EM.py

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
from sklearn import preprocessing
from sklearn.mixture import GaussianMixture

l1 = [0,1,2]

def rename(s):
 l2 = []
 for i in s:
     if i not in l2:
         l2.append(i)
 for i in range(len(s)):
     pos = l2.index(s[i])
 s[i] = l1[pos]
 return s


iris = datasets.load_iris()
X=pd.DataFrame(iris.data,columns=['Sepal_Length','Sepal_Width','Petal_Length','Petal_Width'] )
y = pd.DataFrame(iris.target,columns = ['Targets'])


def graph_plot(l,title,s,target):
 plt.subplot(l[0],l[1],l[2])
 if s==1:
     plt.scatter(X.Sepal_Length,X.Sepal_Width, c=colormap[target], s=40)
     plt.title(title)
 else:
     plt.scatter(X.Petal_Length,X.Petal_Width, c=colormap[target], s=40)
     plt.title(title)


plt.figure()
colormap = np.array(['red', 'lime', 'black'])
graph_plot([1, 2, 1],'sepal',1,y.Targets)
graph_plot([1, 2, 2],'petal',0,y.Targets)
plt.show()


def fit_model(modelName): 
 model = modelName(3)
 model.fit(X)
 plt.figure()
 colormap = np.array(['red', 'lime', 'black'])
 graph_plot([1, 2, 1],'Real Classification',0,y.Targets)
 if modelName == KMeans:
     m = 'Kmeans'
 else:
     m = 'Em'
 y1 = model.predict(X)
 graph_plot([1, 2, 2],m,0,y1)
 plt.show()
 km = rename(y1)
 print("\nPredicted: \n", km)
 print("Accuracy ",sm.accuracy_score(y, km))
 print("Confusion Matrix ",sm.confusion_matrix(y, km))


fit_model(KMeans)
fit_model(GaussianMixture)