K-Means Clustering using PySpark Python

In this tutorial series, we are going to cover K-Means Clustering using Pyspark. K-means is a clustering algorithm that groups data points into K distinct clusters based on their similarity. It is an unsupervised learning technique that is widely used in data mining, machine learning, and pattern recognition. The algorithm works by iteratively assigning data points to a cluster based on their distance from the cluster's centroid and then recomputing the centroid of each cluster. The process continues until the clusters' centroids converge or a maximum number of iterations is reached. K-means is simple, efficient, and effective in finding the optimal clusters for a given dataset, making it a popular choice for various applications.

So, a typical clustering problem looks like this:

• Cluster Similar Documents
• Cluster Customers based on Features
• Identify similar physical groups
• Market Segmentation

We'll be working with a real data set about seeds, from the UCI repository: https://archive.ics.uci.edu/ml/datasets/seeds.

Task: We have seven geometrical parameters of wheat kernels and we have to group them into three different varieties of wheat: Kama, Rosa, and Canadian.

Step 1: Starting the PySpark server 

from pyspark.sql import SparkSession
spark = SparkSession.builder.appName('cluster').getOrCreate()
print('Spark Version: {}'.format(spark.version))

Output:

Spark Version: 3.3.1

Step 2: Load the dataset

#Loading the data
dataset = spark.read.csv("seeds_dataset.csv",header=True,inferSchema=True)

#show the data in the above file using the below command
dataset.show(5)

Output:

+-----+---------+-----------+-----------------+---------------+---------------------+-----------------------+
| Area|Perimeter|Compactness|Length_of_ kernel|Width_of_kernel|Asymmetry_coefficient|Length_of_kernel_groove|
+-----+---------+-----------+-----------------+---------------+---------------------+-----------------------+
|15.26|    14.84|      0.871|            5.763|          3.312|                2.221|                   5.22|
|14.88|    14.57|     0.8811|            5.554|          3.333|                1.018|                  4.956|
|14.29|    14.09|      0.905|            5.291|          3.337|                2.699|                  4.825|
|13.84|    13.94|     0.8955|            5.324|          3.379|                2.259|                  4.805|
|16.14|    14.99|     0.9034|            5.658|          3.562|                1.355|                  5.175|
+-----+---------+-----------+-----------------+---------------+---------------------+-----------------------+
only showing top 5 rows

Print schema

#Print schema
dataset.printSchema()

Output:

root
 |-- Area: double (nullable = true)
 |-- Perimeter: double (nullable = true)
 |-- Compactness: double (nullable = true)
 |-- Length_of_ kernel: double (nullable = true)
 |-- Width_of_kernel: double (nullable = true)
 |-- Asymmetry_coefficient: double (nullable = true)
 |-- Length_of_kernel_groove: double (nullable = true)

Step 3:  Format the data using Vector Assembler into vectors which will be used as "features"

from pyspark.ml.feature import VectorAssembler

vec_assembler = VectorAssembler(inputCols = dataset.columns,
                                outputCol='features')

final_data = vec_assembler.transform(dataset)
final_data.select('features').show(5)

Output:

+--------------------+
|            features|
+--------------------+
|[15.26,14.84,0.87...|
|[14.88,14.57,0.88...|
|[14.29,14.09,0.90...|
|[13.84,13.94,0.89...|
|[16.14,14.99,0.90...|
+--------------------+
only showing top 5 rows

Step 4: Scaling the data

It is a good idea to scale our data to deal with the curse of dimensionality.

from pyspark.ml.feature import StandardScaler

scaler = StandardScaler(inputCol="features", 
                        outputCol="scaledFeatures", 
                        withStd=True, 
                        withMean=False)

# Compute summary statistics by fitting the StandardScaler
scalerModel = scaler.fit(final_data)

# Normalize each feature to have unit standard deviation.
final_data = scalerModel.transform(final_data)

final_data.select('scaledFeatures').show(5)

Output:

+--------------------+
|      scaledFeatures|
+--------------------+
|[5.24452795332028...|
|[5.11393027165175...|
|[4.91116018695588...|
|[4.75650503761158...|
|[5.54696468981581...|
+--------------------+
only showing top 5 rows

Step 5: Find the number of clusters using Silhouette Score

#Importing the model
from pyspark.ml.clustering import KMeans
from pyspark.ml.evaluation import ClusteringEvaluator

silhouette_score=[]

evaluator = ClusteringEvaluator(predictionCol='prediction',
                                featuresCol='scaledFeatures', \
                                metricName='silhouette', 
                                distanceMeasure='squaredEuclidean')

for i in range(2,10):
    kmeans=KMeans(featuresCol='scaledFeatures', k=i)
    model=kmeans.fit(final_data)
    predictions=model.transform(final_data)
    score=evaluator.evaluate(predictions)
    silhouette_score.append(score)
    print('Silhouette Score for k =',i,'is',score)

Output:

Silhouette Score for k = 2 is 0.6650046039315017
Silhouette Score for k = 3 is 0.5928460025426588
Silhouette Score for k = 4 is 0.44804230341047074
Silhouette Score for k = 5 is 0.47760014315974747
Silhouette Score for k = 6 is 0.42900353119793194
Silhouette Score for k = 7 is 0.4419918246535933
Silhouette Score for k = 8 is 0.395868387829853
Silhouette Score for k = 9 is 0.40541652397305605

Plot the Silhouette Score graph

#Visualizing the silhouette scores in a plot
import matplotlib.pyplot as plt

plt.plot(range(2,10),silhouette_score)
plt.xlabel('k')
plt.ylabel('silhouette score')
plt.title('Silhouette Score')
plt.show()

Output:

 Since there is no definitive answer as to what value of K is an acceptable value. I want to move forward with k = 3 Where a local maximum of Silhouette Score is detected.

Step 6: Train the Model

# Trains a k-means model.
kmeans = KMeans(featuresCol='scaledFeatures',k=3)
model = kmeans.fit(final_data)
predictions = model.transform(final_data)

Print cluster centers

# Printing cluster centers
centers = model.clusterCenters()
print("Cluster Centers: ")
for center in centers:
    print(center)

Output:

Cluster Centers: 
[ 4.96198582 10.97871333 37.30930808 12.44647267  8.62880781  1.80062386
 10.41913733]
[ 6.35645488 12.40730852 37.41990178 13.93860446  9.7892399   2.41585309
 12.29286107]
[ 4.07497225 10.14410142 35.89816849 11.80812742  7.54416916  3.15411286
 10.38031464]

Showing the result of groupings:

predictions.select('prediction').show(5)

Output:

+----------+
|prediction|
+----------+
|         0|
|         0|
|         0|
|         0|
|         0|
+----------+
only showing top 5 rows

End Session

#End Session
spark.stop()