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K Fold

The document outlines a Python script that utilizes KMeans clustering on the Iris dataset, focusing on petal length and width. It demonstrates data preprocessing, clustering, and visualization of the results, including the identification of cluster centroids. Additionally, it addresses a known memory leak issue with KMeans on Windows systems.

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Kavya Padarthi
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48 views2 pages

K Fold

The document outlines a Python script that utilizes KMeans clustering on the Iris dataset, focusing on petal length and width. It demonstrates data preprocessing, clustering, and visualization of the results, including the identification of cluster centroids. Additionally, it addresses a known memory leak issue with KMeans on Windows systems.

Uploaded by

Kavya Padarthi
Copyright
© © All Rights Reserved
We take content rights seriously. If you suspect this is your content, claim it here.
Available Formats
Download as PDF, TXT or read online on Scribd
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In [14]: from sklearn.

cluster import KMeans


import pandas as pd
from sklearn.preprocessing import MinMaxScaler
from matplotlib import pyplot as plt
%matplotlib inline

In [16]: from sklearn.datasets import load_iris


iris =load_iris()

In [18]: dir(iris)

Out[18]: ['DESCR',
'data',
'data_module',
'feature_names',
'filename',
'frame',
'target',
'target_names']

In [20]: iris_df = pd.DataFrame(data=iris.data, columns=iris.feature_names)

In [22]: iris_df

Out[22]: sepal length (cm) sepal width (cm) petal length (cm) petal width (cm)

0 5.1 3.5 1.4 0.2

1 4.9 3.0 1.4 0.2

2 4.7 3.2 1.3 0.2

3 4.6 3.1 1.5 0.2

4 5.0 3.6 1.4 0.2

... ... ... ... ...

145 6.7 3.0 5.2 2.3

146 6.3 2.5 5.0 1.9

147 6.5 3.0 5.2 2.0

148 6.2 3.4 5.4 2.3

149 5.9 3.0 5.1 1.8

150 rows × 4 columns

In [34]: iris_df1 = iris_df.drop(['sepal length (cm)', 'sepal width (cm)'], axis='columns')


iris_df1

Out[34]: petal length (cm) petal width (cm)

0 1.4 0.2

1 1.4 0.2

2 1.3 0.2

3 1.5 0.2

4 1.4 0.2

... ... ...

145 5.2 2.3

146 5.0 1.9

147 5.2 2.0

148 5.4 2.3

149 5.1 1.8

150 rows × 2 columns

In [36]: plt.scatter(iris_df1['petal length (cm)'], iris_df1['petal width (cm)'])


plt.xlabel("length")
plt.ylabel("width")

Out[36]: Text(0, 0.5, 'width')

In [44]: km = KMeans(n_clusters=3)
y_predict = km.fit_predict(iris_df1[['petal length (cm)','petal width (cm)']])
y_predict

C:\Users\MyPc\anaconda3\Lib\site-packages\sklearn\cluster\_kmeans.py:1429: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You ca
n avoid it by setting the environment variable OMP_NUM_THREADS=1.
warnings.warn(
Out[44]: array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 2, 2, 2, 2, 2, 0, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0])

In [46]: iris_df1['cluster'] = y_predict


iris_df1

Out[46]: petal length (cm) petal width (cm) cluster

0 1.4 0.2 1

1 1.4 0.2 1

2 1.3 0.2 1

3 1.5 0.2 1

4 1.4 0.2 1

... ... ... ...

145 5.2 2.3 0

146 5.0 1.9 0

147 5.2 2.0 0

148 5.4 2.3 0

149 5.1 1.8 0

150 rows × 3 columns

In [50]: df1 = iris_df1[iris_df1.cluster==0]


df2 = iris_df1[iris_df1.cluster==1]
df3 = iris_df1[iris_df1.cluster==2]
plt.scatter(df1['petal length (cm)'],df1['petal width (cm)'],color='red')
plt.scatter(df2['petal length (cm)'],df2['petal width (cm)'],color='blue')
plt.scatter(df3['petal length (cm)'],df3['petal width (cm)'],color='black')
plt.xlabel("Petal length")
plt.ylabel("Petal width")
plt.show()

In [52]: scaler = MinMaxScaler()


scaler.fit(iris_df1[['petal length (cm)']])
iris_df1['petal length (cm)'] = scaler.transform(iris_df1[['petal length (cm)']])

scaler.fit(iris_df1[['petal width (cm)']])


iris_df1['petal width (cm)'] = scaler.transform(iris_df1[['petal width (cm)']])

In [54]: iris_df1

Out[54]: petal length (cm) petal width (cm) cluster

0 0.067797 0.041667 1

1 0.067797 0.041667 1

2 0.050847 0.041667 1

3 0.084746 0.041667 1

4 0.067797 0.041667 1

... ... ... ...

