KMeans.py

"""Apply EM algorithm to cluster a set of data stored in a .CSV file. Use the same data set for clustering using
k-Means algorithm. Compare the results of these two algorithms and comment on the quality of clustering. You can add
Java/Python ML library classes/API in the program """

import matplotlib.pyplot as plt
from sklearn import datasets
from sklearn.cluster import KMeans
from sklearn.mixture import GaussianMixture 
import sklearn.metrics as sm
import pandas as pd
import numpy as np

iris = datasets.load_iris()

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

print(X)
print(Y)
colormap = np.array(['red', 'lime', 'black'])

plt.subplot(1,2,1)
plt.scatter(X.Petal_Length, X.Petal_Width, c=colormap[Y.Targets], s=40)
plt.title('Real Clustering')

model1 = KMeans(n_clusters=3)
model1.fit(X)

plt.subplot(1,2,2)
plt.scatter(X.Petal_Length, X.Petal_Width, c=colormap[model1.labels_], s=40)
plt.title('K Mean Clustering')
plt.show()

model2 = GaussianMixture(n_components=3) 
model2.fit(X)

plt.subplot(1,2,1)
plt.scatter(X.Petal_Length, X.Petal_Width, c=colormap[model2.predict(X)], s=40)
plt.title('EM Clustering')
plt.show()

print("Actual Target is:\n", iris.target)
print("K Means:\n",model1.labels_)
print("EM:\n",model2.predict(X))
print("Accuracy of KMeans is ",sm.accuracy_score(Y,model1.labels_))
print("Accuracy of EM is ",sm.accuracy_score(Y, model2.predict(X)))