In [1]:

    

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt


    

In [2]:

    

%matplotlib inline


    

In [3]:

    

plt.rcParams["figure.figsize"] = [15,7]


    

In [4]:

    

from sklearn.datasets import load_iris

iris = load_iris()
type(iris)


    

    
Out[4]:





sklearn.datasets.base.Bunch

In [5]:

    

# Convert data type to pandas dataframe

# http://stackoverflow.com/questions/38105539/how-to-convert-a-scikit-learn-dataset-to-a-pandas-dataset
data = pd.DataFrame(data= np.c_[iris['data'], iris['target']],
                    columns= iris['feature_names'] + ['target'])
data.head()


    

    
Out[5]:






  

    

      

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

    

      
0
      
5.1
      
3.5
      
1.4
      
0.2
      
0.0
    
    

      
1
      
4.9
      
3.0
      
1.4
      
0.2
      
0.0
    
    

      
2
      
4.7
      
3.2
      
1.3
      
0.2
      
0.0
    
    

      
3
      
4.6
      
3.1
      
1.5
      
0.2
      
0.0
    
    

      
4
      
5.0
      
3.6
      
1.4
      
0.2
      
0.0
    
  

In [6]:

    

# Rename columns
data.columns = ["sepal_length", "sepal_width", "pedal_length", "pedal_width", "target"]
data.head()


    

    
Out[6]:






  

    

      

      
sepal_length
      
sepal_width
      
pedal_length
      
pedal_width
      
target
    
  
  

    

      
0
      
5.1
      
3.5
      
1.4
      
0.2
      
0.0
    
    

      
1
      
4.9
      
3.0
      
1.4
      
0.2
      
0.0
    
    

      
2
      
4.7
      
3.2
      
1.3
      
0.2
      
0.0
    
    

      
3
      
4.6
      
3.1
      
1.5
      
0.2
      
0.0
    
    

      
4
      
5.0
      
3.6
      
1.4
      
0.2
      
0.0
    
  

In [7]:

    

points = data.iloc[:,[0,1]]
points.head()


    

    
Out[7]:






  

    

      

      
sepal_length
      
sepal_width
    
  
  

    

      
0
      
5.1
      
3.5
    
    

      
1
      
4.9
      
3.0
    
    

      
2
      
4.7
      
3.2
    
    

      
3
      
4.6
      
3.1
    
    

      
4
      
5.0
      
3.6
    
  

In [8]:

    

iris.target_names


    

    
Out[8]:





array(['setosa', 'versicolor', 'virginica'], 
      dtype='<U10')

In [9]:

    

import matplotlib

my_colors = ["red", "green", "blue"]

p = plt.scatter(x=data["sepal_length"],
                y=data["sepal_width"],
                c=data["target"],
                cmap=matplotlib.colors.ListedColormap(my_colors))
plt.xlabel("sepal_length")
plt.ylabel("sepal_width")

## Fix my legend
plt.legend((p,p,p), (iris.target_names))
ax = plt.gca()
legend = ax.get_legend()
legend.legendHandles[0].set_color(my_colors[0])
legend.legendHandles[1].set_color(my_colors[1])
legend.legendHandles[2].set_color(my_colors[2])


    

In [10]:

    

from scipy.spatial import Voronoi, voronoi_plot_2d

vor = Voronoi(points)

## Draw boundaries
voronoi_plot_2d(vor, show_vertices=False, show_points=False)

## Add color to points
plt.scatter(x=data["sepal_length"],
            y=data["sepal_width"],
            c=data["target"],
            cmap=matplotlib.colors.ListedColormap(my_colors))

plt.xlabel(points.columns[0])
plt.ylabel(points.columns[1])
plt.title("Iris dataset")

## Fix my legend
plt.legend((p,p,p), (iris.target_names))
ax = plt.gca()
legend = ax.get_legend()
legend.legendHandles[0].set_color(my_colors[0])
legend.legendHandles[1].set_color(my_colors[1])
legend.legendHandles[2].set_color(my_colors[2])
plt.show()