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

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)
x.columns = ['Sepal_Length', 'Sepal_Width', 'Petal_Length', 'Petal_Width']
y = pd.DataFrame(iris.target)
y.columns = ['Targets']
model = KMeans(n_clusters=3)
model.fit(x)
plt.figure(figsize=(14, 7))
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')

plt.subplot(1,2,2)
plt.scatter(x.Petal_Length, x.Petal_Width, c=colormap[model.labels_], s=40)
plt.title('KMeans')
plt.show()

km = rename(model.labels_)
print('Accuracy: ', sm.accuracy_score(y, km))
print('Confusion Matrix\n', sm.confusion_matrix(y, km))