(y['winetype'])

Out[17]:

2    71
1    59
3    48
Name: winetype, dtype: int64

In [18]:

# We convert all the 1s to 0s and 0s to 1s.
predY = np.choose(model.labels_, [2, 1, 3]).astype(np.int64)

In [19]:

pd.value_counts(y['winetype'])

Out[19]:

2    71
1    59
3    48
Name: winetype, dtype: int64

In [20]:

pd.value_counts(model.labels_)

Out[20]:

0    69
2    62
1    47
dtype: int64

In [21]:

pd.value_counts(predY)

Out[21]:

2    69
3    62
1    47
dtype: int64

In [22]:

# Performance Metrics
sm.accuracy_score(y, predY)

Out[22]:

0.702247191011236

In [23]:

# Confusion Matrix
sm.confusion_matrix(y, predY)

Out[23]:

array([[46,  0, 13],
       [ 1, 50, 20],
       [ 0, 19, 29]])

In [24]:

from ggplot import *
%matplotlib inline

In [25]:

p = ggplot(aes(x='Alcohol', y='Ash',color="winetype"), data=wine)
p + geom_point()

Out[25]:

<ggplot: (12696398)>

In [26]:

p2 = ggplot(aes(x='Alcohol', y='Ash',color="predY"), data=wine)
p2 + geom_point()

Out[26]:

<ggplot: (-9223372036842026194)>

Author: Ajay Ohri

http://about.me/ajayohri View all posts by Ajay Ohri

K Means Clustering in Python

Author: Ajay Ohri

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