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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