from sklearn.cluster import KMeans
from sklearn.mixture import GaussianMixture
import sklearn.metrics as metrics
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
 
names = ['Sepal_Length','Sepal_Width','Petal_Length','Petal_Width', 'Class']
 
dataset = pd.read_csv("8-dataset.csv", names=names)
 
X = dataset.iloc[:, :-1] 
# until last column
 
label = {'Iris-setosa': 0,'Iris-versicolor': 1, 'Iris-virginica': 2} 
 
y = [label[c] for c in dataset.iloc[:, -1]]
 
# Set the size of the plot
 
plt.figure(figsize=(14,7))
 
# Create a colormap
 
colormap=np.array(['red','lime','black'])
 
# REAL PLOT
# Plot the Original Classifications
 
plt.subplot(1,3,1) 
# The subplot() function takes three arguments that describes the layout of the figure.
#The layout is organized in rows and columns, which are represented by the first and second argument.
# The third argument represents the index of the current plot.
 
plt.title('Real')
plt.scatter(X.Petal_Length,X.Petal_Width,c=colormap[y])
 
# Based on petal data compare with target data
 
# K-PLOT
model=KMeans(n_clusters=3, random_state=0).fit(X)
plt.subplot(1,3,2)
plt.title('KMeans')
plt.scatter(X.Petal_Length,X.Petal_Width,c=colormap[model.labels_])
#  c=colormap[model.labels_] for the predicted classess.
 
 
 
 
print('The accuracy score of K-Mean: ',metrics.accuracy_score(y, model.labels_))
print('The Confusion matrixof K-Mean:\n',metrics.confusion_matrix(y, model.labels_))
 
# GMM PLOT 
# Expectation-Maximization (EM) algorithm is based on Gaussian mixture model 
gmm=GaussianMixture(n_components=3, random_state=0).fit(X)
y_cluster_gmm=gmm.predict(X)
plt.subplot(1,3,3)
plt.title('GMM Classification')
plt.scatter(X.Petal_Length,X.Petal_Width,c=colormap[y_cluster_gmm])
 
print('The accuracy score of EM: ',metrics.accuracy_score(y, y_cluster_gmm))
print('The Confusion matrix of EM:\n ',metrics.confusion_matrix(y, y_cluster_gmm))
 

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