145 0.711864 0.916667 0

146 0.677966 0.750000 0

147 0.711864 0.791667 0

148 0.745763 0.916667 0

149 0.694915 0.708333 0

150 rows × 3 columns

In [56]: km = KMeans(n_clusters=3)
y_predict = km.fit_predict(iris_df1[['petal length (cm)','petal width (cm)']])
y_predict

C:\Users\MyPc\anaconda3\Lib\site-packages\sklearn\cluster\_kmeans.py:1429: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You ca
n avoid it by setting the environment variable OMP_NUM_THREADS=1.
warnings.warn(
Out[56]: array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 2, 0, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2])

In [58]: iris_df1['cluster'] = y_predict


iris_df1

Out[58]: petal length (cm) petal width (cm) cluster

0 0.067797 0.041667 1

1 0.067797 0.041667 1

2 0.050847 0.041667 1

3 0.084746 0.041667 1

4 0.067797 0.041667 1

... ... ... ...

145 0.711864 0.916667 2

146 0.677966 0.750000 2

147 0.711864 0.791667 2

148 0.745763 0.916667 2

149 0.694915 0.708333 2

150 rows × 3 columns

In [62]: km.cluster_centers_

Out[62]: array([[0.55867014, 0.51041667],


[0.07830508, 0.06083333],
[0.7740113 , 0.81510417]])

In [68]: df1 = iris_df1[iris_df1.cluster==0]


df2 = iris_df1[iris_df1.cluster==1]
df3 = iris_df1[iris_df1.cluster==2]
plt.scatter(df1['petal length (cm)'],df1['petal width (cm)'],color='red')
plt.scatter(df2['petal length (cm)'],df2['petal width (cm)'],color='blue')
plt.scatter(df3['petal length (cm)'],df3['petal width (cm)'],color='black')
plt.scatter(km.cluster_centers_[:,0],km.cluster_centers_[:,1],marker = '*',color='purple',label='centroid')
plt.xlabel("Petal length")
plt.ylabel("Petal width")
plt.legend()
plt.show()

In [100… sse=[]
k_recg = range(1,30)
for k in k_recg:
km = KMeans(n_clusters=k)
km.fit(iris_df1[['petal length (cm)','petal width (cm)']])
sse.append(km.inertia_)

