K Means Clustering in Python

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()
Screenshot from 2017-07-07 13-08-44
Out[25]:
<ggplot: (12696398)>
In [26]:
p2 = ggplot(aes(x='Alcohol', y='Ash',color="predY"), data=wine)
p2 + geom_point()
 Screenshot from 2017-07-07 13-08-44
Out[26]:
<ggplot: (-9223372036842026194)>

Author: Ajay Ohri

http://about.me/ajayohri

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