From 6af7b43b34942c662122e3905b0724b2dd40a63f Mon Sep 17 00:00:00 2001
From: Nick Pentreath <nickp@za.ibm.com>
Date: Tue, 16 May 2017 10:54:42 +0200
Subject: [PATCH] [SPARK-20553][ML][PYSPARK] Update ALS examples with
recommend-all methods
Update ALS examples illustrating use of "recommendForAllX" methods.
## How was this patch tested?
Built and ran examples locally
Author: Nick Pentreath <nickp@za.ibm.com>
Closes #17950 from MLnick/SPARK-20553-update-als-examples.
---
.../java/org/apache/spark/examples/ml/JavaALSExample.java | 8 ++++++++
examples/src/main/python/ml/als_example.py | 8 ++++++++
.../scala/org/apache/spark/examples/ml/ALSExample.scala | 7 +++++++
3 files changed, 23 insertions(+)
diff --git a/examples/src/main/java/org/apache/spark/examples/ml/JavaALSExample.java b/examples/src/main/java/org/apache/spark/examples/ml/JavaALSExample.java
index 81970b7c81..60ef03d89d 100644
--- a/examples/src/main/java/org/apache/spark/examples/ml/JavaALSExample.java
+++ b/examples/src/main/java/org/apache/spark/examples/ml/JavaALSExample.java
@@ -113,7 +113,15 @@ public class JavaALSExample {
.setPredictionCol("prediction");
Double rmse = evaluator.evaluate(predictions);
System.out.println("Root-mean-square error = " + rmse);
+
+ // Generate top 10 movie recommendations for each user
+ Dataset<Row> userRecs = model.recommendForAllUsers(10);
+ // Generate top 10 user recommendations for each movie
+ Dataset<Row> movieRecs = model.recommendForAllItems(10);
// $example off$
+ userRecs.show();
+ movieRecs.show();
+
spark.stop();
}
}
diff --git a/examples/src/main/python/ml/als_example.py b/examples/src/main/python/ml/als_example.py
index 2e7214ed56..1672d552eb 100644
--- a/examples/src/main/python/ml/als_example.py
+++ b/examples/src/main/python/ml/als_example.py
@@ -55,5 +55,13 @@ if __name__ == "__main__":
predictionCol="prediction")
rmse = evaluator.evaluate(predictions)
print("Root-mean-square error = " + str(rmse))
+
+ # Generate top 10 movie recommendations for each user
+ userRecs = model.recommendForAllUsers(10)
+ # Generate top 10 user recommendations for each movie
+ movieRecs = model.recommendForAllItems(10)
# $example off$
+ userRecs.show()
+ movieRecs.show()
+
spark.stop()
diff --git a/examples/src/main/scala/org/apache/spark/examples/ml/ALSExample.scala b/examples/src/main/scala/org/apache/spark/examples/ml/ALSExample.scala
index 868f49b16f..07b15dfa17 100644
--- a/examples/src/main/scala/org/apache/spark/examples/ml/ALSExample.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/ml/ALSExample.scala
@@ -75,7 +75,14 @@ object ALSExample {
.setPredictionCol("prediction")
val rmse = evaluator.evaluate(predictions)
println(s"Root-mean-square error = $rmse")
+
+ // Generate top 10 movie recommendations for each user
+ val userRecs = model.recommendForAllUsers(10)
+ // Generate top 10 user recommendations for each movie
+ val movieRecs = model.recommendForAllItems(10)
// $example off$
+ userRecs.show()
+ movieRecs.show()
spark.stop()
}
--
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