Skip to content

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

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Closed
mgaido91 wants to merge 2 commits into from
+27 −9
Closed

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

Show file tree
Hide file tree
Changes from 1 commit
Commits
Show all changes
2 commits
Select commit Hold shift + click to select a range
4c4f83e
[SPARK-14516][FOLLOWUP] Adding ClusteringEvaluator to examples
mgaido91 Nov 6, 2017
feb619d
remove useless parameters
mgaido91 Dec 9, 2017
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
15 changes: 12 additions & 3 deletions examples/src/main/java/org/apache/spark/examples/ml/JavaKMeansExample.java
View file
Open in desktop
Original file line number Diff line number Diff line change
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 +52,17 @@ 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()
.setFeaturesCol("features")
.setPredictionCol("prediction")

@yanboliang yanboliang Dec 9, 2017

Copy link
Copy Markdown
Contributor

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

We use default values here, so it's not necessary to set them explicitly. We should keep examples as simple as possible. Thanks.

.setMetricName("silhouette");

double silhouette = evaluator.evaluate(predictions);
System.out.println("Silhouette with squared euclidean distance = " + silhouette);

@srowen srowen Dec 9, 2017

Copy link
Copy Markdown
Member

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

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

@mgaido91 mgaido91 Dec 9, 2017

Copy link
Copy Markdown
Contributor Author

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

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();
Expand Down
13 changes: 10 additions & 3 deletions examples/src/main/python/ml/kmeans_example.py
View file
Open in desktop
Original file line number Diff line number Diff line change
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 +46,15 @@
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(
featuresCol="features", predictionCol="prediction", metricName="silhouette")

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

# Shows the result.
centers = model.clusterCenters()
Expand Down
15 changes: 12 additions & 3 deletions examples/src/main/scala/org/apache/spark/examples/ml/KMeansExample.scala
View file
Open in desktop
Original file line number Diff line number Diff line change
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 +48,17 @@ 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()
.setFeaturesCol("features")
.setPredictionCol("prediction")
.setMetricName("silhouette")

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

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