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[SPARK-14516][FOLLOWUP] Adding ClusteringEvaluator to examples #19676

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[SPARK-14516][FOLLOWUP] Adding ClusteringEvaluator to examples #19676

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[SPARK-14516][FOLLOWUP] Adding ClusteringEvaluator to examples
mgaido91 Nov 6, 2017
feb619d
remove useless parameters
mgaido91 Dec 9, 2017
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12 changes: 9 additions & 3 deletions examples/src/main/java/org/apache/spark/examples/ml/JavaKMeansExample.java
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Expand Up @@ -20,6 +20,7 @@
// $example on$
import org.apache.spark.ml.clustering.KMeansModel;
import org.apache.spark.ml.clustering.KMeans;
import org.apache.spark.ml.evaluation.ClusteringEvaluator;
import org.apache.spark.ml.linalg.Vector;
import org.apache.spark.sql.Dataset;
import org.apache.spark.sql.Row;
Expand Down Expand Up @@ -51,9 +52,14 @@ public static void main(String[] args) {
KMeans kmeans = new KMeans().setK(2).setSeed(1L);
KMeansModel model = kmeans.fit(dataset);

// Evaluate clustering by computing Within Set Sum of Squared Errors.
double WSSSE = model.computeCost(dataset);
System.out.println("Within Set Sum of Squared Errors = " + WSSSE);
// Make predictions
Dataset<Row> predictions = model.transform(dataset);

// Evaluate clustering by computing Silhouette score
ClusteringEvaluator evaluator = new ClusteringEvaluator();

double silhouette = evaluator.evaluate(predictions);
System.out.println("Silhouette with squared euclidean distance = " + silhouette);
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@srowen srowen Dec 9, 2017

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euclidean -> Euclidean, but not important to change unless you're touching the code again anyway

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@mgaido91 mgaido91 Dec 9, 2017

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Thanks, I don't think I am changing the code again, but I can fix this grammatical error if you want.


// Shows the result.
Vector[] centers = model.clusterCenters();
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12 changes: 9 additions & 3 deletions examples/src/main/python/ml/kmeans_example.py
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Expand Up @@ -19,6 +19,7 @@

# $example on$
from pyspark.ml.clustering import KMeans
from pyspark.ml.evaluation import ClusteringEvaluator
# $example off$

from pyspark.sql import SparkSession
Expand All @@ -45,9 +46,14 @@
kmeans = KMeans().setK(2).setSeed(1)
model = kmeans.fit(dataset)

# Evaluate clustering by computing Within Set Sum of Squared Errors.
wssse = model.computeCost(dataset)
print("Within Set Sum of Squared Errors = " + str(wssse))
# Make predictions
predictions = model.transform(dataset)

# Evaluate clustering by computing Silhouette score
evaluator = ClusteringEvaluator()

silhouette = evaluator.evaluate(predictions)
print("Silhouette with squared euclidean distance = " + str(silhouette))

# Shows the result.
centers = model.clusterCenters()
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12 changes: 9 additions & 3 deletions examples/src/main/scala/org/apache/spark/examples/ml/KMeansExample.scala
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Expand Up @@ -21,6 +21,7 @@ package org.apache.spark.examples.ml

// $example on$
import org.apache.spark.ml.clustering.KMeans
import org.apache.spark.ml.evaluation.ClusteringEvaluator
// $example off$
import org.apache.spark.sql.SparkSession

Expand All @@ -47,9 +48,14 @@ object KMeansExample {
val kmeans = new KMeans().setK(2).setSeed(1L)
val model = kmeans.fit(dataset)

// Evaluate clustering by computing Within Set Sum of Squared Errors.
val WSSSE = model.computeCost(dataset)
println(s"Within Set Sum of Squared Errors = $WSSSE")
// Make predictions
val predictions = model.transform(dataset)

// Evaluate clustering by computing Silhouette score
val evaluator = new ClusteringEvaluator()

val silhouette = evaluator.evaluate(predictions)
println(s"Silhouette with squared euclidean distance = $silhouette")

// Shows the result.
println("Cluster Centers: ")
Expand Down