Github user MLnick commented on a diff in the pull request:
https://github.com/apache/spark/pull/16715#discussion_r100929903
--- Diff:
examples/src/main/scala/org/apache/spark/examples/ml/BucketedRandomProjectionLSHExample.scala
---
@@ -38,40 +39,45 @@ object BucketedRandomProjectionLSHExample {
(1, Vectors.dense(1.0, -1.0)),
(2, Vectors.dense(-1.0, -1.0)),
(3, Vectors.dense(-1.0, 1.0))
- )).toDF("id", "keys")
+ )).toDF("id", "features")
val dfB = spark.createDataFrame(Seq(
(4, Vectors.dense(1.0, 0.0)),
(5, Vectors.dense(-1.0, 0.0)),
(6, Vectors.dense(0.0, 1.0)),
(7, Vectors.dense(0.0, -1.0))
- )).toDF("id", "keys")
+ )).toDF("id", "features")
val key = Vectors.dense(1.0, 0.0)
val brp = new BucketedRandomProjectionLSH()
.setBucketLength(2.0)
.setNumHashTables(3)
- .setInputCol("keys")
- .setOutputCol("values")
+ .setInputCol("features")
+ .setOutputCol("hashes")
val model = brp.fit(dfA)
// Feature Transformation
+ println("The hashed dataset where hashed values are stored in the
column 'hashes':")
model.transform(dfA).show()
- // Cache the transformed columns
- val transformedA = model.transform(dfA).cache()
- val transformedB = model.transform(dfB).cache()
- // Approximate similarity join
- model.approxSimilarityJoin(dfA, dfB, 1.5).show()
- model.approxSimilarityJoin(transformedA, transformedB, 1.5).show()
- // Self Join
- model.approxSimilarityJoin(dfA, dfA, 2.5).filter("datasetA.id <
datasetB.id").show()
+ // Compute the locality sensitive hashes for the input rows, then
perform approximate
+ // similarity join.
+ // We could avoid computing hashes by passing in the
already-transformed dataset, e.g.
+ // `model.approxSimilarityJoin(transformedA, transformedB, 1.5)`
+ println("Approximately joining dfA and dfB on Euclidean distance
smaller than 1.5:")
+ model.approxSimilarityJoin(dfA, dfB, 1.5)
+ .select(col("datasetA.id").alias("idA"),
+ col("datasetB.id").alias("idB"),
+ col("distCol").alias("EuclideanDistance")).show()
--- End diff --
We can just pass `distCol = EuclideanDistance ` here, and for
`approxNearestNeighbors`.
We can do this throughout the examples (and obviously for min hash change
it to jaccard accordingly).
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