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import pandas as pd
from pylab import rcParams
import seaborn as sb
import matplotlib.pyplot as plt

from sklearn.cluster import DBSCAN
from collections import Counter


    

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import warnings
warnings.filterwarnings("ignore", category=DeprecationWarning)


    

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%matplotlib inline
rcParams['figure.figsize']=7,4
sb.set_style('whitegrid')


    

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from sklearn import datasets
# import some data to play with

iris= datasets.load_iris()

X = iris.data[:, [1,2]]  # only take two features
y = iris.target

#columns=['Sepal Length','Sepal Width','Petal Length','Petal Width']
columns=['Sepal Width','Petal Length']
df = pd.DataFrame(data=X,columns=columns)
df.head()


    

    
Out[29]:







  

    

      

      
Sepal Width
      
Petal Length
    
  
  

    

      
0
      
3.5
      
1.4
    
    

      
1
      
3.0
      
1.4
    
    

      
2
      
3.2
      
1.3
    
    

      
3
      
3.1
      
1.5
    
    

      
4
      
3.6
      
1.4
    
  

In [71]:

    

model = DBSCAN(eps=0.5, min_samples=19).fit(X)
model


    

    
Out[71]:





DBSCAN(algorithm='auto', eps=0.5, leaf_size=30, metric='euclidean',
    min_samples=19, n_jobs=1, p=None)

In [72]:

    

outliers_df = pd.DataFrame(df)

print(Counter(model.labels_))
outliers_df[model.labels_==-1].head(4)


    

    




Counter({1: 91, 0: 48, -1: 11})






    
Out[72]:







  

    

      

      
Sepal Width
      
Petal Length
    
  
  

    

      
15
      
4.4
      
1.5
    
    

      
41
      
2.3
      
1.3
    
    

      
57
      
2.4
      
3.3
    
    

      
60
      
2.0
      
3.5
    
  

In [75]:

    

fig = plt.figure()
ax = fig.add_axes([.1,.1,1,1])

colors = model.labels_
#colors = ['r','b','orange']

def labels_to_colors(x):
    if x == -1:
        return 'red'
    elif x == 0 :
        return 'blue'
    elif x == 1:
        return 'black'
    else:
        return 'grey'

colors = list(colors)
colors = list(map(lambda x: labels_to_colors(x), colors))


x = X[:,1]
y = X[:,0]
ax.scatter(x,y,c=colors,s=120,marker='o')

ax.set_xlabel(columns[1])
ax.set_ylabel(columns[0])


    

    
Out[75]:





<matplotlib.text.Text at 0x11e836b38>






    

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