C:\Users\MyPc\anaconda3\Lib\site-packages\sklearn\cluster\_kmeans.py:1429: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You ca
n avoid it by setting the environment variable OMP_NUM_THREADS=1.
warnings.warn(
C:\Users\MyPc\anaconda3\Lib\site-packages\sklearn\cluster\_kmeans.py:1429: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You ca
n avoid it by setting the environment variable OMP_NUM_THREADS=1.
warnings.warn(
C:\Users\MyPc\anaconda3\Lib\site-packages\sklearn\cluster\_kmeans.py:1429: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You ca
n avoid it by setting the environment variable OMP_NUM_THREADS=1.
warnings.warn(
C:\Users\MyPc\anaconda3\Lib\site-packages\sklearn\cluster\_kmeans.py:1429: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You ca
n avoid it by setting the environment variable OMP_NUM_THREADS=1.
warnings.warn(
C:\Users\MyPc\anaconda3\Lib\site-packages\sklearn\cluster\_kmeans.py:1429: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You ca
n avoid it by setting the environment variable OMP_NUM_THREADS=1.
warnings.warn(
C:\Users\MyPc\anaconda3\Lib\site-packages\sklearn\cluster\_kmeans.py:1429: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You ca
n avoid it by setting the environment variable OMP_NUM_THREADS=1.
warnings.warn(
C:\Users\MyPc\anaconda3\Lib\site-packages\sklearn\cluster\_kmeans.py:1429: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You ca
n avoid it by setting the environment variable OMP_NUM_THREADS=1.
warnings.warn(
C:\Users\MyPc\anaconda3\Lib\site-packages\sklearn\cluster\_kmeans.py:1429: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You ca
n avoid it by setting the environment variable OMP_NUM_THREADS=1.
warnings.warn(
C:\Users\MyPc\anaconda3\Lib\site-packages\sklearn\cluster\_kmeans.py:1429: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You ca
n avoid it by setting the environment variable OMP_NUM_THREADS=1.
warnings.warn(
C:\Users\MyPc\anaconda3\Lib\site-packages\sklearn\cluster\_kmeans.py:1429: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You ca
n avoid it by setting the environment variable OMP_NUM_THREADS=1.
warnings.warn(
C:\Users\MyPc\anaconda3\Lib\site-packages\sklearn\cluster\_kmeans.py:1429: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You ca
n avoid it by setting the environment variable OMP_NUM_THREADS=1.
warnings.warn(
C:\Users\MyPc\anaconda3\Lib\site-packages\sklearn\cluster\_kmeans.py:1429: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You ca
n avoid it by setting the environment variable OMP_NUM_THREADS=1.
warnings.warn(
C:\Users\MyPc\anaconda3\Lib\site-packages\sklearn\cluster\_kmeans.py:1429: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You ca
n avoid it by setting the environment variable OMP_NUM_THREADS=1.
warnings.warn(
C:\Users\MyPc\anaconda3\Lib\site-packages\sklearn\cluster\_kmeans.py:1429: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You ca
n avoid it by setting the environment variable OMP_NUM_THREADS=1.
warnings.warn(
C:\Users\MyPc\anaconda3\Lib\site-packages\sklearn\cluster\_kmeans.py:1429: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You ca
n avoid it by setting the environment variable OMP_NUM_THREADS=1.
warnings.warn(
C:\Users\MyPc\anaconda3\Lib\site-packages\sklearn\cluster\_kmeans.py:1429: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You ca
n avoid it by setting the environment variable OMP_NUM_THREADS=1.
warnings.warn(
C:\Users\MyPc\anaconda3\Lib\site-packages\sklearn\cluster\_kmeans.py:1429: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You ca
n avoid it by setting the environment variable OMP_NUM_THREADS=1.
warnings.warn(
C:\Users\MyPc\anaconda3\Lib\site-packages\sklearn\cluster\_kmeans.py:1429: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You ca
n avoid it by setting the environment variable OMP_NUM_THREADS=1.
warnings.warn(
C:\Users\MyPc\anaconda3\Lib\site-packages\sklearn\cluster\_kmeans.py:1429: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You ca
n avoid it by setting the environment variable OMP_NUM_THREADS=1.
warnings.warn(
C:\Users\MyPc\anaconda3\Lib\site-packages\sklearn\cluster\_kmeans.py:1429: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You ca
n avoid it by setting the environment variable OMP_NUM_THREADS=1.
warnings.warn(
C:\Users\MyPc\anaconda3\Lib\site-packages\sklearn\cluster\_kmeans.py:1429: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You ca
n avoid it by setting the environment variable OMP_NUM_THREADS=1.
warnings.warn(
C:\Users\MyPc\anaconda3\Lib\site-packages\sklearn\cluster\_kmeans.py:1429: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You ca
n avoid it by setting the environment variable OMP_NUM_THREADS=1.
warnings.warn(
C:\Users\MyPc\anaconda3\Lib\site-packages\sklearn\cluster\_kmeans.py:1429: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You ca
n avoid it by setting the environment variable OMP_NUM_THREADS=1.
warnings.warn(
C:\Users\MyPc\anaconda3\Lib\site-packages\sklearn\cluster\_kmeans.py:1429: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You ca
n avoid it by setting the environment variable OMP_NUM_THREADS=1.
warnings.warn(
C:\Users\MyPc\anaconda3\Lib\site-packages\sklearn\cluster\_kmeans.py:1429: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You ca
n avoid it by setting the environment variable OMP_NUM_THREADS=1.
warnings.warn(
C:\Users\MyPc\anaconda3\Lib\site-packages\sklearn\cluster\_kmeans.py:1429: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You ca
n avoid it by setting the environment variable OMP_NUM_THREADS=1.
warnings.warn(
C:\Users\MyPc\anaconda3\Lib\site-packages\sklearn\cluster\_kmeans.py:1429: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You ca
n avoid it by setting the environment variable OMP_NUM_THREADS=1.
warnings.warn(
C:\Users\MyPc\anaconda3\Lib\site-packages\sklearn\cluster\_kmeans.py:1429: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You ca
n avoid it by setting the environment variable OMP_NUM_THREADS=1.
warnings.warn(
C:\Users\MyPc\anaconda3\Lib\site-packages\sklearn\cluster\_kmeans.py:1429: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You ca
n avoid it by setting the environment variable OMP_NUM_THREADS=1.
warnings.warn(

In [102… plt.xlabel('K')
plt.ylabel('Sum Of Squared error')
plt.plot(k_recg,sse)

Out[102… [<matplotlib.lines.Line2D at 0x24091677050>]


In [ ]:

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