In [103]:

    

import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn import datasets
from sklearn.cluster import KMeans
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import StandardScaler,Normalizer, RobustScaler


    

In [3]:

    

iris=datasets.load_iris()


    

In [59]:

    

dir(iris)


    

    
Out[59]:





['DESCR', 'data', 'feature_names', 'target', 'target_names']

In [7]:

    

X=iris.data
y=iris.target


    

In [50]:

    

## Transform Dataframe
df_iris=pd.DataFrame(X,columns=iris.feature_names)
df_iris['target']=pd.Series(y).astype('category')
df_iris.head()


    

    
Out[50]:






  

    

      

      
sepal length (cm)
      
sepal width (cm)
      
petal length (cm)
      
petal width (cm)
      
target
    
  
  

    

      
0
      
5.1
      
3.5
      
1.4
      
0.2
      
0
    
    

      
1
      
4.9
      
3.0
      
1.4
      
0.2
      
0
    
    

      
2
      
4.7
      
3.2
      
1.3
      
0.2
      
0
    
    

      
3
      
4.6
      
3.1
      
1.5
      
0.2
      
0
    
    

      
4
      
5.0
      
3.6
      
1.4
      
0.2
      
0
    
  

In [56]:

    

### Pairplot
g=sns.pairplot(df_iris,hue="target",vars=iris.feature_names)
plt.show()


    

In [61]:

    

## KMeans - fit model
model = KMeans(n_clusters=3)
model.fit(X)


    

    
Out[61]:





KMeans(algorithm='auto', 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 [65]:

    

## Get label
model.labels_


    

    
Out[65]:





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

In [67]:

    

## Get Cluster Center
centroid=model.cluster_centers_
centroid


    

    
Out[67]:





array([[ 5.9016129 ,  2.7483871 ,  4.39354839,  1.43387097],
       [ 5.006     ,  3.418     ,  1.464     ,  0.244     ],
       [ 6.85      ,  3.07368421,  5.74210526,  2.07105263]])

In [63]:

    

# predict the first 
model.predict(X[:3,])


    

    
Out[63]:





array([1, 1, 1])

In [71]:

    

# Plot centroid in 2D plot
plt.scatter(X[:,2],X[:,3],c=y)
plt.scatter(centroid[:,2],centroid[:,3], marker='D',s=50)
plt.show()


    

In [73]:

    

# Inertia - Sum of squared distances of samples to their closest cluster center
model.inertia_


    

    
Out[73]:





78.940841426146022

In [74]:

    

### Select the best k
ks = range(1,6)
inertias=[]
for k in ks:
    model_tmp=KMeans(n_clusters=k).fit(X)
    inertias.append(model_tmp.inertia_)
# Plot ks vs inertias
plt.plot(ks, inertias, '-o')
plt.xlabel('number of clusters, k')
plt.ylabel('inertia')
plt.xticks(ks)
plt.show()


    

In [75]:

    

eval_label= pd.DataFrame({'label': model.labels_, 'target': y})


    

In [79]:

    

pd.crosstab(eval_label['label'],eval_label['target'])


    

    
Out[79]:






  

    

      
target
      
0
      
1
      
2
    
    

      
label
      

      

      

    
  
  

    

      
0
      
0
      
48
      
14
    
    

      
1
      
50
      
0
      
0
    
    

      
2
      
0
      
2
      
36
    
  

In [85]:

    

# In clustering feature with different variance is a problem.
# If a feature has a variance that is orders of magnitude larger that others,
# it might dominate the objective function and make the
# estimator unable to learn from other features correctly as expected.
from numpy import var
pd.DataFrame({'feature': iris.feature_names,
              'variance': var(X,ddof=1,axis=0)})


    

    
Out[85]:






  

    

      

      
feature
      
variance
    
  
  

    

      
0
      
sepal length (cm)
      
0.685694
    
    

      
1
      
sepal width (cm)
      
0.188004
    
    

      
2
      
petal length (cm)
      
3.113179
    
    

      
3
      
petal width (cm)
      
0.582414
    
  

In [101]:

    

#Standardize features by removing the mean and scaling to unit variance
scaler = StandardScaler()
kmeans = KMeans(n_clusters=3)
pipeline = make_pipeline(scaler,kmeans)
pipeline.fit(X)
labels=pipeline.predict(X)


    

In [90]:

    

eval_df = pd.DataFrame({'labels': labels, 'target': iris.target})


    

In [92]:

    

pd.crosstab(eval_df['labels'],eval_df['target'])


    

    
Out[92]:






  

    

      
target
      
0
      
1
      
2
    
    

      
labels
      

      

      

    
  
  

    

      
0
      
0
      
12
      
36
    
    

      
1
      
50
      
0
      
0
    
    

      
2
      
0
      
38
      
14
    
  

In [102]:

    

kmeans.inertia_


    

    
Out[102]:





140.96581663074699

In [108]:

    

#Normalize samples individually to unit norm.
#Each sample (i.e. each row of the data matrix) with at least one non zero
#component is rescaled independently of other samples so that its norm (l1 or l2) equals one.
normalizer = Normalizer()
kmeans = KMeans(n_clusters=3,init='k-means++')
pipeline = make_pipeline(normalizer,kmeans)
pipeline.fit(X)
labels=pipeline.predict(X)
eval_df = pd.DataFrame({'labels': labels, 'target': iris.target})
pd.crosstab(eval_df['labels'],eval_df['target'])


    

    
Out[108]:






  

    

      
target
      
0
      
1
      
2
    
    

      
labels
      

      

      

    
  
  

    

      
0
      
0
      
5
      
50
    
    

      
1
      
50
      
0
      
0
    
    

      
2
      
0
      
45
      
0
    
  

In [109]:

    

kmeans.inertia_


    

    
Out[109]:





0.32320764544347275