Github user Yunni commented on a diff in the pull request:
https://github.com/apache/spark/pull/15795#discussion_r90736883
--- Diff:
examples/src/main/scala/org/apache/spark/examples/ml/ApproxSimilarityJoinExample.scala
---
@@ -0,0 +1,67 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements. See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+// scalastyle:off println
+package org.apache.spark.examples.ml
+
+// $example on$
+import org.apache.spark.ml.feature.MinHashLSH
+import org.apache.spark.ml.linalg.Vectors
+// $example off$
+import org.apache.spark.sql.SparkSession
+
+object ApproxSimilarityJoinExample {
+ def main(args: Array[String]): Unit = {
+ // Creates a SparkSession
+ val spark = SparkSession
+ .builder
+ .appName("ApproxSimilarityJoinExample")
+ .getOrCreate()
+
+ // $example on$
+ val dfA = spark.createDataFrame(Seq(
+ (0, Vectors.sparse(6, Seq((0, 1.0), (1, 1.0), (2, 1.0)))),
+ (1, Vectors.sparse(6, Seq((2, 1.0), (3, 1.0), (4, 1.0)))),
+ (2, Vectors.sparse(6, Seq((0, 1.0), (2, 1.0), (4, 1.0))))
+ )).toDF("id", "keys")
+
+ val dfB = spark.createDataFrame(Seq(
+ (3, Vectors.sparse(6, Seq((1, 1.0), (3, 1.0), (5, 1.0)))),
+ (4, Vectors.sparse(6, Seq((2, 1.0), (3, 1.0), (5, 1.0)))),
+ (5, Vectors.sparse(6, Seq((1, 1.0), (2, 1.0), (4, 1.0))))
+ )).toDF("id", "keys")
+
+ val mh = new MinHashLSH()
+ .setNumHashTables(5)
+ .setInputCol("keys")
+ .setOutputCol("values")
+
+ val model = mh.fit(dfA)
+ model.approxSimilarityJoin(dfA, dfB, 0.6).show()
+
+ // Cache the transformed columns
--- End diff --
Fixed.
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