Github user sethah commented on a diff in the pull request:
https://github.com/apache/spark/pull/16715#discussion_r100426237
--- Diff:
examples/src/main/java/org/apache/spark/examples/ml/JavaBucketedRandomProjectionLSHExample.java
---
@@ -71,25 +71,32 @@ public static void main(String[] args) {
BucketedRandomProjectionLSH mh = new BucketedRandomProjectionLSH()
.setBucketLength(2.0)
.setNumHashTables(3)
- .setInputCol("keys")
- .setOutputCol("values");
+ .setInputCol("features")
+ .setOutputCol("hashes");
BucketedRandomProjectionLSHModel model = mh.fit(dfA);
// Feature Transformation
+ System.out.println("The hashed dataset where hashed values are stored
in the column 'values':");
model.transform(dfA).show();
// Cache the transformed columns
Dataset<Row> transformedA = model.transform(dfA).cache();
Dataset<Row> transformedB = model.transform(dfB).cache();
// Approximate similarity join
+ System.out.println("Approximately joining dfA and dfB on distance
smaller than 1.5:");
model.approxSimilarityJoin(dfA, dfB, 1.5).show();
+ System.out.println("Joining cached datasets to avoid recomputing the
hash values:");
model.approxSimilarityJoin(transformedA, transformedB, 1.5).show();
+
// Self Join
+ System.out.println("Approximately self join of dfB on distance smaller
than 2.5:");
--- End diff --
I don't think the self join is necessary, but I'll defer to others. Also,
"Approximate self join" and it's dfA not dfB
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